{"id":1931,"date":"2019-02-05T01:23:07","date_gmt":"2019-02-05T01:23:07","guid":{"rendered":"https:\/\/dev2.facadeinteractive.com\/rutgers\/?page_id=1931"},"modified":"2023-11-24T20:59:40","modified_gmt":"2023-11-24T20:59:40","slug":"publications","status":"publish","type":"page","link":"https:\/\/brainhealth.rutgers.edu\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"<div class=\"brain-textbook-content\">\n<h3 class=\"brain-title-h3\" style=\"text-align: center;\">Now Available in 4th Edition<\/h3>\n<h4 class=\"brain-subtitle\">Textbook<\/h4>\n<p>Learning and Memory: From Brain to Behavior<\/p>\n<div class=\"brain-textbook__inner\">\n<div class=\"brain-list-slider\">\n<ul style=\"width: 220px; z-index: 2; left: 5px; top: 0px;\">\n<li><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-160727\" src=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/08\/COVER-4e-JPEG-Gluck_Learning-and-Memory-4e.041519-scaled.jpg\" alt=\"\" width=\"2209\" height=\"1180\" \/><\/li>\n<li><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-160726\" src=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/08\/Spanish-Aprendizaje-y-Memoria.jpeg\" alt=\"\" width=\"960\" height=\"1180\" srcset=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/08\/Spanish-Aprendizaje-y-Memoria.jpeg 960w, https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/08\/Spanish-Aprendizaje-y-Memoria-244x300.jpeg 244w, https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/08\/Spanish-Aprendizaje-y-Memoria-833x1024.jpeg 833w, https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/08\/Spanish-Aprendizaje-y-Memoria-768x944.jpeg 768w\" sizes=\"(max-width: 960px) 100vw, 960px\" \/><\/li>\n<li><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-160724\" src=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/08\/German-Lernen-und-Gedachtnis-1.jpeg\" alt=\"\" width=\"960\" height=\"1180\" \/><\/li>\n<li><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-160723\" src=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/08\/Korean-Translation-JPEG_Textbook_1200res-1-scaled-e1660669587388.jpg\" alt=\"\" width=\"1760\" height=\"1180\" \/><\/li>\n<li><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-160769\" src=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/08\/unnamed.png\" alt=\"\" width=\"215\" height=\"261\" \/><\/li>\n<\/ul>\n<\/div>\n<div class=\"brain-text\">\n<p>Recently updated, with a convenient new modular format, Learning and Memory, Second Edition, shows students where the study of learning and memory is and where it is heading. Requiring no prerequisite coursework, this textbook connects learning, memory, and neuroscience. The book compares brain studies and behavioral approaches in human and other animal species, includes extensive clinical perspectives, and is printed in full-color throughout.<\/p>\n<p><a class=\"brain-link-more\" href=\"https:\/\/www.macmillanlearning.com\/college\/us\/product\/Learning-and-Memory\/p\/1319107389\" target=\"_blank\" rel=\"noopener\">More Info<\/a><\/p>\n<div class=\"brain-more-language-book\">\n<p>Click below for more info or to purchase the book in:<\/p>\n<div class=\"brain__languages\"><a href=\"https:\/\/www.macmillanlearning.com\/college\/us\/product\/Learning-and-Memory\/p\/1319107389\" target=\"_blank\" rel=\"noopener\">English<\/a><br \/>\n<a href=\"https:\/\/drive.google.com\/file\/d\/1MmwlAiMmu5NVy4Qr9X9fBBYaoTwGZjk5\/view\" target=\"_blank\" rel=\"noopener\">Spanish<\/a><br \/>\n<a href=\"https:\/\/link.springer.com\/book\/9783827421029\" target=\"_blank\" rel=\"noopener\">German <\/a><br \/>\n<a href=\"http:\/\/www.yes24.com\/Product\/Goods\/70751059\" target=\"_blank\" rel=\"noopener\"> Korean<\/a><br \/>\n<a href=\"https:\/\/www.nobelyayin.com\/ogrenme-ve-bellek-beyinden-davranisa-learning-and-memory-from-brain-to-behavior-17376.html\" target=\"_blank\" rel=\"noopener\">Turkish<\/a><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<p><strong>BOOKS AUTHORED<\/strong><\/p>\n<p>Gluck, M.A., Mercado, E., &amp; Myers, C.E. (2016). <em>Learning and Memory: From Brain to Behavior. 3rd edition.<\/em> New York: Worth<br \/>\nGluck, M.A., Mercado, E., &amp; Myers, C.E. (2014). <em>Learning and Memory: From Brain to Behavior. 2nd edition.<\/em> New York: Worth<br \/>\nGluck, M.A., Mercado, E., &amp; Myers, C.E. (2008). <em>Learning and Memory: From Brain to Behavior. 1st edition<\/em>. New York: Worth<\/p>\n<p>Spanish translation: <em>Aprendizaje y memoria <\/em>(2009). Mexico: McGraw Hill Interamericana Editores, S.A. DE C.V.<\/p>\n<p>Korean translation: \u00a0<a href=\"http:\/\/www.yes24.com\/24\/goods\/4730192\"><strong>\ud559\uc2b5\uacfc<\/strong> <strong>\uae30\uc5b5<\/strong><strong>: <\/strong><strong>\ub1cc\uc5d0\uc11c<\/strong> <strong>\ud589\ub3d9\uae4c\uc9c0<\/strong><\/a>. (2011). Sigma Press, Inc.<br \/>\nGerman translation: <em>Lernen und Ged\u00e4chtnis<\/em> (2010). Heidelberg: Spektrum Akademischer Verlag.<\/p>\n<p>Gluck, M.A. &amp; Myers, C.E. (2001). <em>Gateway to Memory: An Introduction to Neural Network Models of the Hippocampus and Learning. <\/em>Cambridge, MA: MIT Press.<\/p>\n<p><strong>BOOKS &amp; JOURNAL ISSUES: EDITED<\/strong><\/p>\n<p>&nbsp;<\/p>\n<p>Gluck, M. A., Kosslyn, S. M., &amp; Anderson, J. (2008) <em>Memory and Mind: A Festschrift for Gordon H. Bower.<\/em> New York: Taylor &amp; Francis.<\/p>\n<p>Gluck, M. A., Poldrack, R. A., &amp; Keri, S. (2008). <em>The Cognitive Neuroscience of Category Learning <\/em>(Special issue of <em>Neuroscience and Biobehavioral Reviews<\/em>, Guest Editors). 32. 2<\/p>\n<p>Gluck, M. A. &amp; Myers, C. E. (2001).<em> Gateway to Memory: An Introduction to Neural Network Models of the Hippocampus and Learning<\/em>. Cambridge, MA: MIT Press.<\/p>\n<p>Steinmetz, J., Gluck, M., &amp; Solomon, P. (2001).<em> Model Systems and the Neurobiology of Associative Learning: A Festshrift for Richard F. Thompson<\/em>, Mahwah, NJ: Lawrence Erlbaum Associates.<\/p>\n<p>Gluck, M. A., (1996),<em> Hippocampal Computation and Memory<\/em> (Special issue of Hippocampus, Guest Editor). 6(6). J. Wiley &amp; Sons.<\/p>\n<p>Gluck, M. A. &amp; Rumelhart, D. E., (1990). <em>Neuroscience and Connectionist Theory<\/em>, Hillsdale, N.J. Lawrence Erlbaum Associates.<\/p>\n<p>Gluck, M.A., (1990). <em>Neural Networks for Defense Applications<\/em>. San Francisco: Miller-Freeman Publications.<\/p>\n<p>&nbsp;<\/p>\n<p>REFEREED JOURNAL PUBLICATIONS<\/p>\n<p><strong> 2023<\/strong><\/p>\n<p><a href=\"file:\/\/\/C:\/Users\/user\/Downloads\/Gluck2023-fnagi-15-1266423.pdf\">Gluck, M. A., Gills, J. L., Fausto, B. A., Malin, S. K., Duberstein, P. R., Erickson, K. I., &amp; Hu, L. (<i>in press<\/i>). \u00a0Examining the efficacy of a cardio-dance intervention on brain health and the moderating role of ABCA7 in older African Americans: A protocol for a randomized controlled trial.\u00a0<i>Frontiers in Aging Neuroscience, 15.<\/i>doi: 10.3389\/fnagi.2023.1266423<\/a><\/p>\n<ul>\n<li><strong>Background: <\/strong>African Americans are two to three times more likely to be diagnosed with Alzheimer\u2019s disease (AD) compared to White Americans. Exercise is a lifestyle behavior associated with neuroprotection and decreased AD risk, although most African Americans, especially older adults, perform less than the recommended 150 min\/week of moderate-to-vigorous intensity exercise. This article describes the protocol for a Phase III randomized controlled trial that will examine the effects of cardio-dance aerobic exercise on novel AD cognitive and neural markers of hippocampal-dependent function (Aims #1 and #2) and whether exercise-induced neuroprotective benefits may be modulated by an AD genetic risk factor, ABCA7 rs3764650 (Aim #3). We will also explore the effects of exercise on blood-based biomarkers for AD.<\/li>\n<li><strong><b>Methods and analysis:<\/b> <\/strong> This 6-month trial will include 280 African Americans (\u2265 60 years), who will be randomly assigned to 3 days\/week of either: (1) a moderate-to-vigorous cardio-dance fitness condition or (2) a low-intensity strength, flexibility, and balance condition for 60 min\/session. Participants will complete health and behavioral surveys, neuropsychological testing, saliva and venipuncture, aerobic fitness, anthropometrics and resting-state structural and functional neuroimaging at study entry and 6 months.<\/li>\n<li><strong><b>Discussion:<\/b> <\/strong>Results from this investigation will inform future exercise trials and the development of prescribed interventions that aim to reduce the risk of AD in African Americans.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2023\/09\/Osiecka2023-FAN-Obesity.pdf\">Osiecka Z, Fausto BA, Gills JL, Sinha N, Malin SK and Gluck MA (2023) Obesity reduces hippocampal structure and function in older African Americans with the APOE-\u03b54 Alzheimer\u2019s disease risk allele. Frontiers in Aging Neuroscience. 15:1239727. doi: 10.3389\/fnagi.2023.1239727<\/a><\/p>\n<ul>\n<li>Individuals who were overweight demonstrated better hippocampal cognitive function (generalization of learning: a sensitive marker of preclinical AD) than individuals with normal BMI. However, individuals in the obese categories who were APOE-+4 noncarriers had larger hippocampal subfield cornu Ammonis region 1 (CA1) volumes, while those who were APOE-+4 carriers had smaller CA1 volumes. Being overweight by BMI standards may preserve hippocampal function, but obesity reduces hippocampal structure and function in older African Americans with the APOE-+4 Alzheimer\u2019s disease risk allele.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2023\/07\/Sinha-Fausto-Mander-Gluck_ABCA7-x-Sleep_jad230043.pdf\" target=\"_blank\" rel=\"noopener\"><br \/>\nSinha, N., Fausto, B. A., Mander, B., &amp; Gluck, M. A. (2023). High-quality sleep mitigates ABCA7-related generalization deficits in healthy older African Americans. Journal of Alzheimer\u2019s Disease, 94(1), 281-290. doi:10.3233\/JAD-230043<\/a><\/p>\n<ul>\n<li><strong>Background<\/strong>: Both sleep deficiencies and Alzheimer\u2019s disease (AD) disproportionately affect older African Americans. Genetic susceptibility to AD further compounds risk for cognitive decline in this population. Aside from APOE \u20004, ABCA7 rs115550680 is the strongest genetic locus associated with late-onset AD in African Americans. While sleep and ABCA7 rs115550680 independently influence late-life cognitive outcomes, we know too little about the interplay between these two factors on cognitive function.<\/li>\n<li><strong>Objective<\/strong>: We investigated the interaction between sleep and ABCA7 rs115550680 on hippocampal-dependent cognitive function in older African Americans.<\/li>\n<li><strong>Methods<\/strong>: One-hundred fourteen cognitively healthy older African Americans were genotyped for ABCA7 risk (n=57 carriers of risk \u201cG\u201d allele; n = 57 non-carriers), responded to lifestyle questionnaires, and completed a cognitive battery. Sleep was assessed via a self-reported rating of sleep quality (poor, average, good). Covariates included age and years of education.<\/li>\n<li><strong>Results<\/strong>: UsingANCOVA,we found that carriers of the risk genotype who reported poor or average sleep quality demonstrated significantly poorer generalization of prior learning\u2014a cognitive marker of AD\u2014compared to their non-risk counterparts. Conversely, there was no genotype-related difference in generalization performance in individuals who reported good sleep quality.<\/li>\n<li><strong>Conclusion<\/strong>: These results indicate that sleep quality may be neuroprotective against genetic risk for AD. Future studies employing more rigorous methodology should investigate the mechanistic role of sleep neurophysiology in the pathogenesis and progression of AD associated with ABCA7. There is also need for the continued development of non-invasive sleep interventions tailored to racial groups with specific AD genetic risk profiles.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2023\/06\/Altered-learning-and-transfer-abilities-in-Korsakoffs-syndrome-depending-on-task-complexity.pdf\" target=\"_blank\" rel=\"noopener\"><br \/>\nUlasoglu-Yildiz, C., Yildirim, Z., E. Myers, C. E., Gluck, M. A., &amp; Gurvit, H. (2023). Altered learning and transfer abilities in Korsakoff\u2019s syndrome depending on task complexity, Applied Neuropsychology: Adult<\/a><\/p>\n<p><strong> 2022 <\/strong><br \/>\n<a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/09\/Esiaka.Y.F.G-MiniReview2022.pdf\" target=\"_blank\" rel=\"noopener\"><br \/>\nEsiaka, D., Yarborough, C. C., Fausto, B. A., &amp; Gluck, M. A. (2022). A Mini-Review of Strategies for Recruiting Older African Americans to Alzheimer&#8217;s Disease Research. Community Health Equity Research and Policy.<br \/>\n<\/a><\/p>\n<ul>\n<li>African Americans experience higher incidence and prevalence of Alzheimer&#8217;s disease (AD). Yet, they continue to be underrepresented in AD research, limiting the ability to generalize findings to the increasingly diverse US population. To reduce AD disparities, targeted efforts are needed to increase the representation of African Americans in AD research. This mini review identified evidence-based strategies that increased research participation among older African Americans. Four recruitment strategies emerged from eight published peer-reviewed studies that directly evaluated the effectiveness of strategies aimed at increasing the number of African American participants in Alzheimer&#8217;s research. Targeted efforts are effective in increasing AD research participation among older African Americans, especially when combined with approaches that emphasize transparency and mutual trust and involve the community as stakeholders in the research process.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Lerner.Gluck2022.frontiers.SleepTemporal.pdf\" target=\"_blank\" rel=\"noopener\"><br \/>\nLerner, I. &amp; Gluck, M. A. (2022). Sleep facilitates extraction of temporal regularities with varying timescales. Frontiers in Behavioral Neuroscience. 16:847083.<br \/>\n<\/a><\/p>\n<p><strong> 2021 <\/strong><\/p>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Ket-mid-Npsych_JCP-2021.pdf\" target=\"_blank\" rel=\"noopener\">Keilp, J. G., Madden, S. P., Marver, B. A., Frawley, A., Burke, A. K., Herzallah, M. M., Gluck, M. A., Mann, J., Grunebaum, M. F. (2021). Effects of Ketamine Versus Midazolam on Neurocognition at 24 Hours in Depressed Patients With Suicidal Ideation. Journal of Clinical Psychiatry. 82(6):21<\/a><\/p>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Lerner2021.NLM_.pdf\" target=\"_blank\" rel=\"noopener\">Lerner, I., Lupkin, S. M., Tsai, A., Khawaja, A., &amp; Gluck, M. A. (2021). Sleep to Remember, Sleep to Forget: Rapid Eye Movement Sleep Can Have Inverse Effects on Recall and Generalization of Fear Memories. Neurobiology of Learning and Memory, Volume 180, 2021, 107413.<\/a><\/p>\n<ul>\n<li>Rapid Eye Movement (REM) sleep has been shown to modulate the consolidation of fear memories, a process that may contribute to the development of Post-Traumatic Stress Disorder (PTSD). However, contradictory findings have been reported regarding the direction of this modulation and its differential effects on recall versus generalization. In two complementary experiments, we addressed this by employing sleep deprivation protocols together with a novel fear-conditioning paradigm that required the discrimination between coexisting threat and safety signals. Using skin conductance responses and functional imaging (fMRI), we found two opposing effects of REM sleep: While REM impaired recall of the original threat memories, it improved the ability to generalize these memories to novel situations that emphasized the discrimination between threat and safety signals. These results, as well as previous findings in healthy participants and patients diagnosed with PTSD, could be explained by the degree to which the balance between threat and safety signals for a given stimulus was predictive of threat. We suggest that this account can be integrated with contemporary theories of sleep and fear learning, such as the REM recalibration hypothesis.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Sinha_NLM_2020.pdf\" target=\"_blank\" rel=\"noopener\">Sinha, N., Berg, C. N., Yassa, M. A., &amp; Gluck, M. A. (2021). Increased Dynamic Flexibility in Medial Temporal Lobe Network Following Exercise Intervention Mediates Generalization of Prior Learning. Neurobiology of Learning and Memory. 177. 107340.<\/a><\/p>\n<ul>\n<li>Recent work has conceptualized the brain as a network comprised of groups of sub-networks or modules. \u201cFlexibility\u201d of brain network(s) indexes the dynamic reconfiguration of comprising modules. Using novel techniques from dynamic network neuroscience applied to high-resolution resting-state functional magnetic resonance imaging (fMRI), the present study investigated the effects of an aerobic exercise intervention on the dynamic rearrangement of modular community structure\u2014a measure of neural flexibility\u2014within the medial temporal lobe (MTL) network. The MTL is one of the earliest brain regions impacted by Alzheimer\u2019s disease. It is also a major site of neuroplasticity that is sensitive to the effects of exercise. In a two-group non-randomized, repeated measures and matched control design with 34 healthy older adults, we observed an exercise-related increase in flexibility within the MTL network. Furthermore, MTL network flexibility mediated the beneficial effect aerobic exercise had on mnemonic flexibility, as measured by the ability to generalize past learning to novel task demands. Our results suggest that exercise exerts a rehabilitative and protective effect on MTL function, resulting in dynamically evolving networks of regions that interact in complex communication patterns. These reconfigurations may underlie exercise-induced improvements on cognitive measures of generalization, which are sensitive to subtle changes in the MTL.<\/li>\n<\/ul>\n<p><strong> 2020 <\/strong><\/p>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Sinha-Berg-Shaw-Gluck_ABCA7-Exercise.pdf\" target=\"_blank\" rel=\"noopener\">Sinha, N., Berg, C. N., Shaw, A., &amp; Gluck, M. A. (2020). ABCA7 Genotype Moderates the Effect of Aerobic Exercise Intervention on Generalization of Prior Learning in Healthy Older African Americans. Journal of Alzheimer&#8217;s disease: JAD, 74(1), 309.<\/a><\/p>\n<ul>\n<li>African Americans are at elevated risk for age-related cognitive decline, with double the prevalence of Alzheimer\u2019s disease (AD) compared to Caucasians Americans. Various behavioral, biological, and lifestyle factors may underlie this health disparity, but little is known about the relative importance and interactions among these different risk factors in African Americans. While the neuroprotective effects of aerobic exercise on biomarkers are well established, few studies have examined the differential benefits of exercise based on genetic risk for AD. Furthermore, evidence is limited regarding the potential moderating effects of ABCA7, a gene known to confer significantly greater AD risk in African Americans. In a case-control matched sample of 56 healthy older African Americans, we investigated the effect of an aerobic exercise intervention on a hippocampus-related assessment of generalization following rule learning, in individuals who were carriers of the ABCA7 rs3764650 non-risk (TT) or high-risk (GG) genotypes. Following the exercise-intervention, the non-risk group made significantly fewer generalization errors, while there was no improvement in generalization for the high-risk group. For the controls, no changes in generalization scores were observed regardless of genotype status. Our results indicate that the ongoing adverse effects of ABCA7 high-risk genotype may diminish the benefits associated with aerobic exercise. As such, the potential disease-modifying effects of aerobic exercise on AD-related neuropathology may be limited to carriers of the ABCA7 rs3764650 non-risk genotype.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Fausto_Gluck2022-EthnHealth.pdf\" target=\"_blank\" rel=\"noopener\">Fausto, B. A., &amp; Gluck, M. A. (2020). Low body mass and high-quality sleep maximize the ability of aerobic fitness to promote improved cognitive function in older African Americans.\u00a0<i>Ethnicity and Health. S<\/i>ep 15;1-20. doi: 10.1080\/13557858.2020.1821176<\/a><\/p>\n<p><strong> 2019 <\/strong><\/p>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/2019-Learning-functions-in-short-term.pdf\" target=\"_blank\" rel=\"noopener\">Kumar, D. S., Benedict, E., Wu, O., Rubin, E., Gluck, M. A., Foltin, R. W., Myers, C. E., &amp; Vadhan, N. P. (2019). Learning functions in short-term cocaine users. Addictive Behaviors Reports. V9.<\/a><\/p>\n<ul>\n<li>This study examined learning functions in short-term cocaine users and control participants. Seventeen active cocaine users and seventeen non-cocaine-using controls (with similar reported levels of alcohol and marijuana use) were compared on tasks measuring different aspects of learning. The cocaine users performed more poorly on the Weather Prediction and List-Learning tasks, as well as supplementary executive and psychomotor function tasks, than controls. Conclusions: Individuals with a relatively short duration of cocaine use exhibited moderate weaknesses in probabilistic category learning, verbal learning and psychomotor functions, relative to controls. These weaknesses may underpin difficulty in learning from the probabilistic consequences of behavior and hinder the ability to respond to cognitive-behavioral treatments.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/APOEABCA7.Frontiers.pdf\" target=\"_blank\" rel=\"noopener\">Berg, C. N., Sinha, N., &amp; Gluck, M. A. (2019). The effects of APOE and ABCA7 on cognitive function and Alzheimer\u2019s disease risk in African Americans: A focussed mini review. Frontiers in Human Neuroscience. 13. 387<\/a> <a href=\"https:\/\/doi.org\/10.3389\/fnhum.2019.00387\" target=\"_blank\" rel=\"noopener\">https:\/\/doi.org\/10.3389\/fnhum.2019.00387<\/a><\/p>\n<ul>\n<li>African Americans have double the prevalence of Alzheimer\u2019s disease (AD), as compared to European Americans. However, the underlying causes of this health disparity are due to a multitude of environmental, lifestyle, and genetic factors that are not yet fully understood. Here, we review the effects of the two largest genetic risk factors for AD in African Americans: Apolipoprotein E (APOE) and ABCA7. We will describe the direct effects of genetic variation on neural correlates of cognitive function and report the indirect modulating effects of genetic variation on modifiable AD risk factors, such as aerobic fitness. As a means of integrating previous findings, we present a novel schematic diagram to illustrate the many factors that contribute to AD risk and impaired cognitive function in older African Americans. Finally, we discuss areas that require further inquiry, and stress the importance of racially diverse and representative study populations.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/LernerGluck2019.pdf\" target=\"_blank\" rel=\"noopener\">Lerner, I., Gluck, M.A. (2019). Sleep and the Extraction of Hidden Regularities: A Systematic Review and the Importance of Temporal Rules. Sleep Medicine Reviews, 47, 39-50.<\/a> <a href=\"https:\/\/doi.org\/10.1016\/j.smrv.2019.05.00\" target=\"_blank\" rel=\"noopener\">https:\/\/doi.org\/10.1016\/j.smrv.2019.05.00<\/a><\/p>\n<ul>\n<li>As part of its role in memory consolidation, sleep has been repeatedly identified as critical for the extraction of regularities from wake experiences. However, many null results have been published as well, with no clear consensus emerging regarding the conditions that yield this sleep effect. Here, we systematically review the role of sleep in the extraction of hidden regularities, specifically those involving associative relations embedded in newly learned information. We found that the specific behavioral task used in a study had far more impact on whether a sleep effect was discovered than either the category of the cognitive processes targeted, or the particular experimental design employed. One emerging pattern, however, was that the explicit detection of hidden rules is more likely to happen when the rules are of a temporal nature (i.e., event A at time t predicts a later event B) than when they are non-temporal. We discuss this temporal rule sensitivity in reference to the compressed memory replay occurring in the hippocampus during slow-wave-sleep, and compare this effect to what happens when the extraction of regularities depends on prior knowledge and relies on structures other than the hippocampus.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Berg-Sinha-Gluck.ABCA7Fitnessfnagi2019.pdf\" target=\"_blank\" rel=\"noopener\">Berg, C. N., Sinha, N., &amp; Gluck, M. A. (2019). ABCA7 Risk Genotype Diminishes the Neuroprotective Value of Aerobic Fitness in Healthy Older African Americans. Frontiers in Aging Neuroscience. 