1
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Li N, Lavalley CA, Chou KP, Chuning AE, Taylor S, Goldman CM, Torres T, Hodson R, Wilson RC, Stewart JL, Khalsa SS, Paulus MP, Smith R. Directed exploration is elevated in affective disorders but reduced by an aversive interoceptive state induction. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.19.24309110. [PMID: 38947082 PMCID: PMC11213056 DOI: 10.1101/2024.06.19.24309110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Elevated anxiety and uncertainty avoidance are known to exacerbate maladaptive choice in individuals with affective disorders. However, the differential roles of state vs. trait anxiety remain unclear, and underlying computational mechanisms have not been thoroughly characterized. In the present study, we investigated how a somatic (interoceptive) state anxiety induction influences learning and decision-making under uncertainty in individuals with clinically significant levels of trait anxiety. A sample of 58 healthy comparisons (HCs) and 61 individuals with affective disorders (iADs; i.e., depression and/or anxiety) completed a previously validated explore-exploit decision task, with and without an added breathing resistance manipulation designed to induce state anxiety. Computational modeling revealed a pattern in which iADs showed greater information-seeking (i.e., directed exploration; Cohen's d =.39, p =.039) in resting conditions, but that this was reduced by the anxiety induction. The affective disorders group also showed slower learning rates across conditions (Cohen's d =.52, p =.003), suggesting more persistent uncertainty. These findings highlight a complex interplay between trait anxiety and state anxiety. Specifically, while elevated trait anxiety is associated with persistent uncertainty, acute somatic anxiety can paradoxically curtail exploratory behaviors, potentially reinforcing maladaptive decision-making patterns in affective disorders.
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2
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Yan X, Ebitz RB, Grissom N, Darrow DP, Herman AB. Individual differences in uncertainty evaluation explain opposing exploratory behaviors in anxiety and apathy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.04.597412. [PMID: 38895240 PMCID: PMC11185698 DOI: 10.1101/2024.06.04.597412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Navigating uncertain environments is a fundamental challenge for adaptive behavior, and affective states such as anxiety and apathy can profoundly influence an individual's response to uncertainty. Uncertainty encompasses both volatility and stochasticity, where volatility refers to how rapidly the environment changes and stochasticity describes outcomes resulting from random chance. This study investigates how anxiety and apathy modulate perceptions of environmental volatility and stochasticity and how these perceptions impact exploratory behavior. In a large online sample (N = 1001), participants completed a restless three-armed bandit task, and their choices were analyzed using latent state models to quantify the computational processes. We found that anxious individuals attributed uncertainty more to environmental volatility than stochasticity, leading to increased exploration, particularly after reward omission. Conversely, apathetic individuals perceived uncertainty as more stochastic than volatile, resulting in decreased exploration. The ratio of perceived volatility to stochasticity mediated the relationship between anxiety and exploratory behavior following adverse outcomes. These findings reveal distinct computational mechanisms underlying anxiety and apathy in uncertain environments. Our results provide a novel framework for understanding the cognitive and affective processes driving adaptive and potentially maladaptive behaviors under uncertainty, with implications for the characterization and treatment of neuropsychiatric disorders.
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Affiliation(s)
- Xinyuan Yan
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455, USA
| | - R. Becket Ebitz
- Department of Neuroscience, Universite de Montreal, 2900 Edouard Montpetit Blvd, Montreal, Quebec H3T 1J4, Canada
| | - Nicola Grissom
- Department of Psychology, University of Minnesota, 75 E River Rd, Minneapolis, MN 55455, USA
| | - David P. Darrow
- Department of Neurosurgery, University of Minnesota, Minneapolis, MN 55455, USA
| | - Alexander B. Herman
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455, USA
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3
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Goldman CM, Takahashi T, Lavalley CA, Li N, Taylor S, Chuning AE, Hodson R, Stewart JL, Wilson RC, Khalsa SS, Paulus MP, Smith R. Individuals with Methamphetamine Use Disorder Show Reduced Directed Exploration and Learning Rates Independent of an Aversive Interoceptive State Induction. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.17.24307491. [PMID: 38826438 PMCID: PMC11142260 DOI: 10.1101/2024.05.17.24307491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Methamphetamine Use Disorder (MUD) is associated with substantially reduced quality of life. Yet, decisions to use persist, due in part to avoidance of anticipated withdrawal states. However, the specific cognitive mechanisms underlying this decision process, and possible modulatory effects of aversive states, remain unclear. Here, 56 individuals with MUD and 58 healthy comparisons (HCs) performed a decision task, both with and without an aversive interoceptive state induction. Computational modeling measured the tendency to test beliefs about uncertain outcomes (directed exploration) and the ability to update beliefs in response to outcomes (learning rates). Compared to HCs, those with MUD exhibited less directed exploration and slower learning rates, but these differences were not affected by aversive state induction. These results suggest novel, state-independent computational mechanisms whereby individuals with MUD may have difficulties in testing beliefs about the tolerability of abstinence and in adjusting behavior in response to consequences of continued use.
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Affiliation(s)
| | - Toru Takahashi
- Laureate Institute for Brain Research, Tulsa, OK
- Japan Society for the Promotion of Science, Tokyo, Japan
| | | | - Ning Li
- Laureate Institute for Brain Research, Tulsa, OK
| | | | | | - Rowan Hodson
- Laureate Institute for Brain Research, Tulsa, OK
| | - Jennifer L. Stewart
- Laureate Institute for Brain Research, Tulsa, OK
- Oxley College of Health and Natural Sciences, University of Tulsa, Tulsa, OK
| | - Robert C. Wilson
- Department of Psychology, University of Arizona, Tucson, AZ
- Cognitive Science Program, University of Arizona, Tucson, AZ
| | - Sahib S. Khalsa
- Laureate Institute for Brain Research, Tulsa, OK
- Oxley College of Health and Natural Sciences, University of Tulsa, Tulsa, OK
| | - Martin P. Paulus
- Laureate Institute for Brain Research, Tulsa, OK
- Oxley College of Health and Natural Sciences, University of Tulsa, Tulsa, OK
| | - Ryan Smith
- Laureate Institute for Brain Research, Tulsa, OK
- Oxley College of Health and Natural Sciences, University of Tulsa, Tulsa, OK
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4
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Mochizuki Y, Harasawa N, Aggarwal M, Chen C, Fukuda H. Foraging in a non-foraging task: Fitness maximization explains human risk preference dynamics under changing environment. PLoS Comput Biol 2024; 20:e1012080. [PMID: 38739672 PMCID: PMC11115364 DOI: 10.1371/journal.pcbi.1012080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 05/23/2024] [Accepted: 04/16/2024] [Indexed: 05/16/2024] Open
Abstract
Changes in risk preference have been reported when making a series of independent risky choices or non-foraging economic decisions. Behavioral economics has put forward various explanations for specific changes in risk preference in non-foraging tasks, but a consensus regarding the general principle underlying these effects has not been reached. In contrast, recent studies have investigated human economic risky choices using tasks adapted from foraging theory, which require consideration of past choices and future opportunities to make optimal decisions. In these foraging tasks, human economic risky choices are explained by the ethological principle of fitness maximization, which naturally leads to dynamic risk preference. Here, we conducted two online experiments to investigate whether the principle of fitness maximization can explain risk preference dynamics in a non-foraging task. Participants were asked to make a series of independent risky economic decisions while the environmental richness changed. We found that participants' risk preferences were influenced by the current and past environments, making them more risk-averse during and after the rich environment compared to the poor environment. These changes in risk preference align with fitness maximization. Our findings suggest that the ethological principle of fitness maximization might serve as a generalizable principle for explaining dynamic preferences, including risk preference, in human economic decision-making.
