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Dubinsky JM, Hamid AA. The neuroscience of active learning and direct instruction. Neurosci Biobehav Rev 2024; 163:105737. [PMID: 38796122 DOI: 10.1016/j.neubiorev.2024.105737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 05/13/2024] [Accepted: 05/20/2024] [Indexed: 05/28/2024]
Abstract
Throughout the educational system, students experiencing active learning pedagogy perform better and fail less than those taught through direct instruction. Can this be ascribed to differences in learning from a neuroscientific perspective? This review examines mechanistic, neuroscientific evidence that might explain differences in cognitive engagement contributing to learning outcomes between these instructional approaches. In classrooms, direct instruction comprehensively describes academic content, while active learning provides structured opportunities for learners to explore, apply, and manipulate content. Synaptic plasticity and its modulation by arousal or novelty are central to all learning and both approaches. As a form of social learning, direct instruction relies upon working memory. The reinforcement learning circuit, associated agency, curiosity, and peer-to-peer social interactions combine to enhance motivation, improve retention, and build higher-order-thinking skills in active learning environments. When working memory becomes overwhelmed, additionally engaging the reinforcement learning circuit improves retention, providing an explanation for the benefits of active learning. This analysis provides a mechanistic examination of how emerging neuroscience principles might inform pedagogical choices at all educational levels.
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Affiliation(s)
- Janet M Dubinsky
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA.
| | - Arif A Hamid
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA
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2
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Arató J, Rothkopf CA, Fiser J. Eye movements reflect active statistical learning. J Vis 2024; 24:17. [PMID: 38819805 PMCID: PMC11146064 DOI: 10.1167/jov.24.5.17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 04/23/2024] [Indexed: 06/01/2024] Open
Abstract
What is the link between eye movements and sensory learning? Although some theories have argued for an automatic interaction between what we know and where we look that continuously modulates human information gathering behavior during both implicit and explicit learning, there exists limited experimental evidence supporting such an ongoing interplay. To address this issue, we used a visual statistical learning paradigm combined with a gaze-contingent stimulus presentation and manipulated the explicitness of the task to explore how learning and eye movements interact. During both implicit exploration and explicit visual learning of unknown composite visual scenes, spatial eye movement patterns systematically and gradually changed in accordance with the underlying statistical structure of the scenes. Moreover, the degree of change was directly correlated with the amount and type of knowledge the observers acquired. This suggests that eye movements are potential indicators of active learning, a process where long-term knowledge, current visual stimuli and an inherent tendency to reduce uncertainty about the visual environment jointly determine where we look.
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Affiliation(s)
- József Arató
- Department of Cognitive Science, Central European University, Vienna, Austria
- Center for Cognitive Computation, Central European University, Vienna, Austria
- Vienna Cognitive Science Hub, University of Vienna, Vienna, Austria
| | - Constantin A Rothkopf
- Center for Cognitive Science & Institute of Psychology, Technical University of Darmstadt, Darmstadt, Germany
- Frankfurt Institute for Advanced Studies, Goethe University, Frankfurt, Germany
| | - József Fiser
- Department of Cognitive Science, Central European University, Vienna, Austria
- Center for Cognitive Computation, Central European University, Vienna, Austria
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3
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Nagel J, Morgan DP, Gürsoy NÇ, Sander S, Kern S, Feld GB. Memory for rewards guides retrieval. COMMUNICATIONS PSYCHOLOGY 2024; 2:31. [PMID: 39242930 PMCID: PMC11332070 DOI: 10.1038/s44271-024-00074-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 03/11/2024] [Indexed: 09/09/2024]
Abstract
Rewards paid out for successful retrieval motivate the formation of long-term memory. However, it has been argued that the Motivated Learning Task does not measure reward effects on memory strength but decision-making during retrieval. We report three large-scale online experiments in healthy participants (N = 200, N = 205, N = 187) that inform this debate. In experiment 1, we found that explicit stimulus-reward associations formed during encoding influence response strategies at retrieval. In experiment 2, reward affected memory strength and decision-making strategies. In experiment 3, reward affected decision-making strategies only. These data support a theoretical framework that assumes that promised rewards not only increase memory strength, but additionally lead to the formation of stimulus-reward associations that influence decisions at retrieval.