11:73.<\/a><\/p>\n<ul>\n<li>Although the association of ABCA7 risk variants with Alzheimer\u2019s disease (AD) has been established worldwide, its effect size on the relative odds of being diagnosed with AD is significantly higher in African Americans. Across ethnicities, two common ABCA7 loci (rs115550680 and rs3764650) have been confirmed to increase the risk of AD. While ABCA7 rs115550680 has been linked to the development of late-onset AD in African Americans, no association between ABCA7 variant rs3764650 and AD has been found in this population. In order to elucidate the influence of ABCA7 rs3764650 on AD risk in African Americans, we sought to investigate the relationship between this variant, aerobic fitness, and cognition. The present study tested the hypothesis that in African Americans, ABCA7 rs3764650 confers an indirect risk for AD via its interaction with aerobic fitness, a modifiable lifestyle factor known to attenuate AD related neuropathology. In a case-control sample of 100 healthy African Americans, we observed that ABCA7 rs3764650 genotype modulates the association between aerobic fitness and a cognitive assessment of generalization following rule learning. For carriers of the non-risk genotype, higher levels of aerobic fitness were significantly associated with fewer generalization errors, while carriers of the risk genotype did not show any relationship between aerobic fitness and generalization. Our findings imply that ABCA7 rs3764650 risk genotype may diminish the neuroprotective effects of aerobic fitness, and, they suggest differing risk patterns between cognitive decline and fitness by ABCA7 genotype. Thus, in African Americans the interactive effects of ABCA7 rs3764650 and aerobic fitness likely compound overall ABCA7-related AD risk, and may contribute to health disparities whereby African Americans are at a higher risk for dementia, with double the prevalence of AD.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Lerner_et_al-2019-Scientific_Reports.pdf\" target=\"_blank\" rel=\"noopener\">Lerner, I., Ketz, N. A., Jones, A. P., Bryant, B. B., Robert, B., Skorheim, S. W., Hartholt, A., Rizzo, A. S., Gluck, M. A., Clark, V. P., Pilly, P. K. (2019). Transcranial Current Stimulation During Sleep Facilitates Insight into Temporal Rules, but does not Consolidate Memories of Individual Sequential Experiences. Scientific Reports, 9, 1516.<\/a><\/p>\n<ul>\n<li>Slow-wave sleep (SWS) is known to contribute to memory consolidation, likely through the reactivation of previously encoded waking experiences. Contemporary studies demonstrate that when auditory or olfactory stimulation is administered during memory encoding and then reapplied during SWS, memory consolidation can be enhanced, an effect that is believed to rely on targeted memory reactivation (TMR) induced by the sensory stimulation. Here, we show that transcranial current stimulations (tCS) during sleep can also be used to induce TMR, resulting in the facilitation of high-level cognitive processes. Participants were exposed to repeating sequences in a realistic 3D immersive environment while being stimulated with particular tCS patterns. A subset of these tCS patterns was then reapplied during sleep stages N2 and SWS coupled to slow oscillations in a closed-loop manner. We found that in contrast to our initial hypothesis, performance for the sequences corresponding to the reapplied tCS patterns was no better than for other sequences that received stimulations only during wake or not at all. In contrast, we found that the more stimulations participants received overnight, the more likely they were to detect temporal regularities governing the learned sequences the following morning, with tCS-induced beta power modulations during sleep mediating this effect.<\/li>\n<\/ul>\n<p><strong> 2018 <\/strong><\/p>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Sinha_et_al-2018-Hippocampus.pdf\" target=\"_blank\" rel=\"noopener\">Sinha, N., Reagh, Z. M., Tustison, N. J., Berg, C. N., Shaw, A., Myers, C. E., Hill, D., Yassa, M. A., &amp; Gluck, M. A. (2018). ABCA7 risk variant in healthy older African Americans is associated with a functionally isolated entorhinal cortex mediating deficient generalization of prior discrimination training. Hippocampus. DOI: 10.1002\/hipo.23042<\/a><\/p>\n<ul>\n<li>Using high-resolution resting state functional magnetic resonance imaging (fMRI), the present study tested the hypothesis that ABCA7 genetic risk differentially affects intra-medial temporal lobe (MTL) functional connectivity between MTL subfields, versus internetwork connectivity of the MTL with the medial prefrontal cortex (mPFC), in nondemented older African Americans. Although the association of ABCA7 risk variants with Alzheimer&#8217;s disease (AD) has been confirmed worldwide, its effect size on the relative odds of being diagnosed with AD is significantly higher in African Americans. However, little is known about the neural correlates of cognitive function in older African Americans and how they relate to AD risk conferred by ABCA7. In a case\u2013control fMRI study of 36 healthy African Americans, we observed ABCA7 related impairments in behavioral generalization that was mediated by dissociation in entorhinal cortex (EC) resting state functional connectivity. Specifically, ABCA7 risk variant was associated with EChippocampus hyper-synchronization and EC-mPFC hypo-synchronization. Carriers of the risk genotype also had a significantly smaller anterolateral EC, despite our finding no group differences on standardized neuropsychological tests. Our findings suggest a model where impaired cortical connectivity leads to a more functionally isolated EC at rest, which translates into aberrant EC-hippocampus hyper-synchronization resulting in generalization deficits. While we cannot identify the exact mechanism underlying the observed alterations in EC structure and network function, considering the relevance of A\u03b2 in ABCA7 related AD pathogenesis, the results of our study may reflect the synergistic reinforcement between amyloid and tau pathology in the EC, which significantly increases tau-induced neuronal loss and accelerates synaptic alterations. Finally, our results add to a growing literature suggesting that generalization of learning may be a useful tool for assessing the mild cognitive deficits seen in the earliest phases of prodromal AD, even before the more commonly reported deficits in episodic memory arise.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Lerner_etal_2018_AGING.pdf\" target=\"_blank\" rel=\"noopener\">Lerner I, Sojitra R, Gluck M. How age affects reinforcement learning. Aging (Albany NY). 2018 Nov 12. https:\/\/doi.org\/10.18632\/aging.101649 [Epub ahead of print]<\/a><\/p>\n<ul>\n<li>We administered a novel virtual navigation task in which participants were required to walk through a winding corridor and then choose one of five marked doors to exit. Unknown to participants, the markings on the correct door reflected the corridor\u2019s shape (from a bird\u2019s eye view). Detecting this regularity negates the need to find the exit by trial and error. Participants performed the task twice a day for a week, while their overnight sleep was monitored. We found that the more time participants spent in SWS across the week, the better they were able to implicitly extract the hidden regularity. In contrast, the few participants that explicitly realized the regularity did not rely on SWS to do so. Moreover, the SWS effect was strictly at the trait-level: Baseline levels of SWS prior to the experimental week could predict success just as well, but day-to-day variations in SWS did not predict day-to-day improvements. We propose that our findings indicate SWS facilitates implicit integration of new information into cognitive maps, possibly through compressed memory replay.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/LernerGluck_Frontiers2018.pdf\" target=\"_blank\" rel=\"noopener\">Lerner, I., &amp; Gluck, M. A. (2018). Individual Differences in Slow-Wave-Sleep Predict Acquisition of Full Cognitive Maps. Frontiers in Human Neuroscience, 12, 404.<\/a><\/p>\n<ul>\n<li>We administered a novel virtual navigation task in which participants were required to walk through a winding corridor and then choose one of five marked doors to exit. Unknown to participants, the markings on the correct door reflected the corridor\u2019s shape (from a bird\u2019s eye view). Detecting this regularity negates the need to find the exit by trial and error. Participants performed the task twice a day for a week, while their overnight sleep was monitored. We found that the more time participants spent in SWS across the week, the better they were able to implicitly extract the hidden regularity. In contrast, the few participants that explicitly realized the regularity did not rely on SWS to do so. Moreover, the SWS effect was strictly at the trait-level: Baseline levels of SWS prior to the experimental week could predict success just as well, but day-to-day variations in SWS did not predict day-to-day improvements. We propose that our findings indicate SWS facilitates implicit integration of new information into cognitive maps, possibly through compressed memory replay.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Gluck-Shaw-Hill2018GenerationsJASA.pdf\" target=\"_blank\" rel=\"noopener\">Gluck, M. A., Shaw, A., &amp; Hill, D. (2018). Recruiting older African Americans to brain health and aging research through community engagement: Lessons from the African-American Brain Health Initiative at Rutgers University-Newark. Generations: Journal of the American Society of Aging. 42(2). 78-82.<\/a><\/p>\n<ul>\n<li>The African-American Brain Health Initiative at Rutgers University\u0096Newark is a university\u0096 community partnership combining community engagement, education and training, and brain health research. Partnering with community-based organizations, it promotes brain health literacy, Alzheimer\u0092s awareness, brain-healthy lifestyle choices, and participation in brain research for older African Americans in Greater Newark, New Jersey. Our approach to recruitment relies on building trust through long-term relationships; communicating health knowledge through trusted community leaders; recruiting subjects through targeted efforts; and cultivating research participants as ambassadors.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Sinha-Gluck.2018.APOE4_.NoAging.pdf\" target=\"_blank\" rel=\"noopener\">Sinha, N., Berg, C. N., Tustison, N. J. Shaw, A., Hill, D., Yassa, M. A., &amp; Gluck. M. A. (2018). APOE e4 status in healthy older African Americans is associated with deficits in pattern separation and hippocampal hyperactivation. Neurobiology of Aging, Vol. 69, 221-229.<\/a><\/p>\n<ul>\n<li>African Americans are 1.4 times more likely than European Americans to carry the apolipoprotein E (APOE) \u03b54 allele, a risk factor for Alzheimer&#8217;s disease (AD). However, little is known about the neural correlates of cognitive function in older African Americans and how they relate to genetic risk for AD. In particular, no past study on African Americans has examined the effect of APOE \u03b54 status on pattern separation\u2014mnemonic discrimination performance and its corresponding neural computations in the hippocampus. Previous work using the mnemonic discrimination paradigm has localized increased activation in the DG\/CA3 hippocampal subregions as being correlated with discrimination deficits. In a case-control high-resolution functional magnetic resonance imaging study of 30 healthy African Americans, aged 60 years and older, we observed APOE \u03b54\u2013related impairments in mnemonic discrimination, coincident with dysfunctional hyperactivation in the DG\/CA3, and CA1 regions, despite no evidence of structural differences in the hippocampus between carriers and noncarriers. Our results add to the growing body of evidence that deficits in pattern separation may be an early marker for AD-related neuronal dysfunction.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Sojitra-Lerner-Petok-Gluck2018NoA.pdf\" target=\"_blank\" rel=\"noopener\">Sojitra, R. A., Lerner, I., Petok, &amp; J. R., &amp; Gluck, M. A. (2018). Age affects reinforcement learning through dopamine-based learning imbalance and high decision noise\u0097not through Parkinsonian mechanisms. Neurobiology of Aging. 68. 102-113. <\/a><a href=\"https:\/\/doi.org\/10.1016\/j.neurobiolaging.2018.04.006\" target=\"_blank\" rel=\"noopener\">https:\/\/doi.org\/10.1016\/j.neurobiolaging.2018.04.006<\/a>.<\/p>\n<ul>\n<li>Probabilistic reinforcement learning declines in healthy cognitive aging. While some findings suggest impairments are especially conspicuous in learning from rewards, resembling deficits in Parkinson\u2019s disease, others also show impairments in learning from punishments. To reconcile these findings, we tested 252 adults from 3 age groups on a probabilistic reinforcement learning task, analyzed trial-by-trial performance with a Q-reinforcement learning model, and correlated both fitted model parameters and behavior to polymorphisms in dopamine-related genes. Analyses revealed that learning from both positive and negative feedback declines with age but through different mechanisms: when learning from negative feedback, older adults were slower due to noisy decision-making; when learning from positive feedback, they tended to settle for a nonoptimal solution due to an imbalance in learning from positive and negative prediction errors. The imbalance was associated with polymorphisms in the DARPP-32 gene and appeared to arise from mechanisms different from those previously attributed to Parkinson\u2019s disease. Moreover, this imbalance predicted previous findings on aging using the Probabilistic Selection Task, which were misattributed to Parkinsonian mechanisms.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Argyelan-et-al.-2018-NeuroReport-Dopamine-modulates-striatal-response-to-reward-and-punishment-in-patients-with-Parkinsons-disease-a-pharmacological-challenge-fMRI-study.pdf\" target=\"_blank\" rel=\"noopener\"> Argyelan, M., Herzallah, M., Sako, W., DeLucia, I., Sarpal, D., Vo, A., Fitzpatrick, T., Moustafa, A. A., Eidelberg, D., &amp; Gluck, M. (2018). Dopamine modulates striatal response to reward and punishment in patients with Parkinson&#8217;s disease: a pharmacological challenge fMRI study. <em>Neuroreport<\/em>. <\/a><\/p>\n<ul>\n<li>It is well established that Parkinson&#8217;s disease leads to impaired learning from reward and enhanced learning from punishment. The administration of dopaminergic medications reverses this learning pattern. However, few studies have investigated the neural underpinnings of these cognitive processes. In this study, using fMRI, we tested a group of Parkinson&#8217;s disease patients on and off dopaminergic medications and matched healthy individuals. All individuals completed\u00a0an fMRI\u00a0cognitive task that dissociates feedback learning from reward versus punishment. The administration of dopaminergic medications attenuated blood oxygen level dependent (BOLD) responses to punishment in the bilateral putamen, in bilateral dorsolateral prefrontal cortex and the left premotor cortex. Further, the administration of dopaminergic medications resulted in a higher ratio of BOLD activity between reward and punishment trials in these brain areas. BOLD activity in these brain areas was significantly correlated with learning from punishment, but not from reward trials. Furthermore, the administration of dopaminergic medications altered BOLD activity in the right insula and ventromedial prefrontal cortex when Parkinson&#8217;s disease patients were anticipating feedback. These findings are in agreement with a large body of literature indicating that Parkinson&#8217;s disease is associated with enhanced learning from punishment. However, it was surprising that dopaminergic medications modulated punishment learning as opposed to\u00a0reward\u00a0learning, although reward learning has been directly linked to dopaminergic function. We argue that these results might be attributed to both a change in the balance between direct and indirect pathway activation in the basal ganglia as well as the differential activity of D1 versus D2 dopamine receptors.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Petok-GluckRingman2018-FAD-Generalization-2.pdf\" target=\"_blank\" rel=\"noopener\">Petok, J. R., Myers, C. E., Pa, J., Hobel, Z., Wharton, D. M., Medina, L. D., Casado, M., Coppolla, G., Gluck, M. A., &amp; Ringman, J. M. (2018). Impairment of memory generalization in preclinical autosomal dominant Alzheimer\u0092s disease mutation carriers. Neurobiology of Aging. 65. 149-157.<\/a><\/p>\n<ul>\n<li>Fast, inexpensive, and noninvasive identification of Alzheimer&#8217;s disease(AD) before clinical symptoms emerge would augment our ability to intervene early in the disease. Individuals with fully penetrant genetic mutations causing autosomal dominant Alzheimer&#8217;s disease (ADAD) are essentially certain to develop the disease, providing a unique opportunity to examine biomarkers during the preclinical stage. Using a generalization task that has previously shown to be sensitive to medial temporal lobe pathology, we compared preclinical individuals carrying ADAD mutations to noncarrying kin to determine whether generalization (the ability to transfer previous learning to novel but familiar recombinations) is vulnerable early, before overt cognitive decline. As predicted, results revealed that preclinical ADAD mutation carriers made significantly more errors during generalization than noncarrying kin, despite no differences between groups during learning or retention. This impairment correlated with the left hippocampal volume, particularly in mutation carriers. Such identification of generalization deficits in early ADAD may provide an easily implementable and potentially linguistically and culturally neutral way to identify and track cognition in ADAD.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Schuck-Gluck.AgingKibra.NeurobioAging_28Sept2017_29.pdf\" target=\"_blank\" rel=\"noopener\">Schuck, N.W., Petok, J.R., Meeter, M., Schjeide, B.M., Schr\u00f6der, J., Bertram, L., Gluck, M. A., Li, S. (2018). Aging and a genetic KIBRA polymorphism interactively affect feedback-and observation-based probabilistic classification learning. Neurobiology of Aging. 61. 36-43.<\/a><\/p>\n<ul>\n<li>We tested whether age-related behavioral differences in probabilistic category learning from feedback or observation depend on a genetic factor known to influence individual differences in hippocampal function, the KIBRA gene (single nucleotide polymorphism rs17070145). Results showed comparable age-related performance impairments in observational as well as feedback-based learning. Moreover, genetic analyses indicated an age-related interactive effect of KIBRA on learning: among older adults, the beneficial T-allele was positively associated with learning from feedback, but negatively with learning from observation. In younger adults, no effects of KIBRA were found. Our results add behavioral genetic evidence to emerging data showing age-related differences in how neural resources relate to memory functions, namely that hippocampal and striatal contributions to probabilistic category learning may vary with age.<\/li>\n<\/ul>\n<p><strong> 2017 <\/strong><\/p>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/LernerLupkin_etal_Jneuro2017_FINAL.pdf\" target=\"_blank\" rel=\"noopener\">Lerner, I., Lupkin, S., Sinha, N., Tsai, A., &amp; Gluck, M.A. (2017<em>).<\/em>\u00a0Baseline Levels of Rapid-eye-Movement Sleep May Protect Against Excessive Activity in Fear-Related Neural Circuits.\u00a0<em>Journal of Neuroscience<\/em><\/a><\/p>\n<ul>\n<li>In the current study, we used long-term mobile sleep monitoring and functional neuroimaging (fMRI) to explore whether trait-like variations in sleep patterns, measured in advance in both male and female participants, predict subsequent patterns of neural activity during fear learning. Our results indicate that higher baseline levels of REM sleep predict reduced fear-related activity in, and connectivity between, the hippocampus, amygdala and ventromedial PFC during conditioning. Additionally, skin conductance responses (SCRs) were weakly correlated to the activity in the amygdala. Conversely, there was no direct correlation between REM sleep and SCRs, indicating that REM may only modulate fear acquisition indirectly. In a follow-up experiment, we show that these results are replicable, though to a lesser extent, when measuring sleep over a single night just before conditioning. As such, baseline sleep parameters may be able to serve as biomarkers for resilience, or lack thereof, to trauma.<\/li>\n<\/ul>\n<p>Motor-symptom Laterality Affects Acquisition in Parkinson\u0092s disease: a Cognitive and fMRI study <a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Huang-Gluck-Chen2017.MotorSymptomPD-MDS-16-0758.R2.pdf\" target=\"_blank\" rel=\"noopener\">Pei Huang, M.D.1*, Yu-Yan Tan, M.D., Ph.D.1*, Dong-Qiang Liu, Ph.D.25 , Mohammad M. Herzallah, M.D., Ph.D.3, 4, Elizabeth Lapidow, B.A.3, Ying Wang M.D.16 , Yu-Feng Zang, M.D.5, Mark Gluck, Ph.D.3\u00a7, Sheng-Di Chen, M.D., Ph.D.7 1\u00a7<\/a><\/p>\n<ul>\n<li>Objective: Asymmetric onset of motor symptoms in PD can affect cognitive function. We examined whether motor-symptom laterality could affect feedback-based associative learning and explored its underlying neural mechanism by fMRI in PD patients.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/HerzallahJune2017.FrontiersPsychiatry.PD-Depression.pdf\" target=\"_blank\" rel=\"noopener\">Herzallah, M.M., Khdour, H.Y., Taha, A.B., Elmashala, A.M., Mousa, H.N., Taha,M.B., Ghanim, Z., Sehwail, M.M., Misk, A.J., Balsdon, T., Moustafa, A.A., Myers, C.E., Gluck, M.A. (2017) Depression Reduces Accuracy While Parkinsonism Slows Response Time for Processing Positive Feedback in Patients with Parkinson\u0092s Disease with Comorbid Major Depressive Disorder Tested on a Probabilistic Category-Learning Task. Frontiers in Psychiatry. 8(84). https:\/\/doi.org\/10.3389\/fpsyt.2017.00084<\/a><\/p>\n<ul>\n<li>Using a computerbased cognitive task that dissociates learning from positive and negative feedback, we tested four groups of subjects: (1) patients with PD who have MDD, (2) patients with PD without MDD, (3) matched patients with MDD alone (without PD), and (4) matched healthy control subjects. Furthermore, we used a mathematical model of decision making to fit both choice and response time data, allowing us to detect and characterize differences between the groups that are not revealed by cognitive results. Our results suggest that PD patients with MDD exhibit cognitive profiles with mixed traits characteristic of both MDD and PD, furthering our understanding of both PD and MDD and their often-complex comorbidity. To the best of our knowledge, this is the first study to examine feedback-based learning in PD with MDD while controlling for the effects of PD and MDD.<\/li>\n<\/ul>\n<p><strong> 2016 <\/strong><\/p>\n<p>Montgomery K.S., Edwards G. 3rd, Levites Y., Kumar A., Myers C.E., Gluck M.A., Setlow B., Bizon J.L. (2015). <a href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/26418152\" target=\"_blank\" rel=\"noopener\">Deficits in hippocampal-dependent transfer generalization learning accompany synaptic dysfunction in a mouse model of amyloidosis.<\/a> <em>Frontiers Hippocampus<\/em>. 2015 Sep 29. Doi: 10.1002\/hippo.22535.<\/p>\n<ul>\n<li>Elevated \u00df-amyloid and impaired synaptic function in hippocampus are among the earliest manifestations of Alzheimer&#8217;s disease (AD). The current studies employed a mouse analog of an associative &#8220;transfer learning&#8221; task that has previously been used to identify risk for prodromal AD in humans. The rodent version of the task assesses the transfer of learning about stimulus features relevant to a food reward across a series of compound discrimination problems. The relevant feature that predicts the food reward is unchanged across problems, but an irrelevant feature (i.e., the context) is altered. Across three experiments, our data show that the ability to generalize learned associations to new contexts is disrupted even in the presence of subtle hippocampal dysfunction and suggest that, across species, this aspect of hippocampal-dependent learning may be useful for early identification of AD-like pathology.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Lerner_etal_2016_FINAL.pdf\" target=\"_blank\" rel=\"noopener\">Lerner, I., Lupkin, S., Peters, S., Corter, J., Peters, S., Cannella, L., &amp; Gluck, M.A. (2016). The influence of sleep on emotional and cognitive processing is primarily trait- (but not state-) dependent. <em>Neurobiology of Learning and Memory. 134, 275-286.