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Affiliation(s)
| | | | | | - Chong Chen
- Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, Ube, Yamaguchi, Japan
| | - Haruaki Fukuda
- Graduate School of Business Administration, Hitotsubashi University, Kunitachi, Tokyo, Japan
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5
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Kang P, Tobler PN, Dayan P. Bayesian reinforcement learning: A basic overview. Neurobiol Learn Mem 2024; 211:107924. [PMID: 38579896 DOI: 10.1016/j.nlm.2024.107924] [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: 11/20/2023] [Revised: 03/21/2024] [Accepted: 04/02/2024] [Indexed: 04/07/2024]
Abstract
We and other animals learn because there is some aspect of the world about which we are uncertain. This uncertainty arises from initial ignorance, and from changes in the world that we do not perfectly know; the uncertainty often becomes evident when our predictions about the world are found to be erroneous. The Rescorla-Wagner learning rule, which specifies one way that prediction errors can occasion learning, has been hugely influential as a characterization of Pavlovian conditioning and, through its equivalence to the delta rule in engineering, in a much wider class of learning problems. Here, we review the embedding of the Rescorla-Wagner rule in a Bayesian context that is precise about the link between uncertainty and learning, and thereby discuss extensions to such suggestions as the Kalman filter, structure learning, and beyond, that collectively encompass a wider range of uncertainties and accommodate a wider assortment of phenomena in conditioning.
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Affiliation(s)
- Pyungwon Kang
- University of Zurich, Department of Economics, Laboratory for Social and Neural Systems Research, Zurich, Switzerland.
| | - Philippe N Tobler
- University of Zurich, Department of Economics, Laboratory for Social and Neural Systems Research, Zurich, Switzerland.
| | - Peter Dayan
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany; University of Tübingen, Tübingen Germany.
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6
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Wang T, Avraham G, Tsay JS, Abram SJ, Ivry RB. Perturbation Variability Does Not Influence Implicit Sensorimotor Adaptation. PLoS Comput Biol 2024; 20:e1011951. [PMID: 38598603 PMCID: PMC11034674 DOI: 10.1371/journal.pcbi.1011951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 04/22/2024] [Accepted: 02/27/2024] [Indexed: 04/12/2024] Open
Abstract
Implicit adaptation has been regarded as a rigid process that automatically operates in response to movement errors to keep the sensorimotor system precisely calibrated. This hypothesis has been challenged by recent evidence suggesting flexibility in this learning process. One compelling line of evidence comes from work suggesting that this form of learning is context-dependent, with the rate of learning modulated by error history. Specifically, learning was attenuated in the presence of perturbations exhibiting high variance compared to when the perturbation is fixed. However, these findings are confounded by the fact that the adaptation system corrects for errors of different magnitudes in a non-linear manner, with the adaptive response increasing in a proportional manner to small errors and saturating to large errors. Through simulations, we show that this non-linear motor correction function is sufficient to explain the effect of perturbation variance without referring to an experience-dependent change in error sensitivity. Moreover, by controlling the distribution of errors experienced during training, we provide empirical evidence showing that there is no measurable effect of perturbation variance on implicit adaptation. As such, we argue that the evidence to date remains consistent with the rigidity assumption.
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Affiliation(s)
- Tianhe Wang
- Department of Psychology and Helen Wills Neuroscience Institute, University of California, Berkeley, California, United States of America
| | - Guy Avraham
- Department of Psychology and Helen Wills Neuroscience Institute, University of California, Berkeley, California, United States of America
| | - Jonathan S. Tsay
- Department of Psychology and Helen Wills Neuroscience Institute, University of California, Berkeley, California, United States of America
| | - Sabrina J. Abram
- Department of Psychology and Helen Wills Neuroscience Institute, University of California, Berkeley, California, United States of America
| | - Richard B. Ivry
- Department of Psychology and Helen Wills Neuroscience Institute, University of California, Berkeley, California, United States of America
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7
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Gregorová K, Eldar E, Deserno L, Reiter AMF. A cognitive-computational account of mood swings in adolescence. Trends Cogn Sci 2024; 28:290-303. [PMID: 38503636 DOI: 10.1016/j.tics.2024.02.006] [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: 11/22/2022] [Revised: 02/06/2024] [Accepted: 02/12/2024] [Indexed: 03/21/2024]
Abstract
Teenagers have a reputation for being fickle, in both their choices and their moods. This variability may help adolescents as they begin to independently navigate novel environments. Recently, however, adolescent moodiness has also been linked to psychopathology. Here, we consider adolescents' mood swings from a novel computational perspective, grounded in reinforcement learning (RL). This model proposes that mood is determined by surprises about outcomes in the environment, and how much we learn from these surprises. It additionally suggests that mood biases learning and choice in a bidirectional manner. Integrating independent lines of research, we sketch a cognitive-computational account of how adolescents' mood, learning, and choice dynamics influence each other, with implications for normative and psychopathological development.
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Affiliation(s)
- Klára Gregorová
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Würzburg 97080, Germany; Department of Psychology, Julius-Maximilians-Universität, Würzburg 97070, Germany; German Center of Prevention Research on Mental Health, Würzburg 97080, Germany
| | - Eran Eldar
- Department of Psychology, Hebrew University of Jerusalem, Jerusalem 9190501, Israel; Department of Cognitive & Brain Sciences, Hebrew University of Jerusalem, Jerusalem 9190501, Israel
| | - Lorenz Deserno
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Würzburg 97080, Germany; Department of Psychology, Julius-Maximilians-Universität, Würzburg 97070, Germany; Department of Cognitive & Brain Sciences, Hebrew University of Jerusalem, Jerusalem 9190501, Israel; Department of Psychiatry and Psychotherapy, Technical University of Dresden, Dresden 01069, Germany
| | - Andrea M F Reiter
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Würzburg 97080, Germany; Department of Psychology, Julius-Maximilians-Universität, Würzburg 97070, Germany; German Center of Prevention Research on Mental Health, Würzburg 97080, Germany; Collaborative Research Centre 940 Volition and Cognitive Control, Technical University of Dresden, Dresden 01069, Germany.