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Affiliation(s)
- Juliane Nagel
- Clinical Psychology, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.
- Addiction Behavior and Addiction Medicine, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.
- Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.
| | - David Philip Morgan
- Clinical Psychology, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
- Addiction Behavior and Addiction Medicine, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
- Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Necati Çağatay Gürsoy
- Clinical Psychology, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
- Addiction Behavior and Addiction Medicine, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
- Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Samuel Sander
- Clinical Psychology, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
- Addiction Behavior and Addiction Medicine, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
- Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Simon Kern
- Clinical Psychology, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
- Addiction Behavior and Addiction Medicine, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
- Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Gordon Benedikt Feld
- Clinical Psychology, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.
- Addiction Behavior and Addiction Medicine, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.
- Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.
- Department of Psychology, University of Heidelberg, Heidelberg, Germany.
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Philippe R, Janet R, Khalvati K, Rao RPN, Lee D, Dreher JC. Neurocomputational mechanisms involved in adaptation to fluctuating intentions of others. Nat Commun 2024; 15:3189. [PMID: 38609372 PMCID: PMC11014977 DOI: 10.1038/s41467-024-47491-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 03/12/2024] [Indexed: 04/14/2024] Open
Abstract
Humans frequently interact with agents whose intentions can fluctuate between competition and cooperation over time. It is unclear how the brain adapts to fluctuating intentions of others when the nature of the interactions (to cooperate or compete) is not explicitly and truthfully signaled. Here, we use model-based fMRI and a task in which participants thought they were playing with another player. In fact, they played with an algorithm that alternated without signaling between cooperative and competitive strategies. We show that a neurocomputational mechanism with arbitration between competitive and cooperative experts outperforms other learning models in predicting choice behavior. At the brain level, the fMRI results show that the ventral striatum and ventromedial prefrontal cortex track the difference of reliability between these experts. When attributing competitive intentions, we find increased coupling between these regions and a network that distinguishes prediction errors related to competition and cooperation. These findings provide a neurocomputational account of how the brain arbitrates dynamically between cooperative and competitive intentions when making adaptive social decisions.
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Affiliation(s)
- Rémi Philippe
- CNRS-Institut des Sciences Cognitives Marc Jeannerod, UMR5229, Neuroeconomics, reward, and decision making laboratory, Lyon, France
- Université Claude Bernard Lyon 1, Lyon, France
| | - Rémi Janet
- CNRS-Institut des Sciences Cognitives Marc Jeannerod, UMR5229, Neuroeconomics, reward, and decision making laboratory, Lyon, France
- Université Claude Bernard Lyon 1, Lyon, France
| | - Koosha Khalvati
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - Rajesh P N Rao
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
- Center for Neurotechnology, University of Washington, Seattle, WA, USA
| | - Daeyeol Lee
- Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, MD, USA
- Kavli Discovery Neuroscience Institute, Johns Hopkins University, Baltimore, MD, USA
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University, Baltimore, MD, USA
| | - Jean-Claude Dreher
- CNRS-Institut des Sciences Cognitives Marc Jeannerod, UMR5229, Neuroeconomics, reward, and decision making laboratory, Lyon, France.
- Université Claude Bernard Lyon 1, Lyon, France.