<\/em><\/a><\/p>\n<ul>\n<li>Human studies of sleep and cognition have established that different sleep stages contribute to distinct aspects of cognitive and emotional processing. However, since the majority of these findings are based on single-night studies, it is difficult to determine whether such effects arise due to individual, between-subject differences in sleep patterns, or from within-subject variations in sleep over time. In the current study, we investigated the longitudinal relationship between sleep patterns and cognitive performance by monitoring both in parallel, daily, for a week. Using two cognitive tasks \u0096 one assessing emotional reactivity to facial expressions and the other evaluating learning abilities in a probabilistic categorization task \u0096 we found that between-subject differences in the average time spent in particular sleep stages predicted performance in these tasks far more than within-subject daily variations. Specifically, the typical time individuals spent in Rapid-Eye Movement (REM) sleep and Slow-Wave Sleep (SWS) was correlated to their characteristic measures of emotional reactivity, whereas the typical time spent in SWS and non-REM stages 1 and 2 was correlated to their success in category learning. These effects were maintained even when sleep properties were based on baseline measures taken prior to the experimental week. In contrast, within-subject daily variations in sleep patterns only contributed to overnight difference in one particular measure of emotional reactivity. Thus, we conclude that the effects of natural sleep on emotional cognition and category learning are more trait-dependent than statedependent, and suggest ways to reconcile these results with previous findings in the literature. \u0001 2016 Elsevier Inc. All rights reserved.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/oconnell.Myers_.Gluck2016_pub.pdf\" target=\"_blank\" rel=\"noopener\">O\u2019Connell, G., Myers, C. E., Hopkins, R. O., McLaren, R. P., Gluck, M. A. &amp; Wills, A. J. (2016). Amnesic patients show superior generalization in category learning. Neuropsychology, 30(8):915-919.<\/a>.<\/p>\n<ul>\n<li>Questions remain about the precise role of the hippocampus in this facet of learning, but a connectionist model by Gluck and Myers (1993) predicts that generalization should be enhanced following hippocampal damage. In a two-category learning task, a group of amnesic patients (n=9) learned the training items to a similar level of accuracy as matched controls (n=9). Both groups then classified new items at various levels of distortion. The amnesic group showed significantly more accurate generalization to high distortion novel items, a difference also present when compared to a larger group of unmatched controls (n=33). The model prediction of a broadening of generalization gradients in amnesia, at least for items near category boundaries, was supported by the results. Our study shows for the first time that amnesia can sometimes improve generalization.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Khdour-GluckFrontiersIN.GAD_.2016.pdf\" target=\"_blank\" rel=\"noopener\">Khdour, H.Y., Imam, A.F., Mughrabi, I.T., Myers, C.E., Gluck, M.A., Herzallah, M.M., &amp; Moustafa, A.A. (2016\/in press). Generalized anxiety disorder and social anxiety disorder, but not panic anxiety disorder, are associated with higher sensitivity to learning from negative feedback: behavioral and computational investigation. <em>Frontiers in Behavioral Neuroscience. <\/em><\/a><\/p>\n<ul>\n<li>Anxiety spectrum disorders, including generalized anxiety disorder (GAD), social anxiety disorder (SAD) and panic anxiety disorder (PAD), are a group of common psychiatric conditions. In this study, we tested the cognitive correlates of medication-free patients with GAD, SAD, and PAD, along with matched healthy participants, using a probabilistic category-learning task that allows the dissociation between positive and negative feedback learning. We also fitted all participants\u0092 data to an actor-critic model that examines learning rate parameters from positive and negative feedback to investigate effects of valence vs. action on performance. SAD and GAD patients were more sensitive to negative feedback than either PAD patients or healthy participants. PAD, SAD and GAD patients did not differ in positive-feedback learning compared to healthy participants. Computational analysis revealed that participants\u0092 behavioral results are better explained by the critic\u0092s learning from negative feedback variable. These findings argue that (a) not all anxiety spectrum disorders share the same cognitive correlates, but are rather different in ways that do not link them to the hallmark of anxiety (higher sensitivity to negative feedback); and (b) perception of negative consequences is the core feature of GAD and SAD, but not PAD.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Singh2016-Effect-of-the-Putative-Lithium-Mimetic-Ebselen-on-Brain-Myo-Inositol-Sleep-and-Emotional-Processing-in-Humans-Supplementary-Information.pdf\" target=\"_blank\" rel=\"noopener\">Singh N., Sharpley A. L., Emir U. E., Masaki C., Herzallah M. M., Gluck M. A., Sharp T., Harmer C. J., Vasudevan S. R., Cowen P., Churchill G. C. (2016). Effect of the putative lithium mimetic ebselen on brain myo-inositol, sleep and emotional processing in humans. <em>Neuropsychopharmacology<\/em>. June; 41(7). 1768-78. <\/a><\/p>\n<ul>\n<li>Lithium remains the gold standard in treating bipolar disorder but has unwanted toxicity and side effects. We previously reported that ebselen inhibits inositol monophosphatase (IMPase) and exhibits lithium-like effects in animal models through lowering of inositol. Ebselen has been tested in clinical trials for other disorders, enabling us to determine for the first time the effect of a blood-brain barrier penetrant IMPase inhibitor on human central nervous system (CNS) function. We now report that in a double-blind, placebo-controlled trial with healthy participants, acute oral ebselen reduced brain myo-inositol in the anterior cingulate cortex, consistent with CNS target engagement. Ebselen decreased slow-wave sleep and affected emotional processing by increasing recognition of some emotions, decreasing latency time in the acoustic startle paradigm and decreasing the reinforcement of rewarding stimuli. In summary, ebselen affects the phosphoinositide cycle and has CNS effects on surrogate markers that may be relevant to the treatment of bipolar disorder, which can be tested in future clinical trials.<\/li>\n<\/ul>\n<p>Moustafa, A. A., Gluck, M. A., Herzallah, M. M. &amp; Myers, C. E. (2015). <a href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/26257616\" target=\"_blank\" rel=\"noopener\">The influence of trial order on learning from reward vs. punishment in a probabilistic categorization task: experimental and computational analyses.<\/a> Frontiers in Behavioral Neuroscience, 9:153.<\/p>\n<ul>\n<li>Previous research has shown that trial ordering affects cognitive performance, but this has not been tested using category-learning tasks that differentiate learning from reward and punishment. Here, we tested two groups of healthy young adults using a probabilistic category learning task of reward and punishment in which there are two types of trials (reward, punishment) and three possible outcomes: (1) positive feedback for correct responses in reward trials; (2) negative feedback for incorrect responses in punishment trials; and (3) no feedback for incorrect answers in reward trials and correct answers in punishment trials. We found that early training on reward-based trials led to omission of reward being treated as similar to punishment, but prior training on punishment-based trials led to omission of reward being treated more neutrally. This suggests that early training on one type of trials, specifically reward-based trials, can create a bias in how neutral feedback is processed, relative to those receiving early punishment-based training or training that mixes positive and negative outcomes.<\/li>\n<\/ul>\n<p><strong> 2014 <\/strong><\/p>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Tomer-2014-Love-to-win-or-hate-to-lose.pdf\" target=\"_blank\" rel=\"noopener\"> Tomer, R., Slagter, H. A., Christian, B. T., Fox, A. S., King, C. R., Murali, D., Gluck, M. A., &amp; Davidson, R. J., (2014). Love to win or hate to lose? Asymmetry of dopamine D2 receptor binding predicts sensitivity to reward vs. punishment. <em>Journal of Cognitive Neuroscience<\/em>. In press.<\/a><\/p>\n<ul>\n<li>Humans show consistent differences in the extent to which their behavior reflects a bias towards appetitive approach-related behavior or avoidance of aversive stimuli (Elliot, 2008). We examined the hypothesis that in healthy subjects this motivational bias (assessed by self-report and by a probabilistic learning task that allows direct comparison of the relative sensitivity to reward and punishment) reflects lateralization of dopamine signaling. Using [F-18]fallypride to measure D2\/D3 binding , we found that self-reported motivational bias was predicted by the asymmetry of frontal D2 binding. Similarly, striatal and frontal asymmetries in D2 dopamine receptor binding, rather than absolute binding levels, predicted individual differences in learning from reward vs. punishment. These results suggest that normal variation in asymmetry of dopamine signaling may, in part, underlie human personality and cognition.<\/li>\n<\/ul>\n<p><strong> 2013 <\/strong><\/p>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/2013.Vadhan.LTCoc_.S-R.pdf\" target=\"_blank\" rel=\"noopener\">Vadhan, N. P., Myers, C. E., Benedict, E., Rubin, E., Foltin, R. W., &amp; Gluck, M. A. (2013, November 4). A Decrement in Probabilistic Category Learning in Cocaine Users After Controlling for Marijuana and Alcohol Use. Experimental and Clinical Psychopharmacology. Advance online publication. doi: 10.1037\/a0034506.<\/a><\/p>\n<ul>\n<li>Aspects of stimulus-response (S-R) learning, mediated by striatal dopamine signaling, have been found to be altered in cocaine users relative to healthy controls. However, the influence of cocaine users\u0092 marijuana and alcohol use has not been accounted for. This study evaluated S-R learning and other neurocognitive functions in cocaine users while controlling for the relative influences of marijuana and alcohol use. Twenty-five long-term cocaine users and 2 control groups (25 moderate marijuana and alcohol users and 23 healthy controls) completed a computerized assessment of probabilistic category learning (the Weather Prediction task), as well as measures of equivalence learning, declarative learning, and executive, attentional, and motor function. Cocaine users exhibited decreased performance on the Weather Prediction task, as well as measures of declarative learning, attention, and motor function (p\u00010.05), relative to both control groups. Cocaine users exhibited decrements in probabilistic category learning, declarative recall, and attentional and motor function, compared with both marijuana and alcohol users and nondrug users. Therefore, these decrements appear to be specifically related to the cocaine use, but not the moderate marijuana and alcohol use, of long-term cocaine users.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/QF-MDD-Final-Manuscript.pdf\" target=\"_blank\" rel=\"noopener\"> Herzallah, M. M., Moustafa, A. A., Natsheh, J. Y., Abdellatif, S. M, Taha, M. B., Tayem, Y. I., Sehwail, M. A., Amleh, I., Petrides, G., Myers, C. E, &amp; Gluck, M. A. (2013\/in press). Learning from negative feedback in patients with major depressive disorder is attenuated by SSRI antidepressants. <em>Frontiers in Integrative Neuroscience<\/em>. <\/a><\/p>\n<ul>\n<li>One barrier to interpreting past studies of cognition and Major Depressive Disorder (MDD) has been the failure in many studies to adequately dissociate the effects of MDD from the potential cognitive side effects of Selective Serotonin Reuptake Inhibitors (SSRI) use. To better understand how remediation of depressive symptoms affects cognitive function in MDD, we evaluated three groups of subjects: medication &#8211; na\u00efve patients with MDD, medicated patients with MDD receiving the SSRI paroxetine and healthy control subjects. All were administered a category &#8211; learning task that allows for dissociation between learning from positive feedback (reward) versus learning from negative feedback (punishment). Healthy subjects learned significantly better from positive feedback than medication &#8211; na\u00efve and medicated MDD groups, whose learning accuracy did not differ significantly. In contrast, medicated patients with MDD learned significantly less from negative feedback than medication &#8211; na\u00efve patients with MDD and healthy subjects, whose learning accuracy was comparable. A comparison of subject&#8217;s relative sensitivity to positive versus negative feedback showed that both the medicated MDD and healthy control groups conform to Kahneman and Tversky&#8217;s (1979) Prospect Theory, which expects losses (negative feedback) to loom psychologically slightly larger than gains (positive feedback). However,medicated MDD and HC profiles are not similar, which indicates that the state of medicated MDD is not &#8216;normal&#8217; when comparedto HC, but rather balanced with less learning from both positive and negative feedback. On the other hand, medication &#8211; na\u00efve patients with MDD violate Prospect Theory by having significantly exaggerated learning from negative feedback. This suggests that SSRI antidepressants impair learning from negative feedback, while having negligible effect on learning from positive feedback. Overall, these findings shed light on the importance of dissociating the cognitive consequences of MDD from those of SSRI treatment , and from cognitive evaluation of MDD subjects in a medication &#8211; na\u00efve state before the administration of antidepressants. Future research is needed to correlate the mood &#8211; elevating effects and the cognitive balance between reward &#8211; and punishment &#8211; based learning related to SSRIs.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Depression-impairs-learning-whereas-the-selective-serotonin-reuptake-inhibitor-paroxetine-impairs-generalization-in-patients-with-major-depressive-disorder.pdf\" target=\"_blank\" rel=\"noopener\"> Herzallah, M. M., Moustafa, A. A., Natsheh, J. Y.,Danoun, O. A., Simon, J.R., Tayem, Y. I., Sehwail, M. A., Amleh, I., Bannoura, I., Petrides, G., Myers, E. E, &amp; Gluck, M. A. (2013\/in press). Depression impairs learning, whereas the selective serotonin reuptake inhibitor, paroxetine, impairs generalization in patients with major depressive disorder. <em>Journal of Affective Disorders<\/em>. (http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/23953023) <\/a><\/p>\n<ul>\n<li>To better understand how medication status and task demands affect cognition in major depressive disorder (MDD), we evaluated medication-na\u00efve patients with MDD, medicated patients with MDD receiving the selective serotonin reuptake inhibitors (SSRI) paroxetine, and healthy controls. All three groups were administered a computer-based cognitive task with two phases, an initial phase in which a sequence is learned through reward-based feedback (which our prior studies suggest is striatal- dependent), followed by a generalization phase that involves a change in the context where learned rules are to be applied (which our prior studies suggest is hippocampal-region dependent). Medication-na\u00efve MDD patients were slow to learn the initial sequence but were normal on subsequent generalization of that learning. In contrast, medicated patients learned the initial sequence normally, but were impaired at the generalization phase. We argue that these data suggest (i) an MDD-related impairment in striatal- dependent sequence learning which can be remediated by SSRIs and (ii) an SSRI-induced impairment in hippocampal-dependent generalization of past learning to novel contexts, not otherwise seen in the medication-na\u00efve MDD group. Thus, SSRIs might have a beneficial effect on striatal function required for sequence learning, but a detrimental effect on the hippocampus and other medial temporal lobe structures is critical for generalization<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/SimonGluck_PA13.pdf\" target=\"_blank\" rel=\"noopener\"> Simon, J.R. &amp; Gluck, M.A. (2013\/ in press). Adult age differences in learning and generalization of reward-based associations. <em>Psychology and Aging<\/em>. <\/a><\/p>\n<ul>\n<li>Feedback-based associative learning (e.g., acquiring new associations from positive or negative outcomes) and generalization (e.g., applying past learning to new settings) are important cognitive skills that enable people to make economic decisions or social judgments. This ability to acquire new skills based on feedback and transfer those experiences to predict positive outcomes in novel situations is essential at all ages, but especially among older adults who must continually adapt to new people, environments and technologies. Ample evidence from animal work, clinical research and computational modeling has demonstrated that feedback-based associative learning is sensitive to basal ganglia dysfunction and generalization to medial temporal lobe dysfunction. This dissociation is relevant because of recent evidence suggesting that healthy aging compromises the basal ganglia system earlier than the medial temporal lobes. However, few studies have investigated how healthy aging influences these cognitive processes. Here, we examined both feedback-based associative learning and generalization in younger, middle-aged and older adults using a computer- ized acquired equivalence task. Results revealed a significant effect of age group on feedback-based associative learning, consistent with evidence of persistent age-related declines in the basal ganglia. In contrast, generalization was spared in all but the oldest adult group, likely reflecting preserved medial temporal lobe function until advanced old age. Our findings add behavioral evidence to the emerging view that healthy aging affects the striatal system before the medial temporal lobes. Although further evidence is needed, this finding may shed light on the possible time course of neural system dysfunction in healthy aging.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/2013_MoustafaHippModel2013.pdf\" target=\"_blank\" rel=\"noopener\">Moustafa, A. A., Wufong, E., Servatius, R. J., Pang, K. C., Gluck, M. A., &amp; Myers, C. E. (2013). Why trace and delay conditioning are sometimes (but not always) hippocampal dependent: A computational model. <em>Brain Research<\/em>. 1493: 48-67.<\/a><\/p>\n<ul>\n<li>A recurrent-network model provides a unified account of the hippocampal region in mediating the representation of temporal information in classical eyeblink conditioning. Much empirical research is consistent with a general conclusion that delay conditioning (in which the conditioned stimulus CS and unconditioned stimulus US overlap and co-terminate) is independent of the hippocampal system, while trace conditioning (in which the CS terminates before US onset) depends on the hippocampus. However, recent studies show that, under some circumstances, delay conditioning can be hippocampal-dependent and trace conditioning can be spared following hippocampal lesion. Here, we present an extension of our prior trial-level models of hippocampal function and stimulus representation (Gluck &amp; Myers, 1993, 2001) that can explain these findings within a unified framework. Specifically, the current model includes adaptive recurrent collateral connections that aid in the representation of intra-trial temporal information. With this model, as in our prior models, we argue that the hippocampus is not specialized for conditioned response timing, but rather is a general-purpose system that learns to predict the next state of all stimuli given the current state of variables encoded by activity in recurrent collaterals. As such, the model correctly predicts that hippocampal involvement in classical conditioning should be critical not only when there is an intervening trace interval, but also when there is a long delay between CS onset and US onset. Our model simulates empirical data from many variants of classical conditioning, including delay and trace paradigms in which the length of the CS, the inter-stimulus interval, or the trace interval is varied. Finally, we discuss model limitations, future directions, and several novel empirical predictions of this temporal processing model of hippocampal function and learning.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/2013_neurodegener_dis.pdf\" target=\"_blank\" rel=\"noopener\">Moustafa, A. A., Herzallah, M. M., &amp; Gluck, M. A. (2013). Dissociating the cognitive effects of levodopa versus dopamine agonists in a neurocomputational model of learning in Parkinson&#8217;s disease. <em>Neurodegenerative Disorders<\/em>. 11:102-111 DOI: 10.1159\/000341999 <\/a><\/p>\n<ul>\n<li>Background\/Aims: Levodopa and dopamine agonists have different effects on the motor, cognitive, and psychiatric aspects of Parkinson&#8217;s disease (PD). Methods: Using a compu- tational model of basal ganglia (BG) and prefrontal cortex (PFC) dopamine, we provide a theoretical synthesis of the dissociable effects of these dopaminergic medications on brain and cognition. Our model incorporates the findings that levodopa is converted by dopamine cells into dopamine, and thus activates prefrontal and striatal D1 and D2 do- pamine receptors, whereas antiparkinsonian dopamine ag- onists directly stimulate D2 receptors in the BG and PFC (although some have weak affinity to D1 receptors). Results: In agreement with prior neuropsychological studies, our model explains how levodopa enhances, but dopamine agonists impair or have no effect on, stimulus-response learning and working memory. Conclusion: Our model explains how le- vodopa and dopamine agonists have differential effects on motor and cognitive processes in PD.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/GSTR_A_744391.pdf\" target=\"_blank\" rel=\"noopener\">Sheynin, J. Shikari, S., Gluck, M. A., Moustafa, A. A., Servatius, R. J. &amp; Myers, C. E. (2013). Enhanced avoidance learning in behaviorally inhibited young men and women. <em>Stress<\/em>, 16(3), 289-299. <\/a><\/p>\n<ul>\n<li>Behavioral inhibition (BI) is a temperamental tendency to avoid or withdraw from novel social and nonsocial situations, and has been shown to predispose individuals to anxiety disorders. However, adequate means to assess individual differences in avoidance learning in humans are presently limited. Here, we tested whether individuals with high self-reported BI show faster associative learning on a purely cognitive task and whether such inhibited individuals are more prone to avoid aversive outcomes. In Experiment 1, we tested 74 healthy undergraduate students on a computer-based probabilistic classification task, where participants were asked to classify four distinct visual stimuli into two categories. Two stimuli were associated with reward (point gain) and two were associated with punishment (point loss). In Experiment 2, 79 participants from the same population were tested on a novel modification of the same task, where they also had the option to opt out of responding on each trial, thus avoiding any chance of being punished (or rewarded) on that trial. Results show that inhibited participants demonstrated better associative learning in Experiment 1, while exhibiting a greater tendency to opt out in Experiment 2. These results indicate that the facilitated classically conditioned learning previously observed in inhibited individuals can be extended to a cognitive task, and also highlight a specific preference in inhibited individuals for withdrawal (&#8220;opting out&#8221;) as a response strategy, when multiple strategies are available to avoid punishment.<\/li>\n<\/ul>\n<p><strong> 2012 <\/strong><\/p>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/LevyGigiPTSD2012.pdf\" target=\"_blank\" rel=\"noopener\">Levi-Gigi, E., K\u00e9ri, S., Myers, C.E., Lencovsky, Z., Sharvit-Benbaji, H., Orr, S.P., Gilbertson, M.W., Servatius, R.J., Tsao, J.W. &amp; Gluck, M.A. (2012). Individuals with post-traumatic stress disorder show a selective deficit in generalization of associative learning. <em>Neuropsychology<\/em>, 26(6), 758-767<\/a><\/p>\n<ul>\n<li>Drawing on two different populations, Israeli police and Hungarian civilians, the present study assessed the ability of individuals with Post-Traumatic Stress Disorder (PTSD) to generalize previous learning to novel situations. Past neuroimaging studies have demonstrated diminished medial temporal lobe (MTL) activation and\/or reduced hippocampal volume in individuals with PTSD. Our earlier computational models of cortico-hippocampal function and subsequent experimental tests of these models in MTL-impaired clinical populations argue that even mild hippocampal dysfunction may result in subtle impairments in generalization. Therefore, we predicted that individuals with PTSD would show impaired generalization. Participants were tested on a two-phase learning paradigm, the Acquired Equivalence Task, which measures the ability to generalize past learning to novel situations. We found that both PTSD and non-PTSD participants were capable of learning the initial stimulus-outcome associations. However, as predicted, only individuals with PTSD showed a selective deficit in generalization of this learning to novel situations. This is consistent with an emerging view of PTSD as being not only an anxiety disorder but also a learning disorder.