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8
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Van Allsburg J, Shahan TA. How do animals weigh conflicting information about reward sources over time? Comparing dynamic averaging models. Anim Cogn 2024; 27:11. [PMID: 38429608 PMCID: PMC10907467 DOI: 10.1007/s10071-024-01840-2] [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: 08/24/2023] [Revised: 12/15/2023] [Accepted: 12/19/2023] [Indexed: 03/03/2024]
Abstract
Optimal foraging theory suggests that animals make decisions which maximize their food intake per unit time when foraging, but the mechanisms animals use to track the value of behavioral alternatives and choose between them remain unclear. Several models for how animals integrate past experience have been suggested. However, these models make differential predictions for the occurrence of spontaneous recovery of choice: a behavioral phenomenon in which a hiatus from the experimental environment results in animals reverting to a behavioral allocation consistent with a reward distribution from the more distant past, rather than one consistent with their most recently experienced distribution. To explore this phenomenon and compare these models, three free-operant experiments with rats were conducted using a serial reversal design. In Phase 1, two responses (A and B) were baited with pellets on concurrent variable interval schedules, favoring option A. In Phase 2, lever baiting was reversed to favor option B. Rats then entered a delay period, where they were maintained at weight in their home cages and no experimental sessions took place. Following this delay, preference was assessed using initial responding in test sessions where levers were presented, but not baited. Models were compared in performance, including an exponentially weighted moving average, the Temporal Weighting Rule, and variants of these models. While the data provided strong evidence of spontaneous recovery of choice, the form and extent of recovery was inconsistent with the models under investigation. Potential interpretations are discussed in relation to both the decision rule and valuation functions employed.
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Affiliation(s)
| | - Timothy A Shahan
- Department of Psychology, Utah State University, Logan, Utah, USA
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9
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Monosov IE. Curiosity: primate neural circuits for novelty and information seeking. Nat Rev Neurosci 2024; 25:195-208. [PMID: 38263217 DOI: 10.1038/s41583-023-00784-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/13/2023] [Indexed: 01/25/2024]
Abstract
For many years, neuroscientists have investigated the behavioural, computational and neurobiological mechanisms that support value-based decisions, revealing how humans and animals make choices to obtain rewards. However, many decisions are influenced by factors other than the value of physical rewards or second-order reinforcers (such as money). For instance, animals (including humans) frequently explore novel objects that have no intrinsic value solely because they are novel and they exhibit the desire to gain information to reduce their uncertainties about the future, even if this information cannot lead to reward or assist them in accomplishing upcoming tasks. In this Review, I discuss how circuits in the primate brain responsible for detecting, predicting and assessing novelty and uncertainty regulate behaviour and give rise to these behavioural components of curiosity. I also briefly discuss how curiosity-related behaviours arise during postnatal development and point out some important reasons for the persistence of curiosity across generations.
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Affiliation(s)
- Ilya E Monosov
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, USA.
- Department of Electrical Engineering, Washington University, St. Louis, MO, USA.
- Department of Biomedical Engineering, Washington University, St. Louis, MO, USA.
- Department of Neurosurgery, Washington University, St. Louis, MO, USA.
- Pain Center, Washington University, St. Louis, MO, USA.
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10
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Farkas BC, Baptista A, Speranza M, Wyart V, Jacquet PO. Specifying the timescale of early life unpredictability helps explain the development of internalising and externalising behaviours. Sci Rep 2024; 14:3563. [PMID: 38347055 PMCID: PMC10861493 DOI: 10.1038/s41598-024-54093-x] [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: 08/08/2023] [Accepted: 02/08/2024] [Indexed: 02/15/2024] Open
Abstract
Early life unpredictability is associated with both physical and mental health outcomes throughout the life course. Here, we classified adverse experiences based on the timescale on which they are likely to introduce variability in children's environments: variations unfolding over short time scales (e.g., hours, days, weeks) and labelled Stochasticity vs variations unfolding over longer time scales (e.g., months, years) and labelled Volatility and explored how they contribute to the development of problem behaviours. Results indicate that externalising behaviours at age 9 and 15 and internalising behaviours at age 15 were better accounted for by models that separated Stochasticity and Volatility measured at ages 3 to 5. Both externalising and internalising behaviours were specifically associated with Volatility, with larger effects for externalising behaviours. These findings are interpreted in light of evolutionary-developmental models of psychopathology and reinforcement learning models of learning under uncertainty.
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Affiliation(s)
- Bence Csaba Farkas
- Institut du Psychotraumatisme de l'Enfant et de l'Adolescent, Conseil Départemental Yvelines et Hauts-de-Seine et Centre Hospitalier des Versailles, 78000, Versailles, France.
- UVSQ, Inserm, Centre de Recherche en Epidémiologie et Santé des Populations, Université Paris-Saclay, 78000, Versailles, France.
- LNC2, Département d'études Cognitives, École Normale Supérieure, INSERM, PSL Research University, 75005, Paris, France.
| | - Axel Baptista
- UVSQ, Inserm, Centre de Recherche en Epidémiologie et Santé des Populations, Université Paris-Saclay, 78000, Versailles, France
- Centre Hospitalier de Versailles, Le Chesnay, France
| | - Mario Speranza
- Institut du Psychotraumatisme de l'Enfant et de l'Adolescent, Conseil Départemental Yvelines et Hauts-de-Seine et Centre Hospitalier des Versailles, 78000, Versailles, France
- UVSQ, Inserm, Centre de Recherche en Epidémiologie et Santé des Populations, Université Paris-Saclay, 78000, Versailles, France
- Centre Hospitalier de Versailles, Le Chesnay, France
| | - Valentin Wyart
- Institut du Psychotraumatisme de l'Enfant et de l'Adolescent, Conseil Départemental Yvelines et Hauts-de-Seine et Centre Hospitalier des Versailles, 78000, Versailles, France
- LNC2, Département d'études Cognitives, École Normale Supérieure, INSERM, PSL Research University, 75005, Paris, France
| | - Pierre Olivier Jacquet
- Institut du Psychotraumatisme de l'Enfant et de l'Adolescent, Conseil Départemental Yvelines et Hauts-de-Seine et Centre Hospitalier des Versailles, 78000, Versailles, France
- UVSQ, Inserm, Centre de Recherche en Epidémiologie et Santé des Populations, Université Paris-Saclay, 78000, Versailles, France
- LNC2, Département d'études Cognitives, École Normale Supérieure, INSERM, PSL Research University, 75005, Paris, France
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11
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Leimar O, Quiñones AE, Bshary R. Flexible learning in complex worlds. Behav Ecol 2024; 35:arad109. [PMID: 38162692 PMCID: PMC10756056 DOI: 10.1093/beheco/arad109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 10/23/2023] [Accepted: 12/03/2023] [Indexed: 01/03/2024] Open
Abstract
Cognitive flexibility can enhance the ability to adjust to changing environments. Here, we use learning simulations to investigate the possible advantages of flexible learning in volatile (changing) environments. We compare two established learning mechanisms, one with constant learning rates and one with rates that adjust to volatility. We study an ecologically relevant case of volatility, based on observations of developing cleaner fish Labroides dimidiatus that experience a transition from a simpler to a more complex foraging environment. There are other similar transitions in nature, such as migrating to a new and different habitat. We also examine two traditional approaches to volatile environments in experimental psychology and behavioral ecology: reversal learning, and learning set formation (consisting of a sequence of different discrimination tasks). These provide experimental measures of cognitive flexibility. Concerning transitions to a complex world, we show that both constant and flexible learning rates perform well, losing only a small proportion of available rewards in the period after a transition, but flexible rates perform better than constant rates. For reversal learning, flexible rates improve the performance with each successive reversal because of increasing learning rates, but this does not happen for constant rates. For learning set formation, we find no improvement in performance with successive shifts to new stimuli to discriminate for either flexible or constant learning rates. Flexible learning rates might thus explain increasing performance in reversal learning but not in learning set formation, and this can shed light on the nature of cognitive flexibility in a given system.