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Xu S, Ren W. Distinct processing of the state prediction error signals in frontal and parietal correlates in learning the environment model. Cereb Cortex 2024; 34:bhad449. [PMID: 38037370 DOI: 10.1093/cercor/bhad449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 10/31/2023] [Indexed: 12/02/2023] Open
Abstract
Goal-directed reinforcement learning constructs a model of how the states in the environment are connected and prospectively evaluates action values by simulating experience. State prediction error (SPE) is theorized as a crucial signal for learning the environment model. However, the underlying neural mechanisms remain unclear. Here, using electroencephalogram, we verified in a two-stage Markov task two neural correlates of SPEs: an early negative correlate transferring from frontal to central electrodes and a late positive correlate over parietal regions. Furthermore, by investigating the effects of explicit knowledge about the environment model and rewards in the environment, we found that, for the parietal correlate, rewards enhanced the representation efficiency (beta values of regression coefficient) of SPEs, whereas explicit knowledge elicited a larger SPE representation (event-related potential activity) for rare transitions. However, for the frontal and central correlates, rewards increased activities in a content-independent way and explicit knowledge enhanced activities only for common transitions. Our results suggest that the parietal correlate of SPEs is responsible for the explicit learning of state transition structure, whereas the frontal and central correlates may be involved in cognitive control. Our study provides novel evidence for distinct roles of the frontal and the parietal cortices in processing SPEs.
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Affiliation(s)
- Shuyuan Xu
- MOE Key Laboratory of Modern Teaching Technology, Shaanxi Normal University, Xi'an, Shaanxi, China
| | - Wei Ren
- MOE Key Laboratory of Modern Teaching Technology, Shaanxi Normal University, Xi'an, Shaanxi, China
- Faculty of Education, Shaanxi Normal University, Xi'an, Shaanxi, China
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Donegan KR, Brown VM, Price RB, Gallagher E, Pringle A, Hanlon AK, Gillan CM. Using smartphones to optimise and scale-up the assessment of model-based planning. COMMUNICATIONS PSYCHOLOGY 2023; 1:31. [PMID: 39242869 PMCID: PMC11332031 DOI: 10.1038/s44271-023-00031-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 10/05/2023] [Indexed: 09/09/2024]
Abstract
Model-based planning is thought to protect against over-reliance on habits. It is reduced in individuals high in compulsivity, but effect sizes are small and may depend on subtle features of the tasks used to assess it. We developed a diamond-shooting smartphone game that measures model-based planning in an at-home setting, and varied the game's structure within and across participants to assess how it affects measurement reliability and validity with respect to previously established correlates of model-based planning, with a focus on compulsivity. Increasing the number of trials used to estimate model-based planning did remarkably little to affect the association with compulsivity, because the greatest signal was in earlier trials. Associations with compulsivity were higher when transition ratios were less deterministic and depending on the reward drift utilised. These findings suggest that model-based planning can be measured at home via an app, can be estimated in relatively few trials using certain design features, and can be optimised for sensitivity to compulsive symptoms in the general population.
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Affiliation(s)
- Kelly R Donegan
- School of Psychology, Trinity College Dublin, Dublin, Ireland
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Vanessa M Brown
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, USA
| | - Rebecca B Price
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, USA
| | - Eoghan Gallagher
- School of Psychology, Trinity College Dublin, Dublin, Ireland
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Andrew Pringle
- School of Psychology, Trinity College Dublin, Dublin, Ireland
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Anna K Hanlon
- School of Psychology, Trinity College Dublin, Dublin, Ireland
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Claire M Gillan
- School of Psychology, Trinity College Dublin, Dublin, Ireland.
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland.
- Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland.
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Tuominen J. Decisions under uncertainty are more messy than they seem. Behav Brain Sci 2023; 46:e109. [PMID: 37154119 DOI: 10.1017/s0140525x22002576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Conviction Narrative Theory (CNT) is conceptually so multifaceted as to make critical evaluation difficult. It also omits one course of action: Active engagement with the world. Parsing the developmental and mechanistic processes within CNT would allow for a rigorous research programme to put the account under test. I propose a unifying account based on active inference.
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Affiliation(s)
- Jarno Tuominen
- Department of Psychology and Speech-Language Pathology, University of Turku, FI-20014 Turku, Finland https://www.utu.fi/en/people/jarno-tuominen
- Department of Sociology, University of Helsinki, FI-00100 Helsinki, Finland.