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/MattyassyAlcAlcEpub.pdf\" target=\"_blank\" rel=\"noopener\">M\u00e1tty\u00e1ssy, A., K\u00e9ri, S., Myers, C. E., Levy-Gigi, E. , Gluck, M. A. &amp; Kelemen, O. (2012). Impaired generalization of associative learning in latients with alcohol dependence after intermediate-term abstinence. <em>Alcohol and Alcoholism<\/em>, 47(5), 533-537. In Press: doi: 10.1093\/alcalc\/ags050<\/a><\/p>\n<ul>\n<li>We used an associative learning task to investigate cognitive dysfunctions in alcohol dependence. This test is suitable for the assessment of stimulus\u0096response learning and memory generalization (acquired equivalence), which is related to medial temporal lobe functioning. Methods: Twenty patients with alcohol dependence (abstinence: &gt;6 months) and 20 matched healthy controls participated. In the task, antecedent stimuli were cartoon faces and consequent stimuli were color cartoon fishes. The task was to learn face\u0096fish associations using feedback. In the transfer phase, the fish\u0096face pairs were generalized to new associations. Results: There was no significant difference between patients and controls during the acquisition phase of fish\u0096face associations. In the transfer phase, however, patients were impaired relative to controls. We found no association between task performance and intelligence. Conclusion: These results suggest that abstinent patients with alcohol dependence show marked dysfunctions in in the generalization of associations, which may indicate the dysfunction of the medial temporal lobe.<\/li>\n<\/ul>\n<p><strong> 2011 <\/strong><\/p>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/2011_Mattfled_LM.pdf\" target=\"_blank\" rel=\"noopener\">Mattfeld, A. T., Gluck, M. A. and Stark C. L., (2011). Functional specialization within the striatum along both the dorsal\/ventral and anterior\/posterior axes during associative learning via reward and punishment. <em>Learning and Memory<\/em>. 8:703-711.<\/a><\/p>\n<ul>\n<li>The goal of the present study was to elucidate the role of the human striatum in learning via reward and punishment during an associative learning task. Previous studies have identified the striatum as a critical component in the neural circuitry of reward-related learning. It remains unclear, however, under what task conditions, and to what extent, the striatum is modulated by punishment during an instrumental learning task. Using high-resolution fMRI during a reward- and punishment-based probabilistic associative learning task, we observed activity in the ventral putamen for stimuli learned via reward regardless of whether participants were correct or incorrect (i.e., outcome). In contrast, activity in the dorsal caudate was modulated by trials that received feedback \u0096 either correct reward or incorrect punishment trials. We also identified an anterior\/posterior dissociation reflecting reward and punishment prediction error estimates. Additionally, differences in patterns of activity that correlated with the amount of training were identified along the anterior\/posterior axis of the striatum. We suggest that unique subregions of the striatum \u0096 separated along both a dorsal\/ventral and anterior\/posterior axis \u0096 differentially participate in the learning of associations through reward and punishment.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/1-s2.0-S0197458009002620-main.pdf\" target=\"_blank\" rel=\"noopener\">Montgomery, K. S., Simmons, R. K., Edwards, G., Nicolle, M. M., Gluck, M. A., Myers, C. E., &amp; Bizon, J. L. (2011). Novel age-dependent learning deficits in a mouse model of Alzheimer&#8217;s disease: implications for translational research. <em>Neurobiology of Aging<\/em>. 32(7), 1273-85.<\/a><\/p>\n<ul>\n<li>Computational modeling predicts that the hippocampus plays an important role in the ability to apply previously learned information to novel problems and situations (referred to as the ability to generalize information or simply as \u0091transfer learning\u0092). These predictions have been tested in humans using a computer-based task on which individuals with hippocampal damage are able to learn a series of complex discriminations with two stimulus features (shape and color), but are impaired in their ability to transfer this information to newly configured problems in which one of the features is altered. This deficit occurs despite the fact that the feature predictive of the reward (the relevant information) is not changed. The goal of the current study was to develop a mouse analog of transfer learning and to determine if this new task was sensitive to pathological changes in a mouse model of AD. We describe a task in which mice were able to learn a series of concurrent discriminations that contained two stimulus features (odor and digging media) and could transfer this learned information to new problems in which the irrelevant feature in each discrimination pair was altered. Moreover, we report age-dependent deficits specific to transfer learning in APP + PS1 mice relative to non-transgenic littermates. The robust impairment in transfer learning may be more sensitive to AD-like pathology than traditional cognitive assessments in that no deficits were observed in the APP + PS1 mice on the widely used Morris water maze task. These data describe a novel and sensitive paradigm to evaluate mnemonic decline in ADmouse models that has unique translational advantages over standard species-specific cognitive assessments (e.g., water maze for rodent and delayed paragraph recall for humans).<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Levy_aMCI_Neuropsychologia_epub.pdf\" target=\"_blank\" rel=\"noopener\">Levy-Gigi, E., Kelemen, O., Gluck, M. A., &amp; K\u00e9ri, S. (2011). Impaired context reversal learning, but not cue reversal learning, in patients with amnestic mild cognitive impairment. <em>Neuropsychologia<\/em>, 49, 3320-3326.<\/a><\/p>\n<ul>\n<li>We assessed 30 newly diagnosed patients with amnestic mild cognitive impairment (aMCI) and 30 matched healthy controls. Reversal learning was assessed using a novel reinforcement learning task developed in our lab at Rutgers University. Participants first acquired and then reversed stimulus\u2013outcome associations based on negative and positive feedback (losing and gaining points). Stimuli consisted of a cue (geometric shapes) and a spatial context (background color or pattern). Relative to controls, patients with aMCI exhibited a marked reversal learning deficit, which was highly selective for the reversal of context. The acquisition of stimulus\u2013outcome associations and cue reversal learning were spared. Performance on the context reversal learning task significantly correlated with the right hippocampal volume.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Moustafa2011NeuralNetworks.pdf.pdf\" target=\"_blank\" rel=\"noopener\">Moustafa, A. A. &amp; Gluck, M. A. (2011). Computational cognitive models of prefrontal-striatal-hippocampal interactions in Parkinson&#8217;s disease and schizophrenia. <em>Neural Networks<\/em>, 24(6),575-591. In press (doi:10.1016\/j.neunet.2011.02.006)<\/a><\/p>\n<ul>\n<li>Disruption to different components of the prefrontal cortex, basal ganglia, and hippocampal circuits leads to various psychiatric and neurological disorders including Parkinson&#8217;s disease (PD) and schizophrenia. Medications used to treat these disorders (such as levodopa, dopamine agonists, antipsychotics, among others) affect the prefrontal-striatal-hippocampal circuits in a complex fashion. We have built models of prefrontal-striatal and striatal-hippocampal interactions which simulate cognitive dysfunction in PD and schizophrenia. In these models, we argue that the basal ganglia is key for stimulus-response learning, the hippocampus for stimulus-stimulus representational learning, and the prefrontal cortex for stimulus selection during learning about multidimensional stimuli. In our models, PD is associated with reduced dopamine levels in the basal ganglia and prefrontal cortex. In contrast, the cognitive deficits in schizophrenia are associated primarily with hippocampal dysfunction, while the occurrence of negative symptoms is associated with frontostriatal deficits in a subset of patients. In this paper, we review our past models and provide new simulation results for both PD and schizophrenia. We also describe an extended model that includes simulation of the different functional role of D1 and D2 dopamine receptors in the basal ganglia and prefrontal cortex, a dissociation we argue is essential for understanding the nonuniform effects of levodopa, dopamine agonists, and antipsychotics on cognition. Motivated by clinical and physiological data, we discuss model limitations and challenges to be addressed in future models of these brain disorders.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Somlai2010SchizophreniaResearch.pdf\" target=\"_blank\" rel=\"noopener\">Somlai, Z., Moustafa, A. A., K\u00e9ri, S., Myers, C. E., &amp; Gluck, M. A. (2011). General functioning predicts reward and punishment learning in schizophrenia. Schizophrenia Research. Aug 25 Epub.<\/a><\/p>\n<ul>\n<li>Previous studies investigating feedback-driven reinforcement learning in patients with schizophrenia have provided mixed results. In this study, we explored the clinical predictors of reward and punishment learning using a probabilistic classification learning task. Patients with schizophrenia (n=40) performed similarly to healthy controls (n=30) on the classification learning task. However, more severe negative and general symptoms were associated with lower reward-learning performance, whereas poorer general psychosocial functioning was correlated with both lower reward- and punishment-learning performances. Multiple linear regression analyses indicated that general psychosocial functioning was the only significant predictor of reinforcement learning performance when education, antipsychotic dose, and positive, negative and general symptoms were included in the analysis. These results suggest a close relationship between reinforcement learning and general psychosocial functioning in schizophrenia.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Moustafa_Gluck_prefrontal-basal_ganglia_model_of_Parkinsons_disease_2011.pdf\" target=\"_blank\" rel=\"noopener\"> Moustafa, A.A., &amp; Gluck, M.A. (2011). A neurocomputational model of dopamine and prefrontal-striatal interactions during multi-cue category learning by Parkinson&#8217;s patients. <em>Journal of Cognitive Neuroscience<\/em>, 23(1), 151-167. <\/a><\/p>\n<ul>\n<li>Most existing models of dopamine and learning in Parkinson disease (PD) focus on simulating the role of basal ganglia dopamine in reinforcement learning. Much data argue, however, for a critical role for prefrontal cortex (PFC) dopamine in stimulus selection in attentional learning. Here, we present a new computational model that simulates performance in multicue category learning, such as the \u0093weather prediction\u0094 task. The model addresses how PD and dopamine medications affect stimulus selection processes, which mediate reinforcement learning. In this model, PFC dopamine is key for attentional learning, whereas basal ganglia dopamine, consistent with other models, is key for reinforcement and motor learning. The model assumes that competitive dynamics among PFC neurons is the neural mechanism underlying stimulus selection with limited attentional resources, whereas competitive dynamics among striatal neurons is the neural mechanism underlying action selection. According to our model, PD is associated with decreased phasic and tonic dopamine levels in both PFC and basal ganglia. We assume that dopamine medications increase dopamine levels in both the basal ganglia and PFC, which, in turn, increase tonic dopamine levels but decrease the magnitude of phasic dopamine signaling in these brain structures. Increase of tonic dopamine levels in the simulated PFC enhances attentional shifting performance. The model provides a mechanistic account for several phenomena, including (a) medicated PD patients are more impaired at multicue probabilistic category learning than unmedicated patients and (b) medicated PD patients opt out of reversal when there are alternative and redundant cue dimensions.<\/li>\n<\/ul>\n<p><strong> 2010 <\/strong><\/p>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Moustafa2010BrainandRecognition.pdf\" target=\"_blank\" rel=\"noopener\">Moustafa, A. A., K\u00e9ri, S., Herzallah, M. M., Myers, C. E., &amp; Gluck, M. A. (2010). A neural model of hippocampal\u0096striatal interactions in associative learning and transfer generalization in various neurological and psychiatric patients. <em>Brain and Cognition<\/em>, 74, 132\u0096144. <\/a><\/p>\n<ul>\n<li>Building on our previous neurocomputational models of basal ganglia and hippocampal region function (and their modulation by dopamine and acetylcholine, respectively), we show here how an integration of these models can inform our understanding of the interaction between the basal ganglia and hippocampal region in associative learning and transfer generalization across various patient populations. As a common test bed for exploring interactions between these brain regions and neuromodulators, we focus on the acquired equivalence task, an associative learning paradigm in which stimuli that have been associated with the same outcome acquire a functional similarity such that subsequent generalization between these stimuli increases. This task has been used to test cognitive dysfunction in various patient populations with damages to the hippocampal region and basal ganglia, including studies of patients with Parkinson\u0092s disease (PD), schizophrenia, basal forebrain amnesia, and hippocampal atrophy. Simulation results show that damage to the hippocampal region\u0097as in patients with hippocampal atrophy (HA), hypoxia, mild Alzheimer\u0092s (AD), or schizophrenia\u0097leads to intact associative learning but impaired transfer generalization performance. Moreover, the model demonstrates how PD and anterior communicating artery (ACoA) aneurysm\u0097two very different brain disorders that affect different neural mechanisms\u0097 can have similar effects on acquired equivalence performance. In particular, the model shows that simulating a loss of dopamine function in the basal ganglia module (as in PD) leads to slow acquisition learning but intact transfer generalization. Similarly, the model shows that simulating the loss of acetylcholine in the hippocampal region (as in ACoA aneurysm) also results in slower acquisition learning. We argue from this that changes in associative learning of stimulus\u0096action pathways (in the basal ganglia) or changes in the learning of stimulus representations (in the hippocampal region) can have similar functional effects.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Keri2010ProceedingsofNationalAcademyofSciences.pdf\" target=\"_blank\" rel=\"noopener\">K\u00e9ri , S., Moustafa, A. A., Myers, C. E., Benedek, G., &amp; Gluck, M. A. (2010). Alpha-synuclein gene duplication impairs reward learning. <em>Proceedings of the National Academy of Sciences<\/em>. 107 (36). 15992-94. <\/a><\/p>\n<ul>\n<li>Alpha-synuclein plays an important role in the regulation of dopaminergic neurotransmission and neurodegeneration in Parkinson disease. We investigated reward and punishment learning in asymptomatic carriers of a rare alpha-synuclein gene duplication who were healthy siblings of patients with Parkinson disease. Results revealed that healthy gene duplication carriers displayed impaired reward and intact punishment learning compared with noncarriers. These results demonstrate that a copy number variation of the alpha-synuclein gene is associated with selective impairments on reinforcement learning in asymptomatic carriers without the motor symptoms of Parkinson disease.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Herzallah2010CognitiveBehavioralNeurology.pdf\" target=\"_blank\" rel=\"noopener\">Herzallah, M. M., Moustafa, A. A., Misk, A. J., Al-Dweib, L. H., Abdelrazeq, S. A., Myers, C. E., &amp; Gluck, M. A. (2010). Depression impairs learning whereas anticholinergics impair transfer generalization in Parkinson patients tested on dopaminergic medications. <em>Cognitive &amp; Behavioral Neurology<\/em>. 23(2). 98-105. <\/a><\/p>\n<ul>\n<li>In a study of acquired equivalence in Parkinson disease (PD), in which patients were tested on normal dopaminergic medication, we found that comorbid clinical depression impairs initial acquisition, whereas the use of anticholinergic therapy impairs subsequent transfer generalization. In addition, this study provides a replication of the basic finding of Myers et al (2003) that patients with PD on dopaminergic therapy are impaired at initial acquisition, but normal at subsequent transfer generalization, generalizing these results to an Arabic-speaking population including many participants with no formal education. These results are consistent with our past computational modeling, which argues that acquisition of incrementally acquired, feedback-based learning tasks is dependent on cortico-striatal circuits, whereas transfer generalization is dependent on medial temporal (MT) structures. They are also consistent with prior computational modeling, and with empiric work in humans and animals, suggesting that anticholinergic drugs may particularly impair cognitive abilities that depend on the MT lobe.<\/li>\n<\/ul>\n<p><strong> 2009<\/strong><\/p>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Moustafa_hippo_model_09.pdf\" target=\"_blank\" rel=\"noopener\">Moustafa, A. A., Myers, C. E., &amp; Gluck, M. A. (2009). A neurocomputational model of classical conditioning phenomena: a putative role for the hippocampal region in associative learning. <em>Brain Research<\/em>, 1276, 180-195. <\/a><\/p>\n<ul>\n<li>Some existing models of hippocampal function simulate performance in classical conditioning tasks using the error backpropagation algorithm to guide learning (Gluck, M.A., and Myers, C.E., (1993). Hippocampal mediation of stimulus representation: a computational theory. Hippocampus, 3(4), 491\u0096516.). This algorithm is not biologically plausible because it requires information to be passed backward through layers of nodes and assumes that the environment provides information to the brain about what correct outputs should be. Here, we show that the same information-processing function proposed for the hippocampal region in the Gluck and Myers (1993) model can also be implemented in a network without using the backpropagation algorithm. Instead, our newer instantiation of the theory uses only (a) Hebbian learning methods which match more closely with synaptic and associative learning mechanisms ascribed to the hippocampal region and (b) a more plausible representation of input stimuli. We demonstrate here that this new more biologically plausible model is able to simulate various behavioral effects, including latent inhibition, acquired equivalence, sensory preconditioning, negative patterning, and context shift effects. In addition, the newer model is able to address some new phenomena including the effect of the number of training trials on blocking and overshadowing.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Bodi_AD_FISH_09.pdf\" target=\"_blank\" rel=\"noopener\">B\u00f3di, N., Csibri, E., Myers, C. E., Gluck, M. A., &amp; K\u00e9ri, S. (2009). Associative learning, acquired equivalence, and flexible generalization of knowledge in mild Alzheimer disease. <em>Cognitive Behavioral Neurology<\/em>, 22(2), 89-94. <\/a><\/p>\n<ul>\n<li>Acquired equivalence is a phenomenon in which prior training to treat two stimuli as equivalent increases generalization between them. Previous studies demonstrated that the hippocampal region might play an important role in acquired equivalence associative learning. In this study, we tested the possibility that acquired equivalence learning is a sensitive marker of mild Alzheimer disease. Alzheimer&#8217;s patients exhibited mild impairments in the training phase, whereas they were profoundly impaired on the acquired equivalence test. Associative knowledge could not be transferred to a more flexible retrieval condition. These results suggest that acquired equivalence learning is specifically impaired in early AD, which may indicate the pathology of the hippocampal region.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/RutledgeJNS09.pdf\" target=\"_blank\" rel=\"noopener\">Rutledge, R. B., Lazzaro, S. C., Lau, B., Myers, C. E., Gluck, M. A., &amp; Glimcher, P. W. (2009). Dopaminergic drugs modulate learning rates and perseveration in Parkinson&#8217;s patients in a dynamic foraging task. <em>Journal of Neuroscience<\/em>. 29(48). 15104-15114.<\/a><\/p>\n<ul>\n<li>Although previous studies have shown that Parkinson\u0092s patients are impaired in tasks involving learning from feedback, they have not directly tested the widely held hypothesis that dopamine neuron activity specifically encodes the reward prediction error signal used in reinforcement learning models. To test a key prediction of this hypothesis, we fit choice behavior from a dynamic foraging task with reinforcement learning models and show that treatment with dopaminergic drugs alters choice behavior in a manner consistent with the theory. More specifically,we found that dopaminergic drugs selectively modulate learning from positive outcomes.We observed no effect of dopaminergic drugs on learning from negative outcomes. We also found a novel dopamine-dependent effect on decision making that is not accounted for by reinforcement learning models: perseveration in choice, independent of reward history, increases with Parkinson\u0092s disease and decreases with dopamine therapy.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/SleepCategory2009.5p.pdf\" target=\"_blank\" rel=\"noopener\"> Djonlagic, I., Rosenfeld, A., Shohamy, D., Myers, C., Gluck, M., &amp; Stickgold, R. (2009). Sleep enhances category learning. <em>Learning and Memory<\/em>. 16. 751-755. <\/a><\/p>\n<ul>\n<li>The ability to categorize objects and events in the world around us is a fundamental and critical aspect of human learning. We trained healthy adults on a probabilistic category-learning task (&#8220;Weather Prediction&#8221;) in two different training modes: Feedback or Observational. The aim of this study was to see whether either form of probabilistic category learning undergoes subsequent enhancement during sleep. The findings reported here represent the first clear evidence that active, off-line memory enhancement of a procedural category rule-learning task takes place throughout a night of sleep, leading to an absolute improvement in performance the next morning. The positive correlation between the extent of learning and the amount of REM sleep obtained the following night suggests that successful learning of the classification of objects and events in the world around us can lead to an increase in subsequent REM sleep, and provides further evidence of an active, sleep-dependent process. Evidence from our previous imaging studies and studies using patients with amnesia and Parkinson\u0092s disease suggests that memory systems supported by the MTL and prefrontal cortex are activated during observational learning and early stages of feedback learning, while the striatum is activated as feedback training continues (Poldrack et al. 2001; Aron et al., 2004; Hopkins et al. 2004; Shohamy et al. 2004a,b). Thus, one possible mechanism that could explain our findings is that a localized sleep-dependent process strengthens memories stored in hippocampal-neocortical networks, but not those stored in the striatum.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/shohamy_jocn_2009.pdf\" target=\"_blank\" rel=\"noopener\">Shohamy, D., Myers, C. E., Hopkins, R. O., Sage, J., &amp; Gluck, M. A. (2009.). Distinct hippocampal and basal ganglia contributions to probabilistic learning and reversal. <em>Journal of Cognitive Neuroscience<\/em>. 21 (9). 1821-1833.