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Affiliation(s)
- Olof Leimar
- Department of Zoology, Stockholm University, 106 91 Stockholm, Sweden and
| | - Andrés E Quiñones
- Institute of Biology, University of Neuchâtel, Emile-Argand 11, 2000 Neuchâtel, Switzerland
| | - Redouan Bshary
- Institute of Biology, University of Neuchâtel, Emile-Argand 11, 2000 Neuchâtel, Switzerland
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12
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Pike AC, Sharpley AL, Park RJ, Cowen PJ, Browning M, Pulcu E. Adaptive learning from outcome contingencies in eating-disorder risk groups. Transl Psychiatry 2023; 13:340. [PMID: 37925461 PMCID: PMC10625579 DOI: 10.1038/s41398-023-02633-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 10/13/2023] [Accepted: 10/18/2023] [Indexed: 11/06/2023] Open
Abstract
Eating disorders are characterised by altered eating patterns alongside overvaluation of body weight or shape, and have relatively low rates of successful treatment and recovery. Notably, cognitive inflexibility has been implicated in both the development and maintenance of eating disorders, and understanding the reasons for this inflexibility might indicate avenues for treatment development. We therefore investigate one potential cause of this inflexibility: an inability to adjust learning when outcome contingencies change. We recruited (n = 82) three groups of participants: those who had recovered from anorexia nervosa (RA), those who had high levels of eating disorder symptoms but no formal diagnosis (EA), and control participants (HC). They performed a reinforcement learning task (alongside eye-tracking) in which the volatility of wins and losses was independently manipulated. We predicted that both the RA and EA groups would adjust their learning rates less than the control participants. Unexpectedly, the RA group showed elevated adjustment of learning rates for both win and loss outcomes compared to control participants. The RA group also showed increased pupil dilation to stable wins and reduced pupil dilation to stable losses. Their learning rate adjustment was associated with the difference between their pupil dilation to volatile vs. stable wins. In conclusion, we find evidence that learning rate adjustment is unexpectedly higher in those who have recovered from anorexia nervosa, indicating that the relationship between eating disorders and cognitive inflexibility may be complex. Given our findings, investigation of noradrenergic agents may be valuable in the field of eating disorders.
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Affiliation(s)
- Alexandra C Pike
- Department of Psychology and York Biomedical Research Institute, University of York, Heslington, York, YO10 5DD, UK.
- Anxiety Laboratory, Neuroscience and Mental Health Group, Institute of Cognitive Neuroscience, University College London, 17-19 Queen Square, London, WC1N 3AR, UK.
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, OX3 7JX, UK.
- Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, UK.
| | - Ann L Sharpley
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, OX3 7JX, UK
- Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, UK
| | - Rebecca J Park
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, OX3 7JX, UK
- Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, UK
| | - Philip J Cowen
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, OX3 7JX, UK
- Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, UK
| | - Michael Browning
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, OX3 7JX, UK
- Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, UK
| | - Erdem Pulcu
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, OX3 7JX, UK
- Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, UK
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13
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Modirshanechi A, Becker S, Brea J, Gerstner W. Surprise and novelty in the brain. Curr Opin Neurobiol 2023; 82:102758. [PMID: 37619425 DOI: 10.1016/j.conb.2023.102758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 06/30/2023] [Accepted: 07/20/2023] [Indexed: 08/26/2023]
Abstract
Notions of surprise and novelty have been used in various experimental and theoretical studies across multiple brain areas and species. However, 'surprise' and 'novelty' refer to different quantities in different studies, which raises concerns about whether these studies indeed relate to the same functionalities and mechanisms in the brain. Here, we address these concerns through a systematic investigation of how different aspects of surprise and novelty relate to different brain functions and physiological signals. We review recent classifications of definitions proposed for surprise and novelty along with links to experimental observations. We show that computational modeling and quantifiable definitions enable novel interpretations of previous findings and form a foundation for future theoretical and experimental studies.
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Affiliation(s)
- Alireza Modirshanechi
- Brain-Mind Institute, School of Life Sciences, EPFL, Lausanne, Switzerland; School of Computer and Communication Sciences, EPFL, Lausanne, Switzerland.
| | - Sophia Becker
- Brain-Mind Institute, School of Life Sciences, EPFL, Lausanne, Switzerland; School of Computer and Communication Sciences, EPFL, Lausanne, Switzerland. https://twitter.com/sophiabecker95
| | - Johanni Brea
- Brain-Mind Institute, School of Life Sciences, EPFL, Lausanne, Switzerland; School of Computer and Communication Sciences, EPFL, Lausanne, Switzerland
| | - Wulfram Gerstner
- Brain-Mind Institute, School of Life Sciences, EPFL, Lausanne, Switzerland; School of Computer and Communication Sciences, EPFL, Lausanne, Switzerland.
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14
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Zika O. The relationship between latent state inference and (intolerance of) uncertainty. Neurosci Biobehav Rev 2023; 152:105321. [PMID: 37459938 DOI: 10.1016/j.neubiorev.2023.105321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 06/22/2023] [Accepted: 07/13/2023] [Indexed: 07/23/2023]
Affiliation(s)
- Ondrej Zika
- Max Planck Research Group NeuroCode, Max Planck Institute for Human Development, Berlin, Germany; Max Planck UCL Centre for Computational Psychiatry and Aging Research, Berlin, Germany, and London, UK.
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15
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Zika O, Wiech K, Reinecke A, Browning M, Schuck NW. Trait anxiety is associated with hidden state inference during aversive reversal learning. Nat Commun 2023; 14:4203. [PMID: 37452030 PMCID: PMC10349120 DOI: 10.1038/s41467-023-39825-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 06/29/2023] [Indexed: 07/18/2023] Open
Abstract
Updating beliefs in changing environments can be driven by gradually adapting expectations or by relying on inferred hidden states (i.e. contexts), and changes therein. Previous work suggests that increased reliance on context could underly fear relapse phenomena that hinder clinical treatment of anxiety disorders. We test whether trait anxiety variations in a healthy population influence how much individuals rely on hidden-state inference. In a Pavlovian learning task, participants observed cues that predicted an upcoming electrical shock with repeatedly changing probability, and were asked to provide expectancy ratings on every trial. We show that trait anxiety is associated with steeper expectation switches after contingency reversals and reduced oddball learning. Furthermore, trait anxiety is related to better fit of a state inference, compared to a gradual learning, model when contingency changes are large. Our findings support previous work suggesting hidden-state inference as a mechanism behind anxiety-related to fear relapse phenomena.
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Affiliation(s)
- Ondrej Zika
- Max Planck Research Group NeuroCode, Max Planck Institute for Human Development, Berlin, Germany.
- Max Planck UCL Centre for Computational Psychiatry and Aging Research, Berlin, Germany.
| | - Katja Wiech
- Wellcome Centre for Integrative Neuroimaging (WIN), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Andrea Reinecke
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Health NHS Trust, Warneford Hospital, Oxford, UK
| | - Michael Browning
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Health NHS Trust, Warneford Hospital, Oxford, UK
| | - Nicolas W Schuck
- Max Planck Research Group NeuroCode, Max Planck Institute for Human Development, Berlin, Germany.
- Max Planck UCL Centre for Computational Psychiatry and Aging Research, Berlin, Germany.
- Institute of Psychology, Universität Hamburg, Hamburg, Germany.