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Mennella R, Bavard S, Mentec I, Grèzes J. Spontaneous instrumental avoidance learning in social contexts. Sci Rep 2022; 12:17528. [PMID: 36266316 PMCID: PMC9585085 DOI: 10.1038/s41598-022-22334-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 10/13/2022] [Indexed: 01/13/2023] Open
Abstract
Adaptation to our social environment requires learning how to avoid potentially harmful situations, such as encounters with aggressive individuals. Threatening facial expressions can evoke automatic stimulus-driven reactions, but whether their aversive motivational value suffices to drive instrumental active avoidance remains unclear. When asked to freely choose between different action alternatives, participants spontaneously-without instruction or monetary reward-developed a preference for choices that maximized the probability of avoiding angry individuals (sitting away from them in a waiting room). Most participants showed clear behavioral signs of instrumental learning, even in the absence of an explicit avoidance strategy. Inter-individual variability in learning depended on participants' subjective evaluations and sensitivity to threat approach feedback. Counterfactual learning best accounted for avoidance behaviors, especially in participants who developed an explicit avoidance strategy. Our results demonstrate that implicit defensive behaviors in social contexts are likely the product of several learning processes, including instrumental learning.
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Affiliation(s)
- Rocco Mennella
- grid.508487.60000 0004 7885 7602Laboratoire des Interactions Cognition, Action, Émotion (LICAÉ), Université Paris Nanterre, 200 Avenue de La République, 92001 Nanterre Cedex, France ,grid.440907.e0000 0004 1784 3645Cognitive and Computational Neuroscience Laboratory (LNC2), Inserm U960, Department of Cognitive Studies, École Normale Supérieure, PSL University, 29 Rue d’Ulm, 75005 Paris, France
| | - Sophie Bavard
- grid.440907.e0000 0004 1784 3645Cognitive and Computational Neuroscience Laboratory (LNC2), Inserm U960, Department of Cognitive Studies, École Normale Supérieure, PSL University, 29 Rue d’Ulm, 75005 Paris, France ,grid.9026.d0000 0001 2287 2617Department of Psychology, University of Hamburg, Von-Melle-Park 11, 20146 Hamburg, Germany
| | - Inès Mentec
- grid.440907.e0000 0004 1784 3645Cognitive and Computational Neuroscience Laboratory (LNC2), Inserm U960, Department of Cognitive Studies, École Normale Supérieure, PSL University, 29 Rue d’Ulm, 75005 Paris, France
| | - Julie Grèzes
- grid.440907.e0000 0004 1784 3645Cognitive and Computational Neuroscience Laboratory (LNC2), Inserm U960, Department of Cognitive Studies, École Normale Supérieure, PSL University, 29 Rue d’Ulm, 75005 Paris, France
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Nour MM, Liu Y, Dolan RJ. Functional neuroimaging in psychiatry and the case for failing better. Neuron 2022; 110:2524-2544. [PMID: 35981525 DOI: 10.1016/j.neuron.2022.07.005] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 06/06/2022] [Accepted: 07/08/2022] [Indexed: 12/27/2022]
Abstract
Psychiatric disorders encompass complex aberrations of cognition and affect and are among the most debilitating and poorly understood of any medical condition. Current treatments rely primarily on interventions that target brain function (drugs) or learning processes (psychotherapy). A mechanistic understanding of how these interventions mediate their therapeutic effects remains elusive. From the early 1990s, non-invasive functional neuroimaging, coupled with parallel developments in the cognitive neurosciences, seemed to signal a new era of neurobiologically grounded diagnosis and treatment in psychiatry. Yet, despite three decades of intense neuroimaging research, we still lack a neurobiological account for any psychiatric condition. Likewise, functional neuroimaging plays no role in clinical decision making. Here, we offer a critical commentary on this impasse and suggest how the field might fare better and deliver impactful neurobiological insights.
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Affiliation(s)
- Matthew M Nour
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London WC1B 5EH, UK; Wellcome Trust Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK; Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK.
| | - Yunzhe Liu
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London WC1B 5EH, UK; State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Chinese Institute for Brain Research, Beijing 102206, China
| | - Raymond J Dolan
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London WC1B 5EH, UK; Wellcome Trust Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK; State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
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