<\/a><\/p>\n<ul>\n<li>The hippocampus and the basal ganglia have each, separately, been implicated as necessary for reversal learning-the ability to adaptively change a response when previously learned stimulus-outcome contingencies are reversed. Here, we compared the contribution of the hippocampus and the basal ganglia to distinct aspects of learning and reversal. Amnesic subjects with selective hippocampal damage, Parkinson subjects with disrupted basal ganglia function, and healthy controls were tested on a novel probabilistic learning and reversal paradigm. In this task, reversal can be achieved in two ways: Subjects can reverse a previously learned response, or they can select a new cue during the reversal phase, effectively &#8221;opting out&#8221; of the reversal. We found that both patient groups were intact at initial learning, but differed in their ability to reverse. Amnesic subjects failed to reverse, and continued to use the same cue and response learned before the reversal. Parkinson subjects, by contrast, opted out of the reversal by learning a new cue-outcome association. These results suggest that both the hippocampus and the basal ganglia support reversal learning, but in different ways. The basal ganglia are necessary for learning a new response when a previously learned response is no longer rewarding. The failure of the amnesic subjects to reverse their response or to learn a new cue is consistent with a more general role for the hippocampus in configural learning, and suggests it may also support the ability to respond to changes in cue-outcome contingencies.<\/li>\n<\/ul>\n<ol start=\"2395\">\n<li><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Bodi-et-al.-in-press.pdf\" target=\"_blank\" rel=\"noopener\"> B\u00f3di, S. K\u00e9ri, H. Nagy, A. Moustafa, C. E. Myers, N. Daw, G. Dibo, A. Takats, D. Bereczki, and M. A. Gluck Reward-learning and the novelty-seeking personality: a between- and within-subjects study of the effects of dopamine agonists on young Parkinson&#8217;s patients. <em>Brain<\/em>, September 1, 2009; 132(9): 2385 &#8211; 2395. <\/a><\/li>\n<\/ol>\n<ul>\n<li>In this study, we investigated reward and punishment processing in three groups: young, never-medicated Parkinson&#8217;s disease patients, recently medicated patients receiving the dopamine receptor agonists pramipexole and ropinirole and healthy controls. The never-medicated patients were also re-evaluated after 12 weeks of treatment with dopamine agonists. Reward and punishment processing was assessed by a feedback-based probabilistic classification task. Results revealed that never-medicated patients with Parkinson&#8217;s disease showed selective deficits on reward processing and novelty seeking, which were remediated by dopamine agonists. These medications disrupted punishment processing. In addition, dopamine agonists increased the correlation between reward processing and novelty-seeking personality traits, whereas these drugs decreased the correlation between punishment processing and harm-avoidance personality traits. Our finding that dopamine agonist administration in young patients with Parkinson&#8217;s disease resulted in increased novelty seeking, enhanced reward processing, and decreased punishment processing may shed light on the cognitive and personality bases of the impulse control disorders which arise as side-effects of dopamine agonist therapy in some Parkinson&#8217;s disease patients.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/BBR_5811.pdf\" target=\"_blank\" rel=\"noopener\">Guthrie, M., Myers, C. E., &amp; Gluck, M. A. (2009). A neurocomputational model of tonic and phasic dopamine in action selection: A comparison with cognitive deficits in Parkinson\u0092s disease. <em>Behavioral Brain Research<\/em>.<\/a><\/p>\n<ul>\n<li>The striatal dopamine signal has multiple facets; tonic level, phasic rise and fall, and variation of the phasic rise\/fall depending on the expectation of reward\/punishment.We have developed a network model of the striatal direct pathway using an ionic current level model of the medium spiny neuron that incorporates currents sensitive to changes in the tonic level of dopamine. The model neurons in the network learn action selection based on a novel set of mathematical rules that incorporate the phasic change in the dopamine signal. This network model is capable of learning to perform a sequence learning task that in humans is thought to be dependent on the basal ganglia. When both tonic and phasic levels of dopamine are decreased, as would be expected in unmedicated Parkinson&#8217;s disease (PD), the model reproduces the deficits seen in a human PD group off medication. When the tonic level is increased to normal, but with reduced phasic increases and decreases in response to reward and punishment, respectively, as would be expected in PD medicated with L-Dopa, the model again reproduces the human data. These findings support the view that the cognitive dysfunctions seen in Parkinson&#8217;s disease are not solely either due to the decreased tonic level of dopamine or to the decreased responsiveness of the phasic dopamine signal to reward and punishment, but to a combination of the two factors that varies dependent on disease stage and medication status.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Weickert-Schizo-09.pdf\" target=\"_blank\" rel=\"noopener\">Weickert, T., Goldberg, T., Callicott, Q. C., Apud, J., Das, S., Zoltick, B., Egan, M., Meeter, M., Myers, C., Gluck, M., Weinberger, D., &amp; Mattay, V. (2009). Neural correlates of probabilistic category learning in patients with schizophrenia. <em>Journal of Neuroscience<\/em>. 29(4). 1244-1254. <\/a><\/p>\n<ul>\n<li>Forty patients with schizophrenia receiving antipsychotic medication and 25 healthy participants were assessed on interleaved blocks of probabilistic category learning and control tasks while undergoing functional magnetic resonance imaging. Based on analyses of the patients and healthy adults matched on learning and performance, a minority of patients with schizophrenia achieve successful probabilistic category learning and performance levels through differential activation of a circumscribed neural network which suggests a compensatory mechanism in patients showing successful learning. In particular, we found greater caudate and dorsolateral prefrontal cortex activity in the healthy adults and greater activation in a more rostral region of the dorsolateral prefrontal, cingulate, parahippocampal and parietal cortex in patients. These results suggest that successful probabilistic category learning can occur in the absence of normal frontal-striatal function.<\/li>\n<\/ul>\n<p><strong> 2008<\/strong><\/p>\n<p>&nbsp;<\/p>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Fargas-Keri-2008.pdf\" target=\"_blank\" rel=\"noopener\">Farkas, M., Polgar, P., Kelemen, O., Rethelyi, J., Bitter, I., Myers, C. E., Gluck, M. A., &amp; K\u00e9ri, S. (2008). Associative learning in deficit and non-deficit schizophrenia. <em>Neuroreport<\/em>. 19(1), 55-58.<\/a><\/p>\n<ul>\n<li>We studied feedback-guided associative learning and acquired equivalence in schizophrenia patients who were subtyped as being deficit (showing negative symptoms) or non-deficit (not showing negative symptoms). Acquired equivalence learning, which depends on the medial temporal lobe, was impaired in both subtypes. In contrast, feedback-guided associative learning, which depends on basal ganglia function, was impaired only in the deficit patients. This suggests that the enduring negative symptoms in deficit schizophrenia may be related to decreased response to cognitive feedback and deficient basal ganglia function.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Learning-and-Generalization.pdf\" target=\"_blank\" rel=\"noopener\"> Myers, C. E., Hopkins, R. O, DeLuca, J., Moore, N. B., Wolansky, L. J., &amp; Gluck, M. A. (2008). Learning and generalization deficits in patients with memory impairments due to anterior communicating artery aneurysm rupture or hypoxic brain injury. <em>Neuropsychology<\/em>. 22(5). 681-686. <\/a><\/p>\n<ul>\n<li>Human a anterograde amnesia can result from a variety of etiologies, including hypoxic brain injury and anterior communicating artery (ACoA) aneurysm rupture. Although both etiologies cause a similar severe disruption in declarative memory, we demonstrate here that there are subtle differences in how these patients learn and generalize from incrementally acquired feedback-based learning. In two different tasks, we found that the ACoA patients were slow at initial learning, but completed the transfer generalization phase as well as controls. In contrast, the hypoxic patients tended to show the opposite pattern: normal at initial learning, but impaired at transfer generalization.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/771246311_content-1.pdf\" target=\"_blank\" rel=\"noopener\">Johnson S.C., Schmitz T.W., Asthana S., Gluck M.A., Myers C.E. (2008). Associative Learning Over Trials Activates the Hippocampus in Healthy Elderly but not Mild Cognitive Impairment. <em>Aging, Neuropsychology, and Cognition<\/em>, 15, 129-145.<\/a><\/p>\n<ul>\n<li>The ability to form associations between choice alternatives and their contingent outcomes is an important aspect of learning that may be sensitive to hippocampal dysfunction in memory disorders of aging such as amnestic Mild Cognitive Impairment (aMCI), or early Alzheimer Disease. In this preliminary study we examined brain activation using functional magnetic resonance imaging (fMRI) in twelve healthy elderly participants and nine patients with aMCI during an associative learning task. Using a high-field 3.0 Tesla MRI scanner, we examined the dynamic neural response during associative learning over trials. The slope of signal attenuation associated with learning was analyzed for differences between groups within an a-priori defined hippocampal region. Results indicated dynamic signal attenuation associated with learning in the healthy elderly sample, but not in aMCI. The absence of an associative learning effect in the aMCI sample reaffirms an important link between the learning difficulties that are commonly encountered in aMCI and the medial temporal region.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/NBR_1010.pdf\" target=\"_blank\" rel=\"noopener\">Gluck, M. A., Poldrack, R. A., &amp; K\u00e9ri, S. (2008). The cognitive neuroscience of category learning. <em>Neuroscience and Biobehavioral Reviews<\/em>, 32(2), 193-196. <\/a><\/p>\n<ul>\n<li>The study of category learning has been a central paradigm within cognitive psychology for over 25 years. Cognitive neuroscientists have been drawn to this para- digm for several reasons: first, there is a large body of pre-existing empirical and theoretical analyses of category learning. Neuropsychological studies of clinical populations and neuroimaging of healthy subjects provide insights into the cognitive neuroscience of category learning. Second, category learning has aspects of both elementary associative learning as well as higher-order cognition. On one hand, category learning can be viewed as a &#8221;cognitive skill&#8221; that shares behavioral properties, and possibly some neural substrates, with motor-skill learning and conditioning. It is this dual nature-part elementary skill, part higher cognition-which helps make category learning a valuable paradigm for studying the fundamental aspects of human learning at both the behavioral and neural levels of analysis. In the spring of 2002, the J.S. McDonnell Foundation funded a multidisciplinary collaborative consortium of researchers working in the cognitive neuroscience of category learning. With the conclusion of this three-year consortium, this special issue of Neuroscience and Biobehavioral Reviews presents the broader scientific community with a collection of its highlights, emphasizing new multidisci- plinary collaborations and research which emerged from the consortium.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Chase2008.pdf\" target=\"_blank\" rel=\"noopener\">Chase, H. W., Clark, L., Myers, C. E., Gluck, M. A., Sahakian, B. J., Bullmore, E. T., &amp; Robbins, T. W. (2008). The role of the orbitorfrontal cortex in human discrimination learning. <em>Neuropsychologia<\/em>, 46(5), 1326-1337. <\/a><\/p>\n<ul>\n<li>\u0095 Several lines of evidence implicate the prefrontal cortex in learning but there is little evidence from studies of human lesion patients to demonstrate the critical role of this structure. To this end, we tested patients with lesions of the frontal lobe (n = 36) and healthy controls (n = 35) on two learning tasks: the weather prediction task (WPT), and an eight-pair concurrent visual discrimination task (\u0091Choose\u0092). Performance of both tasks was previously shown to be disrupted in patients with Parkinson\u0092s disease; the Choose deficit was only present when patients were medicated. Patients with damage to the orbitofrontal cortex (OFC) were significantly impaired on Choose, compared to both healthy controls and non-OFC lesion patients. The OFC lesion patients showed a mild deficit on the first 50 trials of the WPT, compared to the control subjects but not non-OFC lesion patients. The selective deficit in the OFC patients on Choose performance could not be attributed to the larger lesion size in this group, and the deficit was not correlated with the volume of damage to adjacent prefrontal subregions (e.g. anterior cingulate cortex). These data support the notion that the OFC play a role in normal discrimination learning, and suggest qualitative similarities in learning performance of patients with OFC damage and medicated PD patients.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/CH18_Gluck_Bowerbook2007.pdf\" target=\"_blank\" rel=\"noopener\"> Gluck, M. A. (2008). Behavioral and neural correlates of error correction in classical conditioning and human category learning. In M. A. Gluck, J. R. Anderson, &amp; S. M. Kosslyn (Eds). <em>Memory and Mind: A Festschrift for Gordon H. Bower<\/em> (pp. 281-305). New York: Lawrence Earlbaum Associates. <\/a><\/p>\n<ul>\n<li>To what extent are the processes of human learning analogous to the more ele- mentary learning processes studied in animal-conditioning experiments? This question, and the broader goal of integrating mathematical models of animal and human learning, was the focus of my collaborative research at Stanford with Gordon Bower in the mid-1980s as well as my doctoral dissertation, which he supervised (Gluck &amp; Bower, 1988a, 1988b, 1990). This chapter is divided into four sections. In the first, I review the concept of error correction, and discuss how this learning principle has been a building block for models of both animal and human learning. Then, I turn to the neural substrates of error correction learning in classical conditioning, discussing the functional roles of three brain regions: the cerebellum, the basal ganglia, and the hippocampus. In the third section, I show how past bridges between animal and human learning provide a behavioral tool for using models and data on the neural substrates of classical conditioning to inform our understanding of the cognitive neuroscience of human learning, especially probabilistic category learning. In the fourth and final section, I briefly review the status of our understanding of the cognitive neuroscience of category learning, and some exciting new research directions that lie ahead.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/meeteretal08-catlearning-NBBR.pdf\" target=\"_blank\" rel=\"noopener\"><br \/>\nMeeter, M., Radics, G., Myers, C. E., Gluck, M. A., Hopkins, R. O. (2008). Probabilistic categorization: How do normal participants and amnesic patients do it? <em>Neuroscience and Biobehavioral Reviews<\/em>. 32, 237-248. <\/a><\/p>\n<ul>\n<li>We review evidence in favor of two alternative conceptualizations of learning in probabilistic categorization: as rule-based learning, or as incremental learning. Each conceptualization forms the basis of a way of analyzing performance: strategy analysis assumes rule-based learning, while rolling regression analysis assumes incremental learning. Here, we contrasted the ability of each to predict performance of normal categorizers. Both turned out to predict responses about equally well. We then reviewed performance of patients with damage to regions deemed important for either rule-based or incremental learning. Evidence was again about equally compatible with either alternative conceptualization of learning, although neither predicted an involvement of the medial temporal lobe. We suggest that a new way of conceptualizing probabilistic categorization might be fruitful, in which the medial temporal lobe helps setup representations that are then used by other regions to assign patterns to categories.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/NBR_985.pdf\" target=\"_blank\" rel=\"noopener\">Shohamy, D., Myers, C. E., Kalanithi, J., &amp; Gluck, M. A. (2008). Basal ganglia and dopamine contributions to probabilistic category learning. <em>Neuroscience and Biobehavioral Reviews<\/em>. 32, 219-236.<\/a><\/p>\n<ul>\n<li>We review behavioral, neuropsychological, functional neuroimaging, and computational studies of basal ganglia and dopamine contributions to category learning in humans. Collectively, these studies implicate the basal ganglia in incremental, feedback-based learning that involves integrating information across multiple experiences. The medial temporal lobes, by contrast, contribute to rapid encoding of relations between stimuli and support flexible generalization of learning to novel contexts and stimuli. By breaking down our understanding of the cognitive and neural mechanisms contributing to different aspects of learning, recent studies are providing insight into how, and when, these different processes support learning, how they may interact with each other, and the consequence of different forms of learning for the representation of knowledge.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/2008.Vadhan.S-R.coc_.pdf\" target=\"_blank\" rel=\"noopener\">Vadhan, N. P, Myers, C. E., Rubin, E., Shohamy, D., Foltin, R. W., &amp; Gluck, M. A. (2008) Stimulus-response learning in long-term cocaine users: acquired equivalence and probabilistic category learning. <em>Drug and Alcohol Dependence<\/em>. 93. 155-162.<\/a><\/p>\n<ul>\n<li>Cocaine-dependent and non-drug using controls were administered two computerized learning tasks. On an acquired equivalence task in which an initial phase of learning with conflicting response demands was followed by a subseqent generalization phase in which the stimuli were presented in novel recombinations, the cocaine users were slower than controls on the initial learning but generalized normally in the second phase. In contrast, when administered a probabilistic &#8220;weather prediction&#8221; category learning task, there were no group differences. These data are consistent with the hypothesis that long-term cocaine users have particular difficulty when established learning interferes with new learning, possibly reflecting altered domaine transmission in the basal ganglia.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/myers2008jgpn.pdf\" target=\"_blank\" rel=\"noopener\">Myers, C, E., Kluger, A., Golomb, J., Gluck, M. A, &amp; Ferris, S. (2008) Learning and generalization tasks predict short-term cognitive outcome in non-demented elderly. <em>Journal of Geriatric Psychiatry and Neurology<\/em>. 21(2). 93-103.<\/a>.<\/p>\n<ul>\n<li>This study examines whether behavioral measures obtained in non-demented elderly can predict cognitive status at two-year follow-up. Prior studies have established that delayed paragraph recall can help predict short-term risk for decline to mild cognitive impairment (MCI) and Alzheimer&#8217;s disease (AD). We examined whether prediction accuracy can be improved by adding a discrimination-and-generalization task that has previously been shown to be disrupted in non-demented elderly with hippocampal atrophy, a risk factor for AD. Fifty non-demented medically medically healthy elderly patients received baseline clinical diagnosis and cognitive testing; two years later, patients received a follow-up clinical diagnosis of normal, MCI, or probable AD. Two baseline variables, delayed paragraph recall and generalization performance, were predictive of follow-up outcome with sensitivity of 81% and specificity of 91% &#8211; better than the classification accuracy based on either of these measures alone. These preliminary results suggest that these behavioral tasks may be useful tools in predicting short-term cognitive outcome in non-demented elderly.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Polgar-Keri-et-al-2008.pdf\" target=\"_blank\" rel=\"noopener\">Polgar, P. Farkas, M, Nagy, O., Kelemen, O. Rethelyi, J. Bitter, I., Myers, C. E., Gluck, M. A., &amp; K\u00e9ri, S. (2009). How to find the way out from four rooms? The learning of &#8220;chaining&#8221; associations may shed light on the neuropsychology of the deficit syndrome in schizophrenia. <em>Schizophrenia Research<\/em>. 99(1-3), 200-207. <\/a><\/p>\n<ul>\n<li>To better understand the cognitive component of the deficit syndrome in schizophrenia in which patients display negative symptoms including apathy, social withdrawal and lack of affect, we studied patients learning a sequence chaining task previously used with Parkinson&#8217;s and aMCI patients (Shohamy et al, 2005; Nagy et al, 2007). Participants navigated a cartoon character through a sequence of four rooms by learning to choose the open door from three colored doors in each room. In the training phase, each stimulus leading to reward (open door in each room) was trained via feedback until the complete sequence was learned. In the probe phase, the decision-making context was manipulated so that, in a given room, there appeared a door which was correct in another room as well as the door that was correct in that room. In our previous papers, we argued that the training phase is predominantly related to basal ganglia circuits while the context-dependent probe phase requires intact medial-temporal lobe functioning. In the current study, both deficit and non-deficit patients (that is, those who display only positive but not negative symptoms) were similarly impaired on the probe phase compared with controls. However, the training phase was only compromised in deficit patients. In particular, more severe negative symptoms were associated with more errors on the training phase. Executive functions were unrelated to performance on this sequence learning task. These results suggest that the deficit syndrome in schizophrenia is associated with prominently impaired stimulus-response reinforcement learning, which may indicate abnormal functioning of basal ganglia circuits.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Keri-Gluck-2007.pdf\" target=\"_blank\" rel=\"noopener\">K\u00e9ri, S., Nagy, H., Myers, C. E., Benedek, G., Shohamy, D., &amp; Gluck, M. A. (2008). Risk and protective haplotypes of the alpha-synuclein gene associated with Parkinson&#8217;s disease differentially affect cognitive sequence learning. <em>Genes, Brain, and Behavior<\/em>. 7 (1). 31-36.<\/a><\/p>\n<ul>\n<li>Alpha-synuclein (SNCA) is a key factor in the regulation of dopaminergic transmission and related to Parkinson&#8217;s disease. In this study, we investigated the effects of risk and protective SNCA haplotypes associated with Parkinson&#8217;s disease on cognitive sequence learning in 204 healthy volunteers. We found that the risk haplotypes are associated with less effective stimulus-reward learning of sequences and with superior context representation of sequences. In contrast, participants with protective haplotypes exhibit better stimulus-reward learning and worse context representation, which suggests that these functions are inversely affected by risk and protective haplotypes. Since stimulus-reward learning may be mediated by the basal ganglia, and context learning may be related to the medial temporal lobe, our data raise the possibility that dopaminergic signals regulated by SNCA inversely affect these memory systems.<\/li>\n<\/ul>\n<p><strong>2007<\/strong><\/p>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/NagyKeri2008.Sequence.Neuropsycholgia.pdf\" target=\"_blank\" rel=\"noopener\">Nagy, H., Myers, C. E., Benedek, M. D., Shohamy, D., Gluck, M., K\u00e9ri, S. Cognitive sequence learning in Parkinson&#8217;s disease and amnestic mild cognitive impairment: dissociation between sequential and non-sequential learning of associations. <em>Neuropsychologia<\/em>. 45, 1386-1392.