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16
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Rosenblau G, Frolichs K, Korn CW. A neuro-computational social learning framework to facilitate transdiagnostic classification and treatment across psychiatric disorders. Neurosci Biobehav Rev 2023; 149:105181. [PMID: 37062494 PMCID: PMC10236440 DOI: 10.1016/j.neubiorev.2023.105181] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 03/14/2023] [Accepted: 04/13/2023] [Indexed: 04/18/2023]
Abstract
Social deficits are among the core and most striking psychiatric symptoms, present in most psychiatric disorders. Here, we introduce a novel social learning framework, which consists of neuro-computational models that combine reinforcement learning with various types of social knowledge structures. We outline how this social learning framework can help specify and quantify social psychopathology across disorders and provide an overview of the brain regions that may be involved in this type of social learning. We highlight how this framework can specify commonalities and differences in the social psychopathology of individuals with autism spectrum disorder (ASD), personality disorders (PD), and major depressive disorder (MDD) and improve treatments on an individual basis. We conjecture that individuals with psychiatric disorders rely on rigid social knowledge representations when learning about others, albeit the nature of their rigidity and the behavioral consequences can greatly differ. While non-clinical cohorts tend to efficiently adapt social knowledge representations to relevant environmental constraints, psychiatric cohorts may rigidly stick to their preconceived notions or overly coarse knowledge representations during learning.
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Affiliation(s)
- Gabriela Rosenblau
- Department of Psychological and Brain Sciences, George Washington University, Washington DC, USA; Autism and Neurodevelopmental Disorders Institute, George Washington University, Washington DC, USA.
| | - Koen Frolichs
- Section Social Neuroscience, Department of General Psychiatry, University of Heidelberg, Heidelberg, Germany; Institute for Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Christoph W Korn
- Section Social Neuroscience, Department of General Psychiatry, University of Heidelberg, Heidelberg, Germany; Institute for Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
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17
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Woo JH, Aguirre CG, Bari BA, Tsutsui KI, Grabenhorst F, Cohen JY, Schultz W, Izquierdo A, Soltani A. Mechanisms of adjustments to different types of uncertainty in the reward environment across mice and monkeys. COGNITIVE, AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2023; 23:600-619. [PMID: 36823249 PMCID: PMC10444905 DOI: 10.3758/s13415-022-01059-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 12/22/2022] [Indexed: 02/25/2023]
Abstract
Despite being unpredictable and uncertain, reward environments often exhibit certain regularities, and animals navigating these environments try to detect and utilize such regularities to adapt their behavior. However, successful learning requires that animals also adjust to uncertainty associated with those regularities. Here, we analyzed choice data from two comparable dynamic foraging tasks in mice and monkeys to investigate mechanisms underlying adjustments to different types of uncertainty. In these tasks, animals selected between two choice options that delivered reward probabilistically, while baseline reward probabilities changed after a variable number (block) of trials without any cues to the animals. To measure adjustments in behavior, we applied multiple metrics based on information theory that quantify consistency in behavior, and fit choice data using reinforcement learning models. We found that in both species, learning and choice were affected by uncertainty about reward outcomes (in terms of determining the better option) and by expectation about when the environment may change. However, these effects were mediated through different mechanisms. First, more uncertainty about the better option resulted in slower learning and forgetting in mice, whereas it had no significant effect in monkeys. Second, expectation of block switches accompanied slower learning, faster forgetting, and increased stochasticity in choice in mice, whereas it only reduced learning rates in monkeys. Overall, while demonstrating the usefulness of metrics based on information theory in examining adaptive behavior, our study provides evidence for multiple types of adjustments in learning and choice behavior according to uncertainty in the reward environment.
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Affiliation(s)
- Jae Hyung Woo
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Claudia G Aguirre
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Bilal A Bari
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Ken-Ichiro Tsutsui
- Department of Physiology, Development & Neuroscience, University of Cambridge, Cambridge, UK
- Laboratory of Systems Neuroscience, Tohoku University Graduate School of Life Sciences, Sendai, Japan
| | - Fabian Grabenhorst
- Department of Physiology, Development & Neuroscience, University of Cambridge, Cambridge, UK
- Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Jeremiah Y Cohen
- The Solomon H. Snyder Department of Neuroscience, Brain Science Institute, Kavli Neuroscience Discovery Institute, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Allen Institute for Neural Dynamics, Seattle, WA, USA
| | - Wolfram Schultz
- Department of Physiology, Development & Neuroscience, University of Cambridge, Cambridge, UK
| | - Alicia Izquierdo
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA
- The Brain Research Institute, University of California, Los Angeles, Los Angeles, CA, USA
| | - Alireza Soltani
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA.
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18
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Brown VM, Price R, Dombrovski AY. Anxiety as a disorder of uncertainty: implications for understanding maladaptive anxiety, anxious avoidance, and exposure therapy. COGNITIVE, AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2023; 23:844-868. [PMID: 36869259 PMCID: PMC10475148 DOI: 10.3758/s13415-023-01080-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/14/2023] [Indexed: 03/05/2023]
Abstract
In cognitive-behavioral conceptualizations of anxiety, exaggerated threat expectancies underlie maladaptive anxiety. This view has led to successful treatments, notably exposure therapy, but is not consistent with the empirical literature on learning and choice alterations in anxiety. Empirically, anxiety is better described as a disorder of uncertainty learning. How disruptions in uncertainty lead to impairing avoidance and are treated with exposure-based methods, however, is unclear. Here, we integrate concepts from neurocomputational learning models with clinical literature on exposure therapy to propose a new framework for understanding maladaptive uncertainty functioning in anxiety. Specifically, we propose that anxiety disorders are fundamentally disorders of uncertainty learning and that successful treatments, particularly exposure therapy, work by remediating maladaptive avoidance from dysfunctional explore/exploit decisions in uncertain, potentially aversive situations. This framework reconciles several inconsistencies in the literature and provides a path forward to better understand and treat anxiety.
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Affiliation(s)
- Vanessa M Brown
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Rebecca Price
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
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19
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Topel S, Ma I, Sleutels J, van Steenbergen H, de Bruijn ERA, van Duijvenvoorde ACK. Expecting the unexpected: a review of learning under uncertainty across development. COGNITIVE, AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2023:10.3758/s13415-023-01098-0. [PMID: 37237092 PMCID: PMC10390612 DOI: 10.3758/s13415-023-01098-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/28/2023] [Indexed: 05/28/2023]
Abstract
Many of our decisions take place under uncertainty. To successfully navigate the environment, individuals need to estimate the degree of uncertainty and adapt their behaviors accordingly by learning from experiences. However, uncertainty is a broad construct and distinct types of uncertainty may differentially influence our learning. We provide a semi-systematic review to illustrate cognitive and neurobiological processes involved in learning under two types of uncertainty: learning in environments with stochastic outcomes, and with volatile outcomes. We specifically reviewed studies (N = 26 studies) that included an adolescent population, because adolescence is a period in life characterized by heightened exploration and learning, as well as heightened uncertainty due to experiencing many new, often social, environments. Until now, reviews have not comprehensively compared learning under distinct types of uncertainties in this age range. Our main findings show that although the overall developmental patterns were mixed, most studies indicate that learning from stochastic outcomes, as indicated by increased accuracy in performance, improved with age. We also found that adolescents tended to have an advantage compared with adults and children when learning from volatile outcomes. We discuss potential mechanisms explaining these age-related differences and conclude by outlining future research directions.