<\/a><\/p>\n<ul>\n<li>We assessed never-medicated patients with Parkinson&#8217;s disease (PD) and amnestic mild cognitive impairment (aMCI) using a chaining task. In the training phase, each link in a sequence of stimuli leading to reward is trained step-by-step using feedback after each decision, until the complete sequence is learned. In the probe phase of the chaining task, the context of stimulus-response associations must be used (the place of the associations in the sequence). Results revealed that patients with PD showed impaired learning during the training phase of the chaining task, but their performance was spared in the probe phase. In contrast, patients with aMCI with prominent medial temporal lobe (MTL) dysfunctions showed intact learning during the training phase of the chaining task, but their performance was impaired in the probe phase of the chaining task. These results indicate that when dopaminergic mechanisms in the BG are dysfunctional, series of stimulus-response associations are less efficiently acquired, but their sequential manner is maintained. In contrast, MTL dysfunctions may result in a non-sequential learning of associations, which may indicate a loss of contextual information.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Nagy2007.pdf\" target=\"_blank\" rel=\"noopener\"> Nagy, O., Kelemen, O., Benedek, G., Myers, C. E., Shohamy, D., Gluck, M. A. &amp; K\u00e9ri, S. (2007). Dopaminergic contribution to cognitive sequence learning. <em>Journal of Neural Transmission<\/em>. 114. 607-612.<\/a><\/p>\n<ul>\n<li>To test the hypthesis that dopaminergic mechanisms in the basal ganglia are important in feedback-guided habit learning, we assessed cognitive sequence learning in 120 healthy volunteers and measured plasma levels of homovanillic acid [HVA] (a metabolite of dopamine), 5-hydroxyindoleacetic acid [5-HIAA] (a metabolite of serotonin), and 3-methoxy-4-hydroxypheylglycol [MHPG] (a metabolite of norepinephrine). Results revealed a significant negative relationship between errors in the feedback-guided training phase of the sequence learning task and the plasma HVA level. Participant who had lower HVA level than the median value of the whole sample committed more errors during the training phase compared with participants who has higher HVA plasma level than the median value. A similar phenomenon was not observed for the context-dependent phase of the task and for 5-HIAA and MHPG. These results suggest that dopamine plays a special role in feedback-guided cognitive sequence learning.<\/li>\n<\/ul>\n<p><a href=\"http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/18167422?ordinalpos=10&amp;itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DefaultReportPanel.Pubmed_RVDocSum\" target=\"_blank\" rel=\"noopener\">Polg\u00e1r P, Farkas M, Nagy O, Kelemen O, R\u00e9thelyi J, Bitter I, Myers CE, Gluck MA, K\u00e9ri S. (2007). Learning cognitive skills in depression: the effect of context-change. Semmelweis Egyetem, Pszichi\u00e1triai \u00e9s Pszichoter\u00e1pi\u00e1s Klinika, Budapest, Hungary. <em>Psychiatry Hungarian<\/em>, 22(4), 271-275. <\/a><\/p>\n<ul>\n<li>AIMS: Patients with depression show cognitive impairment, including executive function deficit, impairments in attention, declarative memory and psychomotor performance. In addition to classic, widely studied cognitive functions, in depression implicit learning and the interpretation of feedback and its impact on performance can also be impaired compared to healthy individuals. While cognitive functions have been widely studied, much less is known about implicit learning in depression. METHODS: The two-phased Kilroy sequence association test examines the basal ganglia-mediated and the temporal lobe and hippocampus-mediated learning processes within one test. We compared the performance of 22 depressed patients (according to DSM-IV) and 20 healthy control subjects using the Kilroy test. In the depressed group, we also compared the performance on each step of the test with the symptom severity measured by the Hamilton D symptom scale. RESULTS: Depressed patients showed impaired performance compared to healthy subjects on the first, learning phase of the test. The degree of deficit on the learning phase correlated with symptom severity. We found no difference between the two groups on the second, context-dependent phase of the test. CONCLUSION: Our results confirm the presence of a striatal deficit in depressed patients. Results indicate that parallel memory systems are not equally affected in depression, and the character of deficit in depression may be specific to the illness.<\/li>\n<\/ul>\n<p><strong>2006<\/strong><\/p>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Myers.Neuropsychologia2006.pdf\" target=\"_blank\" rel=\"noopener\">Myers, C. E., DeLuca, J., Hopkins, R. O, &amp; Gluck, M. A. (2006). Conditional discrimination and reversal in amnesia subsequent to hypoxic brain injury or anterior communicating artery aneurysm rupture. <em>Neuropsychologia<\/em>. 44(1):130-139.<\/a><\/p>\n<ul>\n<li>Human anterograde amnesia can develop following either bilateral damage to the hippocampus and medial temporal lobes (as in hypoxic injury) or following damage to the basal forebrain, as seen following anterior communicating artery (ACoA) aneurysm rupture. Both yield similar mnestic deficits as assessed by standard neuropsychological measures. However, our previous animal and computational models suggest there should be specific qualitative differences in the pattern of impaired and spared memory abilities following damage to the hippocampus versus basal forebrain. Here, we show such a predicted dissociation in both forms of amnesia using a single two-stage task, involving conditional discrimination and reversal. These results highlight the importance of considering etiology in evaluating mnemonic deficits in amnesic populations.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Gluck2006.Alzheimers.pdf\" target=\"_blank\" rel=\"noopener\">Gluck, M. A., Myers, C. E., Nicolle, M. M. &amp; Johnson, S. (2006). Computational models of the hippocampal region: Implications for prediction of risk for Alzheimer&#8217;s disease in non-demented elderly. <em>Current Alzheimer&#8217;s Research<\/em>. 3. 247-257.<\/a><\/p>\n<ul>\n<li>We have pursued an interdisciplinary research program to develop novel behavioral assessment tools for evaluating specific memory impairments following damage to the medial temporal lobe, including the hippocampus and associated structures that show pathology early in the course of Alzheimer&#8217;s disease (AD). Our approach uses computational models to identify the functional consequences of hippocampal-region damage, leading to testable predictions in both rodents and humans. Our modeling argues that hippocampal-region dysfunction may selectively impair the ability to generalize when familiar information is presented in novel recombinations. Converging support for the relevance of these tasks to aging and Alzheimer&#8217;s disease comes from our recent fMRI studies of individuals with mild cognitive impairment (MCI). A new mouse version of one of our tasks shows promise for translating these paradigms into rodents, allowing for future studies of therapeutic interventions in transgenic mouse models of AD.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Strategies-in-probabilistic-categorization-Results-from-a-new-way-of-analyzing-performance.pdf\" target=\"_blank\" rel=\"noopener\"> Meeter, M., Myers, C.E., Shohamy, D., Hopkins, R.O. &amp; Gluck, M.A. (2006). Strategies in probabilistic categorization: Results from a new way of analyzing performance. <em>Learning &amp; Memory<\/em>, 13, 230-239.<\/a><\/p>\n<ul>\n<li>The &#8220;Weather Prediction&#8221; task is a widely used task for investigating probabilistic category learning, in which various cues are probabilistically (but not perfectly) predictive of class membership. Here, we present a new method for the analysis of probabilistic categorization, which attempts to identify the strategy followed by a participant. Monte Carlo simulations show that the analysis can, indeed, reliably identify such a strategy if it is used, and can identify switches from one strategy to another. Analysis of data from normal young adults shows that the fitted strategy can predict subsequent responses. Analysis of performance of patients with dense memory impairments due to hippocampal damage shows that although these patients can change strategies, they are as likely to fall back to an inferior strategy as to move to more optimal ones.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/shohamy_neuropsychologia_2006.pdf\" target=\"_blank\" rel=\"noopener\">Shohamy, D., Myers, C. E., Geghman, K. D., Sage, J. &amp; Gluck, M.A. (2006). L-Dopa impairs learning, but not generalization, in Parkinson&#8217;s disease. <em>Neuropsychologia<\/em>. 44(5), 774-84.<\/a><\/p>\n<ul>\n<li>L-dopa is commonly used to treat the motor symptoms of Parkinson&#8217;s disease. This study demonstrates that L-dopa may be associated with the opposite effect on some forms of cognitive behavior, since Parkinson&#8217;s patients tested off L-dopa medication were able to perform a learning task better than those patients tested while on L-dopa medication, and no worse than healthy control subjects. Further, we found a dissociation of the effect of L-dopa within a single two-phase task: patients tested on medication were not impaired at the ability to generalize based on learned information, only on initial acquisition. This suggests that dopaminergic modulation of learning is implicated in the rate of learning, but not in how that information is later processed.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/aron_2006_NIMG.pdf\">Aron, A. R, Gluck, M. A, &amp; Poldrack, R. A. (2006). Long-term test-retest reliability of functional MRI in a classification learning task. <em>Neuroimage<\/em>. 23(3), 1000-1006.<\/a><\/p>\n<ul>\n<li>Healthy adult subjects were scanned on two sessions, one year apart, while performing a probabilistic classification learning task known to activate fronto-striatal circuitry. We show that behavioral performance and frontostriatal activation were highly concordant at a group level at both timepoints. We conclude that fMRI can have high long-term test-retest reliability, making it suitable as a biomarker for brain development and neurodegeneration.<\/li>\n<\/ul>\n<p><strong>2005 <\/strong><\/p>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Fera-Weickert.JNeurosci.2005.pdf\">Fera, F., Weickert, T. W., Goldberg, T. E., Tessitore, A., Hariri, A., Das, S., Lee, B., Zoltick, B., Meeter, M., Myers, C. E., Gluck, M. A., Weinberger, D. R., Mattay, V. S., (2005). Neural mechanisms underlying probabilistic category learning in normal aging. <em>Journal of Neuroscience<\/em>, 24(49), 11340-11348.<\/a><\/p>\n<ul>\n<li>Probabilistic category learning engages neural circuitry that includes both the prefrontal cortex and the caudate nucleus of the basal ganglia, two regions that show prominent changes with normal aging. Using the &#8220;weather prediction&#8221; task, young and older adults displayed equivalent learning curves, used similar strategies, and activated analogous brain regions with BOLD fMRI. However, the extent of caudate and prefrontal activation was less, and parietal activation was greater, in older participants. The percentage correct and reaction times were mainly positively correlated with caudate and prefrontal activation in young individuals, but positively correlated with prefrontal and parietal cortices in older individuals. Differential activation within a circumscribed neural network in the context of equivalent learning suggests that some brain regions, such as the parietal cortices, may provide compensatory mechanisms for healthy older adults in the context of deficient prefrontal cortex and caudate nuclei responses.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/keri05schizfish.pdf\">K\u00e9ri, S., Nagy, O., Kelemen, O., Myers, C. E, &amp; Gluck, M. A. (2005). Dissociation between medial temporal and basal ganglia memory systems in schizophrenia. <em>Schizophrenia Research<\/em>, 77, 321-328.<\/a><\/p>\n<ul>\n<li>Basal ganglia and medial temporal lobe dependent learning was studied in patients with schizophrenia using a two-phase acquired equivalence task in which prior training to treat two stimuli as equivalent increases generalization between them (Myers et al, 2003). Patients with schizophrenia showed a selective deficit on stimulus generalization, whereas initial stimulus-response learning was spared. However, errors during the initial stimulus-response learning was correlated with daily dose of chlorpromazine-equivalent antipsychotics. This is the first study to show that patients with schizophrenia exhibit deficits in medial-temporal-dependent learning, but not during basal-ganglia-dependent learning, within a single task. High-dose first generation antipsychotics may disrupt basal-ganglia-dependent learning by blocking dopaminergic neurotransmission in the nigro-striatal system.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Gluck-Myers-Meeter2005.pdf\">Gluck, M. A., Myers, C. E., Meeter, M. (2005). Cortico-hippocampal interaction and adaptive stimulus representation: A neurocomputational theory of associative learning and memory. <em>Neural Networks<\/em>, 18, 1265-1279.<\/a><\/p>\n<ul>\n<li>Computational models of the hippocampal region link psychological theories of associative learning with their underlying physiological and anatomical substrates. Our approach to theory development began with a broad description of the computations that depend on the hippocampal region in classical conditioning (Gluck &amp; Myers, 1993, 2001). In more recent computational modeling, we have shown how some aspects of this proposed information-processing function could emerge from known anatomical and physiological characteristics of the hippocampal region, including the entorhinal cortex and the septohippocampal cholinergic system. The modeling to date lays the groundwork for future directions that increase the depth of detail of the biological modeling, as well as the breadth of behavioral phenomena addressed. In particular, we seek to reconcile these incremental associative learning models with other models of the hippocampal region that account for the rapid formation of declarative memories.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/shohamy_BBR_2004.pdf\">Shohamy, D., Myers, C. E., Grossman, S., Sage, J. &amp; Gluck, M. A. (2005). The Role of dopamine in cognitive sequence learning: Evidence from Parkinson&#8217;s disease. <em>Behavioral Brain Research<\/em>, 156,191-199. <\/a><\/p>\n<ul>\n<li>Individuals with Parkinson&#8217;s disease were impaired on a cognitive sequence learning task which requires learning a chain of associations leading to reward; this impairment was remediated with dopaminegic medication. These findings link the cognitive deficits in Parkinson&#8217;s disease with a rich literature of animal studies exploring the role of dopamine in reward-related learning.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/psychrev-meeteretal-au05.pdf\">Meeter, M., Myers, C. E., &amp; Gluck, M. A. (2005). Integrating incremental learning and episodic memory models of the hippocampal region. <em>Psychological Review<\/em>, 112(3), 560-585.<\/a><\/p>\n<ul>\n<li>By integrating previous computational models of corticohippocampal function, we develop and test a unified theory of the neural substrates of familiarity, recollection, and classical conditioning. This approach integrates models from two traditions of hippocampal modeling, those of episodic memory and incremental learning, by drawing on an earlier mathematical model of conditioning, SOP (A. Wagner, 1981). The model describes how a familiarity signal may arise from parahippocampal cortices, giving a novel explanation for the finding that the neural response to a stimulus in these regions decreases with increasing stimulus familiarity.<\/li>\n<\/ul>\n<p><strong>2004<\/strong><\/p>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/aron.pdf\">Aron, A.R., Shohamy, D., Clark, J., Myers, C., Gluck, M.A. &amp; Poldrack, R.A. (2004). Human midbrain sensitivity to cognitive feedback and uncertainty during classification learning. <em>Journal of Neurophysiology<\/em>, 92, 1144-1152.<\/a><\/p>\n<ul>\n<li>We investigated the mechanisms of probabilistic category learning in humans using functional magnetic resonance imaging, in order to examine the effects of feedback and uncertainty. Stimulus presentation and feedback activated brain regions consistent with the mesencephalic dopaminergic system, while the delay period did not. Midbrain activity was significantly different for negative vs. positive feedback (consistent with coding of the &#8216;prediction error&#8217;), and was reliably correlated with the degree of uncertainty, as well as with activity in the mesencephalic dopaminergic system .<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/shohamy_et_al.BRAIN_.2004.pdf\">Shohamy, D., Myers, C., Grossman, S., Sage, J., Gluck, M., Poldrack, R. (2004). Cortico-striatal contributions to feedback-based learning: Converging data from neuroimaging and neuropsychology. <em>Brain<\/em>, 127(4), 851-859.<\/a><\/p>\n<ul>\n<li>Feedback structure was found to be a critical variable in determining when Parkinson&#8217;s patients are impaired, or not, on a probabilistic classification task. Training patients to learn this task in a non-feedback manner (i.e. using observational learning) remediated a previously documented learning deficit. In a prior functional imaging study, healthy controls showed striatal activity during feedback-based learning, which was decreased when the task was learned without feedback. The present findings link prior neuroimaging and\u00a0 neurophysiological data with an understanding of the pattern of spared or impaired learning abilities in Parkinson&#8217;s patients, and provide converging evidence for the role of midbrain dopaminergic\u00a0 systems in feedback processing.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/shohamy_bn_2004.pdf\">Shohamy, D., Myers, C. E., Onlaor, S., &amp; Gluck, M. A. (2004). The role of the basal ganglia in category learning: How do patients with Parkinson&#8217;s disease learn? <em>Behavioral Neuroscience<\/em>, 118 (4), 676-686.<\/a><\/p>\n<ul>\n<li>Parkinson&#8217;s patients were impaired at learning a classification task, and this impairment was associated with Parkinson&#8217;s patients&#8217; failure to adopt the kind of complex learning strategies necessary for optimal performance on this task. This suggests that Parkinson&#8217;s patients may not only be slow to learn under certain conditions, but may also approach learning tasks in a qualitatively different manner.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Hopkins-Probabilistic-Learning-2004.pdf\">Hopkins, R. O, Myers, C. E, Shohamy, D., Grossman, S., &amp; Gluck, M. A. (2004). Impaired probabilistic category learning in hypoxic subjects with hippocampal damage. <em>Neuropsychologia<\/em>, 42, 524-535.<\/a><\/p>\n<ul>\n<li>Amnesic patients were found to be impaired at two forms of probabilistic category learning, both early and late in training, in contrast to previous reports of Knowlton, Squire, &amp; Gluck (1994) which reported essentially normal learning early in training.<\/li>\n<\/ul>\n<p><strong>2003<\/strong><\/p>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Myers2003Neuropsych.Merlin.pdf\">Myers, C. E., Shohamy, D., Gluck, M. A., Grossman, S., Onlaor, S., &amp; Kapur, N. (2003). Dissociating medial temporal and basal ganglia memory systems with a latent learning task. <em>Neuropsychologia<\/em>, 41, 1919-1928.<\/a><\/p>\n<ul>\n<li>A dissociation between medial temporal and basal ganglia damage is evident in a latent learning task in which prior exposure to cues, uncorrelated with each other, slows subsequent learning of an association in healthy normal controls.\u00a0 This effect was abolished in media temporal amnesia. In contrast, Parkinson\u0092s patients showed a reversal of the effect: exposed subjects learned faster than non-exposed subjects.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/tics-glucketal2003-4.pdf\">Gluck, M. A., Meeter, M., &amp; Myers, C. E. (2003). Computational models of the hippocampal region: Linking incremental learning and episodic memory. <em>Trends in Cognitive Science<\/em>, 7(6), 269-276.<\/a><\/p>\n<ul>\n<li>Two approaches to hippocampal models are reviewed: (1) models of the hippocampal region inincremental learning focusing on the development of new stimulus representations, and (2) models that emphasize the role of the hippocampal region in rapid storage and retrieval of episodic memories. It is suggested that both approaches are partially correct and might reflect the different functions of substructures of the hippocampal region.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Myers2003.JOCN_.Fish_.pdf\">Myers, C., Shohamy, D., Gluck, M., Grossman, S., Kluger, A., Ferris, S., Golomb, J., Schnirman, G., &amp; Schwartz, R. (2003). Dissociating hippocampal versus basal ganglia contributions to learning and transfer. <em>Journal of Cognitive Neuroscience<\/em>, 15(2), 185-193.<\/a><\/p>\n<ul>\n<li>As predicted by our prior computational models, we found a double dissociation between the associative learning deficits observed in patients with medial temporal damage\u00a0 (elderly with mild hipocampal atrophy) versus patients with basal ganglia dysfunction (mild Parkinson\u0092s disease). On an \u0093acquired equivalence\u0094 task based on animal conditioning paradigms, MT subjects were normal at initial learning, but impaired on a subsequent transfer generalization. In contrast, Parkinson\u0092s patients were slow to learn the initial task, but then transferred normally. These results suggest distinct contributions of the medial temporal lobe and basal ganglia in learning and memory.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/2002-allenCABN.pdf\">Allen, M.T., Padilla, Y., &amp; Gluck, M.A. (2003). Selective hippocampal lesions disrupt a novel cue effect but fail to eliminate blocking in rabbit eyeblink conditioning. <em>Cognitive, Affective, and Behavioral Neuroscience<\/em>, 2(4), 318-328.<\/a><\/p>\n<ul>\n<li>Selective ibotenic acid lesions of the hippocampus do not eliminate\u00a0 blocking in the rabbit eyeblink paradigm but do disrupt the CR decrement\u00a0 found in control animals on the first compound training trial, as predicted by Gluck &amp; Myers (1993). This suggests that blocking is a multiply determined phenomenon, whereby the cerebellum may mediate blocking through error-correction (Rescorla &amp; Wagner, 1972), while the hippocampus contributes to blocking through novelty detection (Mackintosh &amp; Turner, 1971).<\/li>\n<\/ul>\n<p><strong>2002<\/strong><\/p>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/AllenCABNLIRR-2002.pdf\">Allen, M.T., Chelius, L., &amp; Gluck, M.A. (2002) Selective entorhinal lesions and non-selective cortical-hippocampal region lesions, but not selective hippocampal lesions, disrupt learned irrelevance in rabbit eyeblink conditioning. <em>Cognitive Affective and Behavioral Neuroscience<\/em>, 2, 214-226. <\/a><\/p>\n<ul>\n<li>Rabbits with neurotoxic lesions of the entorhinal cortex as well as broad hippocampal-region lesions failed to show slowed learning following uncorrelated preexposures to the CS and US (i.e., learned irrelevance) and, thus, learned faster than control rabbits. In contrast, hippocampal-lesioned animals showed normal (slowed) learning. This confirms a novel prediction of Myers, Gluck, &amp; Granger\u0092s (1995) computational model of entorhinal function in associative learning, which expects that the entorhinal cortex is necessary for unsupervised compression of stimulus-stimulus redundancies.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Allen2002LatentInhib.pdf\">Allen, M., Chelius, L., Masand, V., Gluck, M., Myers, C., &amp; Schnirman, G., (2002). A comparison of latent inhibition and learned irrelevance pre-exposure effects in rabbit and human eyeblink conditioning. Integrative Physiological and Behavioral Science, 37(3). 188-214.<\/a><\/p>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/GluckLM2002.pdf\">Gluck, M. A., Shohamy, D., &amp; Myers, C. E. (2002). How do people solve the \u0093weather prediction\u0094 task?: Individual variability in strategies for probabilistic category learning. <em>Learning and Memory<\/em>, 9, 408-418.<\/a><\/p>\n<ul>\n<li>New experimental and theoretical analyses of our \u0093weather prediction\u0094 task indicate that there are at least three different strategies that describe how subjects learn this task: (1) an optimal multi-cue strategy, (2) a one-cue strategy, and (3) a singleton strategy. This variability in how subjects approach this task may have important implications for interpreting how various brain regions are involved in probabilistic category learning.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/tijseling.pdf\">Tijsseling, A. G. &amp; Gluck, M. A. (2002). Processing integral and separable dimensions in category learning: A connectionist perspective. <em>Connection Science<\/em>. 14(1). 1-48.<\/a><\/p>\n<ul>\n<li>A mechanistic connectionist explanation for variations in dimensional interactions provides a new perspective for the exploration of how similarities between stimuli are transformed from physical to psychological space when learning to identify, discriminate, and categorize them. Although currently limited to monochromatic stimuli, the model may serve as a starting point for characterizing the general properties of the human perceptual system that cause some pairs of dimensions to be treated integrally, and others separably, a classic problem in cognitive psychology.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Allen-IPBS-blocking.pdf\">Allen, T., Padilla, Y., Gluck, M. (2002). Blocking in Rabbit Eyeblink Conditioning Is Not Due to Learned Inattention: Indirect Support for an Error Correction Mechanism of Blocking. <em>Integrative Physiological &amp; Behavioral Science<\/em>. 254 &#8211; 264.<\/a><\/p>\n<p>Allen, M.T., Padilla, Y., &amp; Gluck, M.A. (2002). Blocking in rabbit eyeblink conditioning is not due to learned inattention. <em>Integrative Physiological and Behavioral Science<\/em>, 37(4), 254-264.<\/p>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Allen-Septal-Lesion-BN-paper.pdf\">Allen, M.T., Padilla, Y., &amp; Gluck, M.A. (2002). Ibotenic acid lesions of the medial septum retard delay eyeblink conditioning in rabbits (<em>Oryctolagus cuniculus<\/em>). <em>Behavioral Neuroscience<\/em>, 116(4), 733-738.<\/a><\/p>\n<ul>\n<li>Rabbits with selective medial septal lesions and rabbits receiving systemic scopolamine were significantly slower to condition than were intact and sham-lesioned rabbits. This demonstrates that the selective removal of the medial septum retards delay eyeblink conditioning in a manner similar to the disruption seen after systemic administration of scopolamine. Extending the earlier findings of Berry and Thompson\u00a0 (1979) these results support our previous computational models of septo-hippocampal function in learning (Myers et al., 1996).<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/ipbs2002.pdf\">Myers, C<strong>.<\/strong>, Bryant, D., DeLuca, J., &amp; Gluck, M. (2002). Dissociating basal forebrain and medial temporal amnesic syndromes: Insights from classical conditioning. <em>Integrative Physiological and Behavioral Science<\/em>, April-June 2002, 37(2), 85-102.<\/a><\/p>\n<ul>\n<li>A two-phase learning and transfer task provides a double dissociation between MT amnesics (spared initial learning but impaired transfer) and AcoA amnesics (slow initial learning but spared transfer), as implied by our previous computational models of septo-hippocampal interaction (Myers et al., 1996). These subtle but dissociable differences in the amnesic syndrome following damage to the MTL lobes vs. basal forebrain appear to be most salient in non &#8211; declarative tasks such as eyeblink classical conditioning and simple associative learning.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Myers2002.JGNP_.Choose.pdf\">Myers, C., Kluger, A., Golomb, J., Ferris, S., de Leon, M., Schnirman, G., &amp; Gluck, M. (2002).\u00a0 Hippocampal atrophy disrupts transfer generalization in non-demented elderly. <em>Journal of Geriatric Psychology and Neurology<\/em>, 15, 82-90.<\/a><\/p>\n<ul>\n<li>Nondemented elderly were trained on a series of concurrent visual discriminations, then tested for transfer when stimulus feature were recombined in new ways. As predicted by Gluck and Myers\u0092s (1993) corticohippocampal model, individuals with mild hippocampal atrophy were normal on the initial concurrent discrimination but showed significant impairments in transfer compared to non-atrophied subjects.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/RokersBN2002.pdf\">Rokers, B., Mercado, E., Allen, M. T., Myers, C. E. &amp; Gluck, M. A. (2002). A connectionist model of septohippocampal dynamics during conditioning: Closing the loop. <em>Behavioral Neuroscience<\/em>. 116(1), 48-62.<\/a><\/p>\n<ul>\n<li>Our previous neurocomputational model of septo-hippocampal function in conditioning was extended to incorporate hippocampal-septal feedback loops, modeled as dynamic variations in hippocampal learning rates. The model successfully accounts for changes in behavior and septo-hippocampal activity during various conditioning paradigms. The model provides a computational, neurally-based synthesis of prior learning theories that explain change s in medial septal activity based on the novelty of stimulus events.<\/li>\n<\/ul>\n<p><strong>2001<\/strong><\/p>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/al-2.pdf\">Allen, M. T., Myers, C., &amp; Gluck, M. (2001).\u00a0 Parallel neural systems for classical conditioning:\u00a0 Support from computational modeling. <em>Integrative Physiological and Behavioral Science<\/em>, 36(1), 36-61.<\/a><\/p>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/hr-1.pdf\"> Ordu\u00f1a, I., Mercado, E., III, Gluck, M.A., &amp; Merzenich, M.M. (2001). Spectrotemporal sensitivities in rat auditory cortical neurons. <em>Hearing Research<\/em>, 160, 47-57.<br \/>\n<\/a><br \/>\n<a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/neurorep.pdf\"> Mercado, E., III, Bao, S., Ordu\u00f1a, I., Gluck, M.A., &amp; Merzenich, M.M. (2001). Basal forebrain stimulation changes cortical sensitivities to complex sound. <em>Neuroreport<\/em>, 12, 2283-2288. <\/a><\/p>\n<ul>\n<li>The selectivity and sensitivity of auditory cortical neuronal responses were altered in adult rats by repeated presentation of complex sounds that were paired with basal forebrain stimulation. The auditory cortical region that was responsive to complex sounds was two to five times greater in paired-stimulation rats than in na\u00efve rats. These findings demonstrate that feature selectivity within the auditory cortex can be flexibly altered in adult mammals through appropriate intensive training.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/poldrack.pdf\">Poldrack, R. A., Clark, J., Pare-Blagoev, E. J., Shohamy, D., Creso-Moyano, J., Myers, C. E. &amp; Gluck, M. A. (2001). Interactive memory systems in the brain. <em>Nature<\/em>. 414 (Nov 29). 546-550.<\/a><\/p>\n<ul>\n<li>FMRI studies of probabilistic category learning showed that the medial temporal lobe and basal ganglia were differentially engaged across subjects depending on whether the training involved error-correcting feedback to categorization responses or the passive observation of stimulus-category pairs. Event-related FMRI showed rapid modulation of activity in the MTL early in learning which was negatively correlated across individuals with basal ganglia activity. As predicted by our previous computational model of cortico-hippocampal activity (Gluck &amp; Myers, 1993), hippocampal activity rapidly declined with training.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/al-1.pdf\">Gluck, M.A., Allen, M.T., Myers, C.E., &amp; Thompson, R.F. (2001) Cerebellar substrates for error-correction in motor-reflex conditioning. <em>Neurobiology of Learning and Memory<\/em>, 76, 314-341.<\/a><\/p>\n<ul>\n<li>We evaluate a mapping of Rescorla and Wagner&#8217;s (1972) behavioral model of classical conditioning onto the cerebellar substrates for motor-reflex learning. Several novel implications of this cerebellar error-correcting model are described, including a recent empirical study by Kim et al. (1998). They verified our prediction that suppressing the putative error-correction pathway should interfere with Kamin&#8217;s (1969) blocking effect, a behavioral manifestation of error-correction learning. Overall, the model leads to a comprehensive view of the neural substrates of conditioning in which this real-time circuit-level model of the cerebellum can be viewed as a generalization of the long-term memory module of Gluck and Myers (1993) trial-level theory of cerebellar-hippocampal interaction in classical motor-reflex conditioning.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/2001-mercadoCABN.pdf\">Mercado, E., III, Myers, C., Gluck, M. (2001).\u00a0 A computational model of mechanisms controlling experience-dependent reorganization of representational maps in auditory cortex. <em>Cognitive, Affective and Behavioral Neuroscience<\/em>, 1(1), 37-55. <\/a><\/p>\n<ul>\n<li>Simulations performed with a biologically-based neural network model of auditory cortical processing are used to investigate the possible effects of basal forebrain modulation on map reorganization in auditory cortex.\u00a0 The model successfully accounts for experimentally observed effects of pairing basal forebrain stimulation with the presentation of a\u00a0 single tone, but not of two tones, suggesting that auditory cortical plasticity is constrained in ways not accounted\u00a0 for by current theories.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/bn2001.pdf\"><br \/>\nMyers, C. E., DeLuca, J., Schultheis, M. T., Schnirman, G. M., Ermita, B. R., Diamond, B., Warren, S. G., &amp; Gluck, M. A. (2001). Impaired delay eyeblink classical conditioning in individuals with anterograde amnesia resulting from anterior communicating artery aneurysm rupture.\u00a0 <em>Behavioral Neuroscience<\/em>, 115(3), 560-570.<\/a><\/p>\n<ul>\n<li>Anterior communicating artery (ACoA) aneurysm rupture can lead to an anterograde amnesia syndrome similar to that observed after damage to the hippocampus and medial temporal lobes (MT). It is currently believed that ACoA amnesia results from basal forebrain damage that disrupts hippocampal processing without direct hippocampal damage. Converging evidence from animal studies and computational modeling suggests that qualitative differences may exist in the pattern of memory impairment after basal forebrain or MT damage. For example, animals with basal forebrain but not hippocampal damage are impaired at delay eyeblink classical conditioning (EBCC). In this study, individuals with ACoA amnesia were shown to be impaired at delay EBCC compared with matched controls; this contrasts with the spared delay EBCC previously observed in MT amnesia. This finding suggests the beginning of a possible dissociation between the memory impairments in MT versus ACoA amnesia.<\/li>\n<\/ul>\n<p><strong>2000<\/strong><\/p>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/2000_Myers_Psychobio.pdf\">Myers, C. E., Hopkins, R. O., Kesner, R. P, Monti, L., &amp; Gluck, M. A. (2000). Conditional spatial discrimination in humans with hypoxic brain injury. <em>Psychobiology<\/em>, 28(3), 275-282.<\/a><\/p>\n<ul>\n<li>Hypoxic subjects with significant hippocampal atrophy were able to acquire and reverse a discrimination, although they were slower relative to matched controls. They also showed a tendency to perseverate after reward contingencies were reversed.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/MyersQJEP2000.pdf\">Myers, C., Oliver, L., Ermita, B., Warren, S., &amp; Gluck, M. (2000). Stimulus exposure effects in human associative learning. <em>Quarterly Journal of Experimental Psychology B:\u00a0 Comparative and Physiological Psychology<\/em>, 53B, 173-187.<\/a><\/p>\n<ul>\n<li>Learning that one cue (CS) predicts a second, salient cue (US) can often be slowed by prior exposure to one or both stimuli. In animals, CS\u00b1US learning is more strongly retarded following uncorrelated exposure to both CS and US than following exposure to the US alone. In this paper we present several studies showing a similar effect in humans, using a computer-based task. Experiments 1 and 2 used a between-groups design and demonstrated a strong CS\/US exposure effect, whether or not the US was signaled by a neutral cue during exposure. Experiment 3 demonstrated similar effects using a within-subjects design. Overall, these results are consistent with several theoretical interpretations and suggest that uncorrelated CS\/US exposure leads to a robust retardation of subsequent CS\u00b1US learning in humans.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Allen-BN-2000.pdf\">Shohamy, D., Allen, M. T., &amp; Gluck, M. A. (2000). Dissociating entorhinal and hippocampal involvement in latent inhibition. <em>Behavioral Neuroscience<\/em>, 114(5), 867-874.<\/a><\/p>\n<ul>\n<li>Rabbits with neurotoxic lesions of the entorhinal cortex failed to show slowed learning following preexposure to a cue (i.e., latent inhibition) and, thus, learned faster than control rabbits. In contrast, hippocampal-lesioned animals showed normal (slowed) learning. This confirms a novel prediction of Myers, Gluck, &amp; Granger\u0092s (1995) computational model of entorhinal function in associative learning.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/ncomp.pdf\">Mercado, E., III, Myers, C. E. &amp; Gluck, M.A. (2000). Modeling auditory cortical processing as an adaptive chirplet transform. <em>Neurocomputing<\/em>, 32-33(1-4), 913-919.<br \/>\n<\/a><\/p>\n<p>Rokers, B., Myers, C.E, &amp; Gluck, M.A. (2000). A dynamic model of learning in the septo-hippocampal system. <em>Neurocomputing<\/em>. 32-33(1-4), 501-507.<\/p>\n<ul>\n<li>Gluck and Myers (Hippocampus (1993) 491-516) modeled the hippocampus as an auto-encoder; Myers et al. (Neurobiol. Learning Memory 66 (1996) 51-66) argued that the cholinergic input from medial septum modulates learning rate in this auto-encoder. Neurophysiological evidence suggests the hippocampus self-regulates septal acetylcholine release in response to novel stimuli (Hasselmo and Schnell, J. Neurosci. 14 (1994) 3898-3914). We have extended our earlier model of septohippocampal modulation to include such a feedback loop. The resulting dynamic model learns faster and better than the earlier version on phenomena such as blocking and shift reversal. It can also be applied to data regarding the effects of the anticholinergic drug scopolamine.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/neuralcomp.pdf\">Japkowicz, N., Hanson, S.J., &amp; Gluck, M.A., (2000). Nonlinear autoassociation is not equivalent to PCA. <em>Neural Computation<\/em>, 12(3), 531-545.<\/a><\/p>\n<ul>\n<li>A common misperception within the neural network community is that even with nonlinearities in their hidden layer, autoassociators trained with backpropagation are equivalent to linear methods such as principal component analysis (PCA). Our purpose is to demonstrate that nonlinear autoassociators actually behave differently from linear methods and that they can outperform these methods when used for latent extraction, projection, and classification. While linear autoassociators emulate PCA, and thus exhibit a flat or unimodal reconstruction error surface, autoassociators with nonlinearities in their hidden layer learn domains by building error reconstruction surfaces that, depending on the task, contain multiple local valleys. This interpolation bias allows nonlinear autoassociators to represent appropriate classifications of nonlinear multimodal domains, in contrast to linear autoassociators, which are inappropriate for such tasks. In fact, autoassociators with hidden unit nonlinearities can be shown to perform nonlinear classification and nonlinear recognition.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/MyersNpsych2000.pdf\">Myers, C., McGlinchey-Berroth, R., Warren, S., Monti, L., Brawn, C. M., &amp; Gluck, M. A. (2000). Latent learning in medial temporal amnesia: Evidence for disrupted representational but preserved attentional processes. <em>Neuropsychology<\/em>, 14(1), 3-15.<\/a><\/p>\n<ul>\n<li>Individuals with medial temporal lobe amnesia were impaired at representational processing in a latent learning \u00a0 paradigm, as predicted by the Gluck &amp; Myers (1993) model of hippocampal function. In contrast, these subjects were not impaired at an attentional task.<\/li>\n<\/ul>\n<p><strong>1995-1999<\/strong><\/p>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/1998_Myers_Psychobio.pdf\">Myers, C., Ermita, B., Hasselmo, M. &amp; Gluck, M. (1998).\u00a0 Further implications of a computational model of septohippocampal cholinergic modulation in eyeblink conditioning.\u00a0 <em>Psychobiology<\/em>, 26(1), 1-20.<\/a><\/p>\n<ul>\n<li>Builds upon our previous work (Myers et al., 1996) in which we showed that Gluck and Myers\u0092s (1993) corticohippocampal model could be extended to incorporate Hasselmo and Schnell\u0092s (1994) hypothesis that septohippocampal cholinergic processes regulate the amount of information storage in the hippocampus. Here we show that the model accounts for numerous additional results from the eyeblink conditioning literature, and is consistent with data concerning localized scopalamine injections to the medial septum, the lateral septum, and the hippocampus.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/gluck97memory.pdf\">Gluck, M. A., Ermita, B. R., Oliver, L. M., &amp; Myers, C. E. (1997).\u00a0 Extending models of hippocampal function in animal conditioning to human amnesia. <em>Memory<\/em>, 5 (1\/2), 179-212.<\/a><\/p>\n<ul>\n<li>Although most analyses of human amnesia have focused on the loss of explicit declarative and episodic memories following hippocampal-region damage, considerable insights into amnesia can also be realized by studying hippocampal function in simple procedural, or habit-based, associative learning tasks. Reviews several recent papers which argue that the hippocampal region plays a critical role in the formation of new stimulus representations during\u00a0 learning, (Gluck &amp; Myers, 1993, 1995; Myers &amp; Gluck, 1995; Myers, Gluck, &amp; Granger, 1995). Summarizes our recent experimental work with amnesic patients using two behavioral paradigms: eyeblink conditioning and probabilistic category learning.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/gluckmyers97.pdf\">Gluck, M. A., &amp; Myers, C. E. (1997). Psychobiological models of hippocampal function in learning and memory. <em>Annual Review of Psychology<\/em>. 48. 481-514.<\/a><\/p>\n<ul>\n<li>We review current computational models of hippocampal function in learning and memory, concentrating on those that make strongest contact with psychological issues and behavioral data.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/gluckmyers96.pdf\">Gluck, M. A., &amp; Myers, C. E. (1996). Integrating behavioral and physiological models of hippocampal function. <em>Hippocampus (Special Issue on Computational Models of Hippocampal Function in Memory, M. Gluck, Guest Editor)<\/em>. 6(6). 643-653.<\/a><\/p>\n<ul>\n<li>In recent modeling of hippocampal function, we have attempted to integrate formal behavioral analyses of classical conditioning with psychobiological data on brain lesions (Gluck and Myers [1993] Hippocampus 3:491-516; Myers and Gluck [1994] Behav Neurosci 108(5):835-847). Based on comparative behavioral analyses, we have argued that animals with hippocampal region damage are unable to alter stimulus similarity based on experience. While hippocampal-damaged animals can still learn whether to respond to an individual stimulus, they are notably impaired at many tasks involving learning relationships between stimuli-especially in the absence of explicit reinforcement. These analyses lead to a computational theory which identifies two representational recoding processes- predictive differentiation and redundancy compression-which alter stimulus similarity relationships in intact animals but are dependent on intact hippocampal region processing. More recent, and ongoing, modeling aims to broaden this model of hippocampal region function in classical conditioning, with an emphasis on physiological and anatomical constraints, including the role of the fornix and subcortical modulation, preprocessing in sensory cortices, and localization of the proposed representational functions within more precisely identified hippocampal region substrates (Myers et al. [1995] Psychology 23(2):116-138; Myers and Gluck [1996] Behav Neurosci; Myers et al. [1996] Neurobiol Learning Memory). Working to bridge between behavioral and physiological levels of analysis, we ultimately hope to develop a more complete understanding of hippocampal region function in memory across a wider range of behavioral paradigms, elucidating how this functionality emerges from underlying physiological and anatomical substrates.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Computational-Models-of-Hippocampal-Function-in-Memory-Introduction-to-Special-Issue.pdf\">Gluck, M. A. (1996). Computational models of hippocampal function in memory: Introduction to special issue. Hippocampus (Special Issue on Computational Models of Hippocampal Function in Memory, M. Gluck, Guest Editor). 6(6). 565-566. <\/a><\/p>\n<p>Gluck, M. A., Oliver, L. M., &amp; Myers, C. E. (1996). Late-training amnesic deficits in probabilistic category learning: A neurocomputational analysis.<em>Learning and Memory<\/em>, 3, 326-340.<\/p>\n<ul>\n<li>Building upon earlier behavioral models of animal and human learning, we explore how a psychobiological model of animal conditioning can be applied to amnesic category learning. We show that the late-training deficit found in Knowlton, Squire, and Gluck\u0092s (1994) study of amnesic category learning can be understood as a natural consequence of Gluck and Myers (1993) theory of hippocampal-region function, a theory which has heretofore been applied only to studies of animal learning.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/myers96BehNeuro.pdf\">Myers, C. E., &amp; Gluck, M. A.\u00a0 (1996). Cortico-hippocampal representations in simultaneous odor discrimination: A computational interpretation of Eichenbaum, Mathews, and Cohen (1989). <em>Behavioral Neuroscience<\/em>, 110(4), 1-22.<\/a><\/p>\n<ul>\n<li>A model of hippocampal-region function in Pavlovian conditioning (Gluck &amp; Myers, 1993; Myers &amp; Gluck, 1994) is generalized to provide a computational interpretation of Eichenbaum and colleagues&#8217; simultaneous odor discrimination studies (Eichenbaum, Fagan, Mathews &amp; Cohen, 1988; Eichenbaum, Mathews &amp; Cohen, 1989). This modeling makes two central points. First, Eichenbaum et al.\u0092s data on forced-choice odor discrimination can be understood as a reflecting the same underlying representational recodings previously invoked to describe behaviors seen in studies of classical eyeblink conditioning. Second, the computational mechanisms which underlie performance in the intact and lesioned model are consistent with the more qualitative interpretations suggested by Eichenbaum et al. (1988). 1989) to explain the intact and lesioned rat data.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/myers98nblm.pdf\">Myers, C. E., Ermita, B., Harris, K., Hasselmo, M., Solomon, P., &amp; Gluck, M. A. (1996).\u00a0 A computational model of the effects of septohippocampal disruption on classical eyeblink conditioning. <em>Neurobiology of Learning and Memory<\/em>, 66, 51-66.<\/a><\/p>\n<ul>\n<li>A previous neurocomputational model of cortico-hippocampal interaction (Gluck &amp; Myers, 1993) provides a framework for examing the behavioral effects of septohippocampal modulation during classical conditioning. According to Hasselmo\u0092s (1995) model of septohippocampal function, anticholinergic drugs such as scopolomine should disrupt learning by selectively reducing the hippocampus\u0092s ability to store new information.\u00a0 Hasselmo\u0092s model can be approximated within the Gluck-Myers model by a manipulation of hippocampal learning rates, and with this manipulation, we can account for the effects of scopolamine on classical conditioning in humans and rabbits. The model further predicts that while cholinergic agonists (such as Tacrine) may improve learning in subjects with artificially depressed brain acetycholine levels, there may be little memory improvement in normal subjects from such cholinergic therapy.<\/li>\n<\/ul>\n<p>Gabrieli, J.D.E., McGlinchey-Berroth, R., Carrillo, M.C., Gluck, M.A., Cermak, L.S., &amp; Disterhoft, J.F. (1995). Intact delay-eyeblink classical conditioning in amnesia. <em>Behavioral Neuroscience<\/em>, 109(5), 819-827.<\/p>\n<ul>\n<li>The status of classical conditioning in human amnesia was exampined by comparing conditioning of the eyeblink response to a tone conditioned stimulus in the delay paradigm between 7 amnesic and 7 age- and education-matched normal control subjects. Amnesic patients exhibited normal baseline performance in pseudoconditioning, and normal acquisition and extinction of conditioned responses in terms of the number, latency, and magnitude of eyeblinks. These results indicate that in humans, as in rabbits, brain structures critical for declarative memory are not essential for the acquisition of elementary CS-US associations.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/1995_Myers_Psychobio.pdf\">Myers, C. E., Gluck, M. A., &amp; Granger R. (1995). Dissociation of hippocampal and entorhinal function in associative Learning: A computational approach. <em>Psychobiology<\/em>. 23(2). 116-138.<\/a><\/p>\n<ul>\n<li>Proposes that Gluck &amp; Myers (1993) computational theory of hippocampal-region function may be subdivided and localized via analysis of the biological substrate and its emergent operation. In particular, the parahippocampal region is conjectured to constructs new stimulus representations which compress redundant information.\u00a0 To the extent that parahippocampal-region function can survive damage strictly limited to the hippocampus proper, this hypothesis has implications for interpreting the behavioral consequences of selective lesions which spare the parahippocampal region.