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Affiliation(s)
- Selin Topel
- Leiden University, Institute of Psychology, Wassenaarseweg 52, 2333, AK, Leiden, The Netherlands.
- Leiden Institute for Brain and Cognition, Leiden, The Netherlands.
| | - Ili Ma
- Leiden University, Institute of Psychology, Wassenaarseweg 52, 2333, AK, Leiden, The Netherlands
- Leiden Institute for Brain and Cognition, Leiden, The Netherlands
| | - Jan Sleutels
- Leiden University, Institute of Psychology, Wassenaarseweg 52, 2333, AK, Leiden, The Netherlands
- Leiden University, Institute for Philosophy, Leiden, The Netherlands
| | - Henk van Steenbergen
- Leiden University, Institute of Psychology, Wassenaarseweg 52, 2333, AK, Leiden, The Netherlands
- Leiden Institute for Brain and Cognition, Leiden, The Netherlands
| | - Ellen R A de Bruijn
- Leiden University, Institute of Psychology, Wassenaarseweg 52, 2333, AK, Leiden, The Netherlands
- Leiden Institute for Brain and Cognition, Leiden, The Netherlands
| | - Anna C K van Duijvenvoorde
- Leiden University, Institute of Psychology, Wassenaarseweg 52, 2333, AK, Leiden, The Netherlands
- Leiden Institute for Brain and Cognition, Leiden, The Netherlands
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20
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Wang Z, Nan T, Goerlich KS, Li Y, Aleman A, Luo Y, Xu P. Neurocomputational mechanisms underlying fear-biased adaptation learning in changing environments. PLoS Biol 2023; 21:e3001724. [PMID: 37126501 PMCID: PMC10174591 DOI: 10.1371/journal.pbio.3001724] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 05/11/2023] [Accepted: 03/31/2023] [Indexed: 05/02/2023] Open
Abstract
Humans are able to adapt to the fast-changing world by estimating statistical regularities of the environment. Although fear can profoundly impact adaptive behaviors, the computational and neural mechanisms underlying this phenomenon remain elusive. Here, we conducted a behavioral experiment (n = 21) and a functional magnetic resonance imaging experiment (n = 37) with a novel cue-biased adaptation learning task, during which we simultaneously manipulated emotional valence (fearful/neutral expressions of the cue) and environmental volatility (frequent/infrequent reversals of reward probabilities). Across 2 experiments, computational modeling consistently revealed a higher learning rate for the environment with frequent versus infrequent reversals following neutral cues. In contrast, this flexible adjustment was absent in the environment with fearful cues, suggesting a suppressive role of fear in adaptation to environmental volatility. This suppressive effect was underpinned by activity of the ventral striatum, hippocampus, and dorsal anterior cingulate cortex (dACC) as well as increased functional connectivity between the dACC and temporal-parietal junction (TPJ) for fear with environmental volatility. Dynamic causal modeling identified that the driving effect was located in the TPJ and was associated with dACC activation, suggesting that the suppression of fear on adaptive behaviors occurs at the early stage of bottom-up processing. These findings provide a neuro-computational account of how fear interferes with adaptation to volatility during dynamic environments.
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Affiliation(s)
- Zhihao Wang
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (BNU), Faculty of Psychology, Beijing Normal University, Beijing, China
- CNRS-Centre d'Economie de la Sorbonne, Panthéon-Sorbonne University, France
| | - Tian Nan
- School of Psychology, Sichuan Center of Applied Psychology, Chengdu Medical College, Chengdu, China
| | - Katharina S Goerlich
- University of Groningen, Department of Biomedical Sciences of Cells & Systems, Section Cognitive Neuroscience, University Medical Center Groningen, Groningen, the Netherlands
| | - Yiman Li
- Shenzhen Key Laboratory of Affective and Social Neuroscience, Magnetic Resonance Imaging, Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen, China
| | - André Aleman
- University of Groningen, Department of Biomedical Sciences of Cells & Systems, Section Cognitive Neuroscience, University Medical Center Groningen, Groningen, the Netherlands
| | - Yuejia Luo
- School of Psychology, Sichuan Center of Applied Psychology, Chengdu Medical College, Chengdu, China
- Shenzhen Key Laboratory of Affective and Social Neuroscience, Magnetic Resonance Imaging, Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen, China
- The State Key Lab of Cognitive and Learning, Faculty of Psychology, Beijing Normal University, Beijing, China
| | - Pengfei Xu
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (BNU), Faculty of Psychology, Beijing Normal University, Beijing, China
- Center for Neuroimaging, Shenzhen Institute of Neuroscience, Shenzhen, China
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21
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Sandhu TR, Xiao B, Lawson RP. Transdiagnostic computations of uncertainty: towards a new lens on intolerance of uncertainty. Neurosci Biobehav Rev 2023; 148:105123. [PMID: 36914079 DOI: 10.1016/j.neubiorev.2023.105123] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 02/21/2023] [Accepted: 03/08/2023] [Indexed: 03/13/2023]
Abstract
People radically differ in how they cope with uncertainty. Clinical researchers describe a dispositional characteristic known as "intolerance of uncertainty", a tendency to find uncertainty aversive, reported to be elevated across psychiatric and neurodevelopmental conditions. Concurrently, recent research in computational psychiatry has leveraged theoretical work to characterise individual differences in uncertainty processing. Under this framework, differences in how people estimate different forms of uncertainty can contribute to mental health difficulties. In this review, we briefly outline the concept of intolerance of uncertainty within its clinical context, and we argue that the mechanisms underlying this construct may be further elucidated through modelling how individuals make inferences about uncertainty. We will review the evidence linking psychopathology to different computationally specified forms of uncertainty and consider how these findings might suggest distinct mechanistic routes towards intolerance of uncertainty. We also discuss the implications of this computational approach for behavioural and pharmacological interventions, as well as the importance of different cognitive domains and subjective experiences in studying uncertainty processing.
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Affiliation(s)
- Timothy R Sandhu
- Department of Psychology, Downing Place, University of Cambridge, CB2 3EB, UK; MRC Cognition and Brain Sciences Unit, 15 Chaucer Road, CB2 7EF, UK.
| | - Bowen Xiao
- Department of Psychology, Downing Place, University of Cambridge, CB2 3EB, UK
| | - Rebecca P Lawson
- Department of Psychology, Downing Place, University of Cambridge, CB2 3EB, UK; MRC Cognition and Brain Sciences Unit, 15 Chaucer Road, CB2 7EF, UK
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22
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Lee JK, Rouault M, Wyart V. Adaptive tuning of human learning and choice variability to unexpected uncertainty. SCIENCE ADVANCES 2023; 9:eadd0501. [PMID: 36989365 PMCID: PMC10058239 DOI: 10.1126/sciadv.add0501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 02/28/2023] [Indexed: 06/19/2023]
Abstract
Human value-based decisions are notably variable under uncertainty. This variability is known to arise from two distinct sources: variable choices aimed at exploring available options and imprecise learning of option values due to limited cognitive resources. However, whether these two sources of decision variability are tuned to their specific costs and benefits remains unclear. To address this question, we compared the effects of expected and unexpected uncertainty on decision-making in the same reinforcement learning task. Across two large behavioral datasets, we found that humans choose more variably between options but simultaneously learn less imprecisely their values in response to unexpected uncertainty. Using simulations of learning agents, we demonstrate that these opposite adjustments reflect adaptive tuning of exploration and learning precision to the structure of uncertainty. Together, these findings indicate that humans regulate not only how much they explore uncertain options but also how precisely they learn the values of these options.