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/gluckmyers95.pdf\">Gluck, M. A. &amp; Myers, C. E., (1995). Representation and association in memory: A neurocomputational view of hippocampal function. <em>Current Directions in Psychological Science<\/em>, 4(1). 23-29.<\/a><\/p>\n<ul>\n<li>An overview of our current computational theories of hippocampal function in memory, with special reference to similarities and differences to related qualitative theories of H. Eichenbaum. To appear side-by-side with a similar article by Eichenbaum.<\/li>\n<\/ul>\n<p><strong>1991-1994<\/strong><\/p>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Probabilistic-Classification-Learning-in-Amnesia.pdf\">Knowlton, B. J.,\u00a0 Squire, L. R. , &amp; Gluck, M. A. (1994). Probabilistic category learning in amnesia. <em>Learning and Memory<\/em>. 1, 106-120.<\/a><\/p>\n<ul>\n<li>Amnesic patients and control subjects were trained on two category learning tasks in which each of four cues was probabilistically associated with one of two outcomes. Both groups exhibited significant and similar learning curves during early training trials, but the control subjects outperformed the amnesics on later trials. These findings are relevant to recent connectionist theories of category learning (Gluck &amp; Bower, 1988), and raise the possibility that the learning of cue-outcome associations is fundamentally similar to conditioning as studied in experimental animals.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/myers94behneur.pdf\">Myers, C. E. &amp; Gluck, M. A. (1994). Context, conditioning and hippocampal re-representation. <em>Behavioral Neuroscience<\/em>, 108(5), 835-847.<\/a><\/p>\n<ul>\n<li>A previous computational account of hippocampal-region function in associative learning (Gluck &amp; Myers, 1993) has emergent implications which accurately describe the role of the hippocampal region in contextual processing.\u00a0 This account unifies two seemingly conflicting views of contextual processing; it accords contextual cues no special representational status yet it still allows context to stand in a superordinate relationship to the cues it contains. The model makes several novel predictions and provides a framework for understanding the conditions under which a learned response either is, or is not, decremented in a novel context as well as explaining data suggesting that hippocampal lesion reduces contextual sensitivity.<\/li>\n<\/ul>\n<p>Gluck, M., Myers, C.E., &amp; Goebel, J. (1994). A computational perspective on dissociating hippocampal and entorhinal function (Response to Eichenbaum, et al.).<em>Behavioral and Brain Sciences<\/em>, 17, 478-479.<\/p>\n<p>Shanks, D. R. &amp; Gluck, M. A. (1994). Tests of an adaptive network model for the identification, categorization, and recognition of continuous-dimension stimuli. <em>Connection Science<\/em>, 6(1), 59-89.<\/p>\n<ul>\n<li>Describes a new adaptive network model, the Consequential Region Model, for the identification and categorization of stimuli varying on multiple, continuous dimensions.\u00a0 The models provides excellent fits to identification and categorization data. These results illustrate how an associative network can show appropriate sensitivity to inter-item similarities among training exemplars as an emergent property of its scheme for representing stimuli.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/1994_rmn-mag-tjp-scm-pg_mc_Comparing.pdf\">Nosofsky, R. M., Gluck, M. A., Palmeri, T. J., McKinley, S. C., &amp; Glauthier, P. (1994). Comparing models of rule-based classification learning: A replication and extension of Shepard, Hovland, and Jenkins. <em>Memory Cognition<\/em>, 22, 352-369.<\/a><\/p>\n<ul>\n<li>An experimental study of task difficulty for learning six fundamental types of rule-based categorization problems is used to compare four current computational models of classification learning. The results of these new category learning experiments suggest the need to incorporate some form of selective attention to dimensions in category-learning models based on stimulus generalization and cue conditioning.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/gluck.edu\/pdf\/1993_Gluck_Hippoc.pdf\">Gluck, M. A. &amp; Myers, C. (1993). Hippocampal mediation of stimulus representation: A computational theory. <em>Hippocampus<\/em>, 3(4): 491-516.<\/a><\/p>\n<ul>\n<li>Proposes a computational theory of the hippocampal-region&#8217;s function in mediating stimulus representations. The theory assumes that the hippocampal region develops new stimulus representations which enhance the discriminability of differentially predictive cues while compressing the representation of redundant cues. The theory makes several novel predictions regarding the effects of hippocampal lesionsand suggests that a profitable direction for future empirical and theoretical research will be the study of learning tasks in which both intact and lesioned animals exhibit similar initial learning behaviors, but differ on subsequent transfer and generalization tasks.<\/li>\n<\/ul>\n<p>Gluck, M. A., &amp; Granger, R. (1993). Computational models of the neural bases of learning and memory. <em>Annual Review of Neuroscience<\/em>, 16, 667-706.<\/p>\n<ul>\n<li>Reviews current efforts to develop computational models of the neural bases of learning and memory, with a focus on the behavioral implications of network-level characterizations of synaptic change in three anatomical regions: olfactory (piriform) cortex, cerebellum, and the hippocampal formation.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Corter-Gluck-1992.pdf\">Corter, J. E., &amp; Gluck, M. A. (1992) Explaining basic categories: Feature predictability and information. <em>Psychological Bulletin<\/em>, 111(2), 291-303. <\/a><\/p>\n<ul>\n<li>A quantitative measure is derived for the utility of a category that successfully predicts the basic level in experiments on both artifificial and\u00a0 natural category hierarchies.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Stimulus-Generalization-and-Representation-in-Adaptive-Network-models-of-Category-Learning.pdf\">Gluck, M. A. (1991). Stimulus generalization and representation in adaptive network models of category learning. <em>Psychological Science<\/em>, 2(1), 50-55.<\/a><\/p>\n<ul>\n<li>It is shown here how an approximate exponential generalization gradient emerges from stimulus representation assumptions isomorphic to a special case of Shepard\u0092s theory of stimulus generalization within Gluck and Bower\u0092s (1988) configural-cue network model of human learning. Thus, the network model can be viewed as a combination of Shepard\u0092s theory and an associative learning rule derived from the Rescorla-Wagner theory of classical conditioning.<\/li>\n<\/ul>\n<p><strong>1984-1990<\/strong><\/p>\n<p>Gluck, M. A. &amp; Bower, G. H. (1990). Component and pattern information in adaptive networks <em>Journal of Experimental Psychology: General<\/em>, 119(1), 105-109.<\/p>\n<p>Donegan, N. H., Gluck, M. A., &amp; Thompson, R. F. (1989).\u00a0 Integrating behavioral and biological models of classical conditioning.\u00a0 <em>Psychology of Learning and Motivation (Volume 22)<\/em>. New York: Academic Press, 109-156.<\/p>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/GluckBower1988.pdf\">Gluck, M. A. &amp; Bower, G. H. (1988).\u00a0 From conditioning to category learning: An adaptive network model.\u00a0 <em>Journal of Experimental Psychology: General<\/em>, 117(3), 227-247.<\/a><\/p>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Evaluating_Adaptive_Network_Model-copy.pdf\">Gluck, M. A., &amp; Bower, G. H. (1988). Evaluating an adaptive network model of human learning. <em>Journal of Memory and Language<\/em>, 27, 166-195. <\/a><\/p>\n<p>Gluck, M. A., Parker, D. B., &amp; Reifsnider, E. (1988).\u00a0 Some biological implications of a differential-Hebbian learning rule. <em>Psychobiology<\/em>, 16(3), 298-302.<\/p>\n<p>Gluck, M. A. &amp; Thompson, R. F. (1987).\u00a0 Modeling the neural substrates of associative learning and memory: A computational approach, <em>Psychological Review<\/em>, 94(2), 176-191.<\/p>\n<p><a href=\"https:\/\/gluck.edu\/pdf\/Jolicoeur_Gluck_Kosslyn (1984).pdf\">Jolicoeur, P., Gluck, M. A., &amp; Kosslyn, S. M. (1984).\u00a0 Pictures and names: Making the connection. <em>Cognitive Psychology<\/em>, 16, 243-275.<\/a><\/p>\n<p><strong>BOOK CHAPTERS<\/strong><\/p>\n<p>Orduna, I. &amp; Gluck, M.A. (2001) The Neural Basis of Blocking. <em>International Encyclopedia of the Social and Behavioral Sciences<\/em>. 1260-1262.<\/p>\n<ul>\n<li>Blocking is a conditioning paradigm, first described by Leon Kamin (1969), in which previous conditioning to a stimulus which a second stimulus can be conditioned during compound conditioning. This effect reflects the fact that temporal contiguity does not suffice for associations to occur between events, and reveals the animal&#8217;s active role in the selection and processing of stimuli during associative learning. No single neural mechanism has yet been identified as a neural basis for blocking, but compelling evidence for an underlying negative feedback mechanism has been provided. The hippocampus, as a structure claimed to be relevant for the selection of stimuli, has also been shown to play a role in blocking, although those results are somewhat obscured by lack of specificity in the lesions. Future research in the neural basis of blocking should include further exploration of negative feedback mechanisms as well as more detailed studies regarding the role of the hippocampus and related structures in this phenomenon.<\/li>\n<\/ul>\n<p>Rokers, B., Myers, C., &amp; Gluck, M. (2001). &#8220;A dynamic model of learning in the septohippocampal system.&#8221; In J. Bower (Ed.), <em>Computational Neuroscience: Trends in Research 1999<\/em>, New York: Plenum Press.<\/p>\n<p>Gluck, M.A., Allen, M.T., &amp; Myers, C.E. (2001). Medial Septal Modulation of Conditioning: From Two-Stage Learning Theories to Connectionist Models. In J. E. Steinmetz, M. A. Gluck, &amp; P. F. Solomon (Eds.), <em>Model Systems of Associative Learning: A Festschrift for Richard F. Thompson<\/em> (pp. 295-316). Mahwah, NJ: Lawrence Erlbaum Associates.<\/p>\n<p>Allen, M.T., Myers, C.E., &amp; Gluck, M.A. (2000). Neural network approaches to eyeblink classical conditioning. In D. S. Woodruff-Pak &amp; J. E. Steinmetz (Eds.), <em>Eyeblink Classical Conditioning: Animal<\/em> (pp. 229-255). Kluwer Academic Publishers.<\/p>\n<p>Cahill, L., Gluck, M., Hasselmo, M., Keil, F., Martin, A., McGaugh, J., Murre, J., Myers, C., Petrides, M., Roozendaal, B., Schacter, D., Simons, D., Smith, W. &amp; Williams, C. (1998). Learning and memory: Systems analysis. In M. Zigmond, F. Bloom, S. Landis, J. Roberts, L. Squire (Eds.), <em>Fundamental Neuroscience<\/em>, New York: Academic Press.<\/p>\n<p>Gluck, M., &amp; Myers, C. (1998). Psychobiological models of hippocampal function in learning and memory. In J. Martinez &amp; R. Kesner (Eds.), <em>Neurobiology of Learning and Memory<\/em> (pp. 417-448). San Diego, CA: Academic Press.<\/p>\n<p>Zackheim, J., Myers, C., &amp; Gluck, M. (1998). A temporally sensitive recurrent network model of occasion setting. In N. Schmajuk &amp; P. Holland (Eds.), <em>Occasion Setting: Associative Learning and Cognition in Animals<\/em> (pp. 319-342). Washington, DC: American Psychological Association.<\/p>\n<p>Ermita, B.R., Myers, C.E., Hasselmo, M., &amp; Gluck, M.A. (1997). Septohippocampal cholinergic modulation in classical conditioning. In J. M. Bower (Ed.), <em>Computational Neuroscience<\/em> (pp. 631-639). New York: Plenum Press.<\/p>\n<p>Gluck, M.A., &amp; Myers, C.E. (1997). Adaptive stimulus representations in a computational model of cortico-hippocampal function. In M. Baudry &amp; J. Davis (Eds.), <em>Long Term Potentiation: Volume III<\/em> (pp. 325-350). Cambridge, MA: MIT Press.<\/p>\n<p>Gluck, M.A., &amp; Myers, C.E. (1997). A neural-network approach to adaptive similarity and stimulus representations in cortico-hippocampal function. In J. Donahoe &amp; V. Dorsel (Eds.), <em>Neural-network models of cognition: Biobehavioral foundations<\/em>. Amsterdam, Netherlands: Elsevier Science Press.<\/p>\n<p>Gluck, M.A. &amp; Myers, C.E. (1994). A neurocomputational theory of hippocampal function in stimulus representation and learning. In S. Zournetzer, J. Davis, T. McKenna, &amp; C. Lau (Eds.), <em>An Introduction to Neural and Electronic Networks (Second Edition)<\/em> (pp. 77-90).<\/p>\n<p>Gluck, M.A., Myers, C.E., &amp; Thompson, R.F. (1994). A computational model of the cerebellum and motor-reflex learning. In S. Zournetzer, J. Davis, T. McKenna, &amp; C. Lau (Eds.). <em>An Introduction to Neural and Electronic Networks (Second Edition)<\/em> (pp. 91-80).<\/p>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Ch-9_Stimulus-Sampling-and-Distributed-Representations.pdf\">Gluck, M. A. (1992). Stimulus sampling and distributed representations in adaptive network theories of learning. In A. Healy, S. Kosslyn, &amp; R. Shiffrin (Eds.), <em>From Learning Theory to Connectionist Theory: Essays in Honor of William K. Estes<\/em> (pp. 169-199). New Jersey: Lawrence Erlbaum Associates.<\/a><\/p>\n<ul>\n<li>Current &#8220;connectionist&#8221; adaptive network theories of learning are reviewed and contrasted with Estes&#8217; Stimulus Sampling Theory from the late 1950s. This earlier theory suggests a motivation and mechanism for extending current network theories to include distributed representations.<\/li>\n<\/ul>\n<p>Bartha, G. T., Thompson, R. F., &amp; Gluck M. A. (1991). Sensorimotor learning and the cerebellum. In M.A. Arbib &amp; J.-P. Ewert (Eds.), Visual Structures and Integrated Functions, <em>Springer Research Notes in Neural Computing<\/em> (pp. 381-196). Berlin: Springer-Verlag.<\/p>\n<ul>\n<li>An earlier model of response topography in eyeblink conditioning is extended to be more physiologically realistic in its circuit connectivity, plasticity rule, and stimulus representation.<\/li>\n<\/ul>\n<p>Thompson, R.F. &amp; Gluck, M.A. (1991). Brain substrates of basic associative learning and memory. In H. J. Weingartner &amp; R. F. Lister (Eds.), <em>Cognitive Neuroscience<\/em> (pp. 24-45). New York: Oxford University Press.<\/p>\n<p>Gluck, M.A., Reifsnider, E.S., &amp; Thompson, R.F. (1990). Adaptive Signal Processing and the Cerebellum: Models of Classical Conditioning and VOR Adaptation. In Gluck, M.A. &amp; Rumelhart, D. E. (Eds.). <em>Neuroscience and Connectionist Theory<\/em> (pp. 131-185). Hillsdale, NJ: Lawrence Erlbaum Associates.<\/p>\n<p>Thompson, R.F., &amp; Gluck, M.A. (1989). A biological neural-network analysis of learning and memory. In S. Zournetzer, J. Davis, &amp; C. Lau (Eds.) <em>An Introduction to Neural and Electronic Networks<\/em> (pp. 91-107). New York: Academic Press.<\/p>\n<p><strong>BOOKS\u00a0 AND GUEST-EDITED JOURNAL ISSUES<\/strong><\/p>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Preface.Gluck_Bowerbook2007.pdf\">Memory and Mind: A Festschrift for Gordon H. Bower<\/a><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Preface.Gluck_Bowerbook2007.pdf\">, (M. A. Gluck, J. R. Anderson, &amp; S. M. Kosslyn, Eds.) , 2007<\/a><\/p>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/CH18_Gluck_Bowerbook2007-1.pdf\">Read Chapter 18<\/a> Gluck, M. A., Mercado, E., &amp; Myers, C. E. (2007, Expected). <em>Learning and Memory: From Brain to Behavior.<\/em> New York: Worth.<\/p>\n<p><a href=\"http:\/\/mitpress.mit.edu\/catalog\/item\/default.asp?ttype=2&amp;tid=8565\">Gluck, M. A. &amp; Myers, C. E. (2001). <em>Gateway to Memory: An Introduction to Neural Network Models of the Hippocampus and Learning.<\/em> Cambridge, MA: MIT Press.<\/a><\/p>\n<p>Steinmetz, J., Gluck, M., &amp; Solomon, P. (2001).\u00a0 <em>Model Systems and the Neurobiology of Associative Learning: A Festshrift for Richard F. Thompson, <\/em>Mahwah, NJ: Lawrence Erlbaum Associates.<\/p>\n<p>Gluck, M. A., <em>Guest<\/em> <em>Editor,<\/em> (1996), <em>Hippocampal Computation and Memory<\/em> (Special issue of Hippocampus). 6(6). J. Wiley &amp; Sons.<\/p>\n<p><a href=\"http:\/\/www.erlbaum.com\/ME2\/dirmod.asp?sid=28807ECF50FE49F0837125BE640E681F&amp;nm=&amp;type=eCommerce&amp;mod=CommerceProductCatalog&amp;mid=CD22EA0F118949C09A932248C040F650&amp;tier=3&amp;id=109A2E775AF84C45B3386FF260E82BD6&amp;itemid=0-8058-0619-9\">Gluck, M. A. &amp; Rumelhart, D. E., <em> Editors<\/em> (1990).\u00a0 <em>Neuroscience and Connectionist Theory<\/em>, Hillsdale, N.J. Lawrence Erlbaum Associates.<\/a><\/p>\n<p>Gluck, M.A., <em>Editor<\/em> (1990).\u00a0 <em>Neural Networks for Defense Applications<\/em>, San Francisco: Miller-Freeman Publications.<\/p>\n<p><strong>BOOK REVIEWS AND COMMENTARIES<\/strong><\/p>\n<p>Warren, S. &amp; Gluck, M., (1998). Making memories. Lincoln Center Theater Review (pp. 18-20). Spring Issue Number 18. New York, NY: Lincoln Center.<\/p>\n<p>Gluck, M. A., Bartha, G. T., Reifsnider, E. S., &amp; Shiffrar, M. M. (1990). Review of Eckmiller &amp; von der Malsburg, &#8216;Neural Computers.&#8217; Psychological Science, 1(5), 287-292.<\/p>\n<p><strong>CONFERENCE PROCEEDINGS<\/strong><\/p>\n<p>Japkowicz, N., Myers, C.E. &amp; Gluck, M.A (1995). A novelty detection approach to classification.\u00a0 <em> Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence.<\/em> Montreal, August 20-25 1995). Montreal, Canada: Morgan Kaufmann. 518-523.<\/p>\n<p>Gluck, M. A. (1994). What does the hippocampus compute?: Precis of the NIPS workshop. In J. D. Cowan, G. Tesauro, G., &amp; J. Alspector, (Editors), <em>Advances in Neural Information Processing Systems\u00a0 #6.<\/em> San Francisco, CA: Morgan Kaufman Publishers. 1173-1175.<\/p>\n<p>Gluck, M. A., &amp; Myers, C. E. (1993). Adaptive stimulus representations: A computational theory of hippocampal-region function. In S. Hanson, J. Cowan, &amp; C. Giles (Editors). <em>Advances in Neural Information Processing Systems #5.<\/em> San Mateo, CA: Morgan Kaufman. 937-944.<\/p>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/GGS-92.pdf\">Gluck, M. A., Glauthier, P. T., &amp; Sutton, R. S. (1992). Adaptation of cue-specific learning rates in network models of human category learning. In <em>Proceedings of the 14th Annual Conference of the Cognitive Science Society (Bloomington, IN)<\/em>, Hillsdale, NJ: Lawrence Earlbaum Associates. 540-545.<\/a><\/p>\n<p>Gluck, M. A. &amp; Myers, C. E. (1992). Hippocampal-system function in stimulus representation and generalization: A computational theory. In <em>Proceedings of the 14th Annual Conference of the Cognitive Science Society (Bloomington, IN)<\/em>, Hillsdale, NJ: Lawrence Earlbaum Associates. 390-395.<\/p>\n<p>Hanson, S. J., &amp; Gluck, M. A. (1991).\u00a0 Spherical units as dynamic consequential regions: Implications for attention and cue-competition in categorization. <em>Advances in Neural Information Processing Systems #3<\/em>. San Mateo, CA: Morgan Kaufman, 656-665.<\/p>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/computational-models-of-category-learning.pdf\">Billman, D., Fisher, D., Gluck, M., Langley, P., &amp; Pazzani, M. (1990).\u00a0 Computational models of category learning. In <em>Proceedings of the 12th Annual Conference of the Cognitive Science Society (Cambridge, MA)<\/em>, Hillsdale, NJ: Lawrence Erlbaum Associates 989-996.<\/a><\/p>\n<p>Gluck, M., Parker, D. B., &amp; Reifsnider, E. B., (1989).\u00a0 Learning with temporal derivatives in pulse-coded neuronal systems. In D. Touretzky (Ed.). <em>Advances in Neural Information Processing Systems. <\/em>San Mateo, CA:\u00a0 Morgan Kaufman, 195-205.<\/p>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Pavel_Gluck_Henkle_1989_Constraints-on-Adaptive-Networks-.pdf\">Pavel, M., Gluck, M. A., &amp; Henkle, V. (1989).\u00a0 Constraints on adaptive networks for modeling human generalization. In D. Touretzky (Ed.). <em>Advances in Neural Infromation Processing Systems.<\/em>\u00a0 San Mateo, CA: Morgan Kaufman, 2-10.<\/a><\/p>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/A-Configural-Cue-network-Model-of-Animal-and-Human-Associative-Learning.pdf\">Gluck, M. A. &amp; Bower, G. H., &amp; Hee, M. (1989).\u00a0 A configural-cue network model of animal and human associative learning. <em>Proceedings of the 11th Annual Conference of the Cognitive Science Society<\/em>, Ann Arbor, MI. 323-332.<\/a><\/p>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/GluckBowerConfigCue-NEVERPUBLISHED.pdf\"><strong><em>Never published 1992 book-length draft version<\/em><\/strong><\/a><\/p>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Corter-Gluck-Bower-1988.pdf\">Corter, J., Gluck, M. A., Bower, G. H. (1988).\u00a0 Basic levels in hierarchically structured categories, <em> Proceedings of the 10th Annual Conference of the Cognitive Science Society<\/em>, Montreal, Canada. 118-124. <\/a><\/p>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/Pavel_Gluck_Henkle_1988_Generalizations-by-Humans_Multi-layer-Adaptive-Networks.pdf\">Pavel, M., Gluck, M. A., Henkle, V. (1988).\u00a0 Generalization by humans and multi-layer adaptive networks, <em>Proceedings of the 10th Annual Conference of the Cognitive Science Society,<\/em> Montreal, Canada. 680-687.<\/a><\/p>\n<p><a href=\"https:\/\/brainhealth.rutgers.edu\/wp-content\/uploads\/2022\/04\/GluckBower1988-1.pdf\">Gluck, M. A. &amp; Bower, G. H. (1988).\u00a0 From conditioning to category learning: An adaptive network model.\u00a0 Journal of Experimental Psychology: General<em>,<\/em> 117(3), 227-247.<\/a><\/p>\n<p>Gluck, M. A. &amp; Bower, G. H. (1986). Conditioning and categorization: Some common effects of informational variables in animal and human learning. <em>Proceedings of the 8th Annual Conference of the Cognitive Science Society, <\/em>Amherst, Mass. 126-140.<\/p>\n<p>Gluck, M. A., &amp; Thompson, R. F. (1985).\u00a0 A computer model of the neural substrates of classical conditioning in the Aplysia. <em>Proceedings of the 7th Annual Conference of the Cognitive Science Society, <\/em>Irvine, Calif. 36-40.<\/p>\n<p>Gluck, M. A. &amp; Corter, J. E. (1985).\u00a0 Information, uncertainty, and the utility of categories. <em>Proceedings of the 7th Annual Conference of the Cognitive Science Society,<\/em> Irvine, Calif. .283-287<\/p>\n<p>Corter, J. E., &amp; Gluck, M. A. (1985).\u00a0 Machine generalization and human categorization: An information-theoretic view. <em>Proceedings of AAAI\/IEEE Workshop on Uncertainty and Probability in Artificial Intelligence,<\/em> Los Angeles, Calif. 201-207.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Now Available in 4th Edition Textbook Learning and Memory: From Brain to Behavior Recently updated, with a convenient new modular format, Learning and Memory, Second Edition, shows students where the study of learning and memory is and where it is heading. Requiring no prerequisite coursework, this textbook connects learning, memory, and neuroscience. The book compares brain studies and behavioral approaches in human and other animal species, includes extensive clinical perspectives, and is printed in full-color throughout. More Info Click below for more info or to purchase the book in: English Spanish German Korean Turkish BOOKS AUTHORED Gluck, M.A., Mercado, E., &amp; Myers, C.E. (2016). Learning and Memory: From Brain to Behavior. 3rd edition. New York: Worth Gluck, M.A., Mercado, E., &amp; Myers, C.E. (2014). Learning and Memory: From Brain to Behavior. 2nd edition. New York: Worth Gluck, M.A., Mercado, E., &amp; Myers, C.E. (2008). Learning and Memory: From Brain to Behavior. 1st edition. New York: Worth Spanish translation: Aprendizaje y memoria (2009). Mexico: McGraw Hill Interamericana Editores, S.A. DE C.V. Korean translation: \u00a0\ud559\uc2b5\uacfc \uae30\uc5b5: \ub1cc\uc5d0\uc11c \ud589\ub3d9\uae4c\uc9c0. (2011). Sigma Press, Inc. German translation: Lernen und Ged\u00e4chtnis (2010). Heidelberg: Spektrum Akademischer Verlag. Gluck, M.A. &amp; Myers, C.E. (2001). Gateway to Memory: An&#8230;.<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":37,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"acf":[],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/brainhealth.rutgers.edu\/wp-json\/wp\/v2\/pages\/1931"}],"collection":[{"href":"https:\/\/brainhealth.rutgers.edu\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/brainhealth.rutgers.edu\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/brainhealth.rutgers.edu\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/brainhealth.rutgers.edu\/wp-json\/wp\/v2\/comments?post=1931"}],"version-history":[{"count":5,"href":"https:\/\/brainhealth.rutgers.edu\/wp-json\/wp\/v2\/pages\/1931\/revisions"}],"predecessor-version":[{"id":223237,"href":"https:\/\/brainhealth.rutgers.edu\/wp-json\/wp\/v2\/pages\/1931\/revisions\/223237"}],"wp:attachment":[{"href":"https:\/\/brainhealth.rutgers.edu\/wp-json\/wp\/v2\/media?parent=1931"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}