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Affiliation(s)
- Junseok K. Lee
- Laboratoire de Neurosciences Cognitives et Computationnelles, Institut National de la Santé et de la Recherche Médicale (Inserm), Paris, France
- Département d’Études Cognitives, École Normale Supérieure, Université PSL, Paris, France
| | - Marion Rouault
- Laboratoire de Neurosciences Cognitives et Computationnelles, Institut National de la Santé et de la Recherche Médicale (Inserm), Paris, France
- Département d’Études Cognitives, École Normale Supérieure, Université PSL, Paris, France
| | - Valentin Wyart
- Laboratoire de Neurosciences Cognitives et Computationnelles, Institut National de la Santé et de la Recherche Médicale (Inserm), Paris, France
- Département d’Études Cognitives, École Normale Supérieure, Université PSL, Paris, France
- Institut du Psychotraumatisme de l’Enfant et de l’Adolescent, Conseil Départemental Yvelines et Hauts-de-Seine, Versailles, France
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23
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Hein TP, Gong Z, Ivanova M, Fedele T, Nikulin V, Herrojo Ruiz M. Anterior cingulate and medial prefrontal cortex oscillations underlie learning alterations in trait anxiety in humans. Commun Biol 2023; 6:271. [PMID: 36922553 PMCID: PMC10017780 DOI: 10.1038/s42003-023-04628-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 02/27/2023] [Indexed: 03/18/2023] Open
Abstract
Anxiety has been linked to altered belief formation and uncertainty estimation, impacting learning. Identifying the neural processes underlying these changes is important for understanding brain pathology. Here, we show that oscillatory activity in the medial prefrontal, anterior cingulate and orbitofrontal cortex (mPFC, ACC, OFC) explains anxiety-related learning alterations. In a magnetoencephalography experiment, two groups of human participants pre-screened with high and low trait anxiety (HTA, LTA: 39) performed a probabilistic reward-based learning task. HTA undermined learning through an overestimation of volatility, leading to faster belief updating, more stochastic decisions and pronounced lose-shift tendencies. On a neural level, we observed increased gamma activity in the ACC, dmPFC, and OFC during encoding of precision-weighted prediction errors in HTA, accompanied by suppressed ACC alpha/beta activity. Our findings support the association between altered learning and belief updating in anxiety and changes in gamma and alpha/beta activity in the ACC, dmPFC, and OFC.
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Affiliation(s)
- Thomas P Hein
- Goldsmiths, University of London, Psychology Department, Whitehead Building New Cross, London, SE14 6NW, UK
| | - Zheng Gong
- Centre for Cognition and Decision making, Institute for Cognitive Neuroscience, HSE University, Moscow, Russian Federation
| | - Marina Ivanova
- Centre for Cognition and Decision making, Institute for Cognitive Neuroscience, HSE University, Moscow, Russian Federation
| | - Tommaso Fedele
- Centre for Cognition and Decision making, Institute for Cognitive Neuroscience, HSE University, Moscow, Russian Federation
| | - Vadim Nikulin
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Maria Herrojo Ruiz
- Goldsmiths, University of London, Psychology Department, Whitehead Building New Cross, London, SE14 6NW, UK.
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24
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Bounmy T, Eger E, Meyniel F. A characterization of the neural representation of confidence during probabilistic learning. Neuroimage 2023; 268:119849. [PMID: 36640947 DOI: 10.1016/j.neuroimage.2022.119849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 12/09/2022] [Accepted: 12/29/2022] [Indexed: 01/13/2023] Open
Abstract
Learning in a stochastic and changing environment is a difficult task. Models of learning typically postulate that observations that deviate from the learned predictions are surprising and used to update those predictions. Bayesian accounts further posit the existence of a confidence-weighting mechanism: learning should be modulated by the confidence level that accompanies those predictions. However, the neural bases of this confidence are much less known than the ones of surprise. Here, we used a dynamic probability learning task and high-field MRI to identify putative cortical regions involved in the representation of confidence about predictions during human learning. We devised a stringent test based on the conjunction of four criteria. We localized several regions in parietal and frontal cortices whose activity is sensitive to the confidence of an ideal observer, specifically so with respect to potential confounds (surprise and predictability), and in a way that is invariant to which item is predicted. We also tested for functionality in two ways. First, we localized regions whose activity patterns at the subject level showed an effect of both confidence and surprise in qualitative agreement with the confidence-weighting principle. Second, we found neural representations of ideal confidence that also accounted for subjective confidence. Taken together, those results identify a set of cortical regions potentially implicated in the confidence-weighting of learning.
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Affiliation(s)
- Tiffany Bounmy
- Cognitive Neuroimaging Unit, CEA DRF/Joliot, INSERM, Université Paris-Saclay, NeuroSpin Center, Gif-sur-Yvette, France; Université de Paris, Paris, France.
| | - Evelyn Eger
- Cognitive Neuroimaging Unit, CEA DRF/Joliot, INSERM, Université Paris-Saclay, NeuroSpin Center, Gif-sur-Yvette, France
| | - Florent Meyniel
- Cognitive Neuroimaging Unit, CEA DRF/Joliot, INSERM, Université Paris-Saclay, NeuroSpin Center, Gif-sur-Yvette, France.
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25
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Barnby JM, Dayan P, Bell V. Formalising social representation to explain psychiatric symptoms. Trends Cogn Sci 2023; 27:317-332. [PMID: 36609016 DOI: 10.1016/j.tics.2022.12.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 12/09/2022] [Accepted: 12/13/2022] [Indexed: 01/06/2023]
Abstract
Recent work in social cognition has moved beyond a focus on how people process social rewards to examine how healthy people represent other agents and how this is altered in psychiatric disorders. However, formal modelling of social representation has not kept pace with these changes, impeding our understanding of how core aspects of social cognition function, and fail, in psychopathology. Here, we suggest that belief-based computational models provide a basis for an integrated sociocognitive approach to psychiatry, with the potential to address important but unexamined pathologies of social representation, such as maladaptive schemas and illusory social agents.
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Affiliation(s)
- Joseph M Barnby
- Social Computation and Cognitive Representation Lab, Department of Psychology, Royal Holloway, University of London, Egham TW20 0EX, UK.
| | - Peter Dayan
- Max Planck Institute for Biological Cybernetics, Tübingen, 72076, Germany; University of Tübingen, Tübingen, 72074, Germany
| | - Vaughan Bell
- Clinical, Educational, and Health Psychology, University College London, London WC1E 7HB, UK; South London and Maudsley NHS Foundation Trust, London SE5 8AZ, UK
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26
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Heald JB, Lengyel M, Wolpert DM. Contextual inference in learning and memory. Trends Cogn Sci 2023; 27:43-64. [PMID: 36435674 PMCID: PMC9789331 DOI: 10.1016/j.tics.2022.10.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 10/11/2022] [Accepted: 10/12/2022] [Indexed: 11/25/2022]
Abstract
Context is widely regarded as a major determinant of learning and memory across numerous domains, including classical and instrumental conditioning, episodic memory, economic decision-making, and motor learning. However, studies across these domains remain disconnected due to the lack of a unifying framework formalizing the concept of context and its role in learning. Here, we develop a unified vernacular allowing direct comparisons between different domains of contextual learning. This leads to a Bayesian model positing that context is unobserved and needs to be inferred. Contextual inference then controls the creation, expression, and updating of memories. This theoretical approach reveals two distinct components that underlie adaptation, proper and apparent learning, respectively referring to the creation and updating of memories versus time-varying adjustments in their expression. We review a number of extensions of the basic Bayesian model that allow it to account for increasingly complex forms of contextual learning.
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Affiliation(s)
- James B Heald
- Department of Neuroscience, Columbia University, New York, NY 10027, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA.
| | - Máté Lengyel
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK; Center for Cognitive Computation, Department of Cognitive Science, Central European University, Budapest, Hungary.
| | - Daniel M Wolpert
- Department of Neuroscience, Columbia University, New York, NY 10027, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK.
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27
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Trait somatic anxiety is associated with reduced directed exploration and underestimation of uncertainty. Nat Hum Behav 2023; 7:102-113. [PMID: 36192493 DOI: 10.1038/s41562-022-01455-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 08/26/2022] [Indexed: 02/01/2023]
Abstract
Anxiety has been related to decreased physical exploration, but past findings on the interaction between anxiety and exploration during decision making were inconclusive. Here we examined how latent factors of trait anxiety relate to different exploration strategies when facing volatility-induced uncertainty. Across two studies (total N = 985), we demonstrated that people used a hybrid of directed, random and undirected exploration strategies, which were respectively sensitive to relative uncertainty, total uncertainty and value difference. Trait somatic anxiety, that is, the propensity to experience physical symptoms of anxiety, was inversely correlated with directed exploration and undirected exploration, manifesting as a lesser likelihood for choosing the uncertain option and reducing choice stochasticity regardless of uncertainty. Somatic anxiety is also associated with underestimation of relative uncertainty. Together, these results reveal the selective role of trait somatic anxiety in modulating both uncertainty-driven and value-driven exploration strategies.
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Yamamori Y, Robinson OJ. Computational perspectives on human fear and anxiety. Neurosci Biobehav Rev 2023; 144:104959. [PMID: 36375584 PMCID: PMC10564627 DOI: 10.1016/j.neubiorev.2022.104959] [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: 06/13/2022] [Revised: 10/25/2022] [Accepted: 11/09/2022] [Indexed: 11/12/2022]
Abstract
Fear and anxiety are adaptive emotions that serve important defensive functions, yet in excess, they can be debilitating and lead to poor mental health. Computational modelling of behaviour provides a mechanistic framework for understanding the cognitive and neurobiological bases of fear and anxiety, and has seen increasing interest in the field. In this brief review, we discuss recent developments in the computational modelling of human fear and anxiety. Firstly, we describe various reinforcement learning strategies that humans employ when learning to predict or avoid threat, and how these relate to symptoms of fear and anxiety. Secondly, we discuss initial efforts to explore, through a computational lens, approach-avoidance conflict paradigms that are popular in animal research to measure fear- and anxiety-relevant behaviours. Finally, we discuss negative biases in decision-making in the face of uncertainty in anxiety.
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Affiliation(s)
- Yumeya Yamamori
- Institute of Cognitive Neuroscience, University College London, UK.
| | - Oliver J Robinson
- Institute of Cognitive Neuroscience, University College London, UK; Clinical, Educational and Health Psychology, University College London, UK
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29
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Abstract
Learning and interpreting the structure of the environment is an innate feature of biological systems, and is integral to guiding flexible behaviors for evolutionary viability. The concept of a cognitive map has emerged as one of the leading metaphors for these capacities, and unraveling the learning and neural representation of such a map has become a central focus of neuroscience. In recent years, many models have been developed to explain cellular responses in the hippocampus and other brain areas. Because it can be difficult to see how these models differ, how they relate and what each model can contribute, this Review aims to organize these models into a clear ontology. This ontology reveals parallels between existing empirical results, and implies new approaches to understand hippocampal-cortical interactions and beyond.
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30
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Nussenbaum K, Velez JA, Washington BT, Hamling HE, Hartley CA. Flexibility in valenced reinforcement learning computations across development. Child Dev 2022; 93:1601-1615. [PMID: 35596654 PMCID: PMC9831067 DOI: 10.1111/cdev.13791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Optimal integration of positive and negative outcomes during learning varies depending on an environment's reward statistics. The present study investigated the extent to which children, adolescents, and adults (N = 142 8-25 year-olds, 55% female, 42% White, 31% Asian, 17% mixed race, and 8% Black; data collected in 2021) adapt their weighting of better-than-expected and worse-than-expected outcomes when learning from reinforcement. Participants made choices across two contexts: one in which weighting positive outcomes more heavily than negative outcomes led to better performance, and one in which the reverse was true. Reinforcement learning modeling revealed that across age, participants shifted their valence biases in accordance with environmental structure. Exploratory analyses revealed strengthening of context-dependent flexibility with increasing age.
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Affiliation(s)
| | | | | | | | - Catherine A. Hartley
- Corresponding Author: Catherine A. Hartley, Department of Psychology, New York University, 6 Washington Place, Room 871A, New York, NY, 10003.
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Nicholas J, Daw ND, Shohamy D. Uncertainty alters the balance between incremental learning and episodic memory. eLife 2022; 11:81679. [PMID: 36458809 PMCID: PMC9810331 DOI: 10.7554/elife.81679] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 12/01/2022] [Indexed: 12/04/2022] Open
Abstract
A key question in decision-making is how humans arbitrate between competing learning and memory systems to maximize reward. We address this question by probing the balance between the effects, on choice, of incremental trial-and-error learning versus episodic memories of individual events. Although a rich literature has studied incremental learning in isolation, the role of episodic memory in decision-making has only recently drawn focus, and little research disentangles their separate contributions. We hypothesized that the brain arbitrates rationally between these two systems, relying on each in circumstances to which it is most suited, as indicated by uncertainty. We tested this hypothesis by directly contrasting contributions of episodic and incremental influence to decisions, while manipulating the relative uncertainty of incremental learning using a well-established manipulation of reward volatility. Across two large, independent samples of young adults, participants traded these influences off rationally, depending more on episodic information when incremental summaries were more uncertain. These results support the proposal that the brain optimizes the balance between different forms of learning and memory according to their relative uncertainties and elucidate the circumstances under which episodic memory informs decisions.
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Affiliation(s)
- Jonathan Nicholas
- Department of Psychology, Columbia UniversityNew YorkUnited States,Mortimer B. Zuckerman Mind, Brain, Behavior Institute, Columbia UniversityNew YorkUnited States
| | - Nathaniel D Daw
- Department of Psychology, Princeton UniversityPrincetonUnited States,Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Daphna Shohamy
- Department of Psychology, Columbia UniversityNew YorkUnited States,Mortimer B. Zuckerman Mind, Brain, Behavior Institute, Columbia UniversityNew YorkUnited States,The Kavli Institute for Brain Science, Columbia UniversityNew YorkUnited States
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