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Hagan KE, Aimufua I, Haynos AF, Walsh BT. The explore/exploit trade-off: An ecologically valid and translational framework that can advance mechanistic understanding of eating disorders. Int J Eat Disord 2024; 57:1102-1108. [PMID: 38385592 DOI: 10.1002/eat.24173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/26/2024] [Accepted: 02/08/2024] [Indexed: 02/23/2024]
Abstract
The explore/exploit trade-off is a decision-making process that is conserved across species and balances exploring unfamiliar choices of unknown value with choosing familiar options of known value to maximize reward. This framework is rooted in behavioral ecology and has traditionally been used to study maladaptive versus adaptive non-human animal foraging behavior. Researchers have begun to recognize the potential utility of understanding human decision-making and psychopathology through the explore/exploit trade-off. In this article, we propose that explore/exploit trade-off holds promise for advancing our mechanistic understanding of decision-making processes that confer vulnerability for and maintain eating pathology due to its neurodevelopmental bases, conservation across species, and ability to be mathematically modeled. We present a model for how suboptimal explore/exploit decision-making can promote disordered eating and present recommendations for future research applying this framework to eating pathology. Taken together, the explore/exploit trade-off provides a translational framework for expanding etiologic and maintenance models of eating pathology, given developmental changes in explore/exploit decision-making that coincide in time with the emergence of eating pathology and evidence of biased explore/exploit decision-making in psychopathology. Additionally, understanding explore/exploit decision-making in eating disorders may improve knowledge of their underlying pathophysiology, informing targeted clinical interventions such as neuromodulation and pharmacotherapy. PUBLIC SIGNIFICANCE STATEMENT: The explore/exploit trade-off is a cross-species decision-making process whereby organisms choose between a known option with a known reward or sampling unfamiliar options. We hypothesize that imbalanced explore/exploit decision-making can promote disordered eating and present preliminary data. We propose that explore/exploit trade-off has significant potential to advance understanding of the neurocognitive and neurodevelopmental mechanisms of eating pathology, which could ultimately guide revisions of etiologic models and inform novel interventions.
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Affiliation(s)
- Kelsey E Hagan
- Department of Psychiatry, Virginia Commonwealth University, Richmond, Virginia, USA
- Institute for Women's Health, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Ivieosa Aimufua
- Department of Psychiatry, New York State Psychiatric Institute, Columbia University Irving Medical Center, New York, New York, USA
| | - Ann F Haynos
- Department of Psychiatry, Virginia Commonwealth University, Richmond, Virginia, USA
- Department of Psychology, Virginia Commonwealth University, Richmond, Virginia, USA
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, Minnesota, USA
| | - B Timothy Walsh
- Department of Psychiatry, New York State Psychiatric Institute, Columbia University Irving Medical Center, New York, New York, USA
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2
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Paunov A, L'Hôtellier M, Guo D, He Z, Yu A, Meyniel F. Multiple and subject-specific roles of uncertainty in reward-guided decision-making. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.27.587016. [PMID: 38585958 PMCID: PMC10996615 DOI: 10.1101/2024.03.27.587016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Decision-making in noisy, changing, and partially observable environments entails a basic tradeoff between immediate reward and longer-term information gain, known as the exploration-exploitation dilemma. Computationally, an effective way to balance this tradeoff is by leveraging uncertainty to guide exploration. Yet, in humans, empirical findings are mixed, from suggesting uncertainty-seeking to indifference and avoidance. In a novel bandit task that better captures uncertainty-driven behavior, we find multiple roles for uncertainty in human choices. First, stable and psychologically meaningful individual differences in uncertainty preferences actually range from seeking to avoidance, which can manifest as null group-level effects. Second, uncertainty modulates the use of basic decision heuristics that imperfectly exploit immediate rewards: a repetition bias and win-stay-lose-shift heuristic. These heuristics interact with uncertainty, favoring heuristic choices under higher uncertainty. These results, highlighting the rich and varied structure of reward-based choice, are a step to understanding its functional basis and dysfunction in psychopathology.
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Affiliation(s)
- Alexander Paunov
- INSERM-CEA Cognitive Neuroimaging Unit (UNICOG), NeuroSpin Center, CEA Paris-Saclay, Gif-sur-Yvette, France Université de Paris, Paris, France
- Institut de Neuromodulation, GHU Paris, Psychiatrie et Neurosciences, Centre Hospitalier Sainte-Anne, Pôle Hospitalo-universitaire 15, Université Paris Cité, Paris, France
| | - Maëva L'Hôtellier
- INSERM-CEA Cognitive Neuroimaging Unit (UNICOG), NeuroSpin Center, CEA Paris-Saclay, Gif-sur-Yvette, France Université de Paris, Paris, France
| | - Dalin Guo
- Department of Cognitive Science, University of California San Diego, San Diego, CA, USA
| | - Zoe He
- Department of Cognitive Science, University of California San Diego, San Diego, CA, USA
| | - Angela Yu
- Department of Cognitive Science, University of California San Diego, San Diego, CA, USA
- Centre for Cognitive Science & Hessian AI Center, Technical University of Darmstadt, Germany
| | - Florent Meyniel
- INSERM-CEA Cognitive Neuroimaging Unit (UNICOG), NeuroSpin Center, CEA Paris-Saclay, Gif-sur-Yvette, France Université de Paris, Paris, France
- Institut de Neuromodulation, GHU Paris, Psychiatrie et Neurosciences, Centre Hospitalier Sainte-Anne, Pôle Hospitalo-universitaire 15, Université Paris Cité, Paris, France
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3
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Katabi G, Shahar N. Exploring the steps of learning: computational modeling of initiatory-actions among individuals with attention-deficit/hyperactivity disorder. Transl Psychiatry 2024; 14:10. [PMID: 38191535 PMCID: PMC10774270 DOI: 10.1038/s41398-023-02717-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 12/13/2023] [Accepted: 12/14/2023] [Indexed: 01/10/2024] Open
Abstract
Attention-deficit/hyperactivity disorder (ADHD) is characterized by difficulty in acting in a goal-directed manner. While most environments require a sequence of actions for goal attainment, ADHD was never studied in the context of value-based sequence learning. Here, we made use of current advancements in hierarchical reinforcement-learning algorithms to track the internal value and choice policy of individuals with ADHD performing a three-stage sequence learning task. Specifically, 54 participants (28 ADHD, 26 controls) completed a value-based reinforcement-learning task that allowed us to estimate internal action values for each trial and stage using computational modeling. We found attenuated sensitivity to action values in ADHD compared to controls, both in choice and reaction-time variability estimates. Remarkably, this was found only for first-stage actions (i.e., initiatory actions), while for actions performed just before outcome delivery the two groups were strikingly indistinguishable. These results suggest a difficulty in following value estimation for initiatory actions in ADHD.
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Affiliation(s)
- Gili Katabi
- School of Psychological Sciences, Tel Aviv University, Tel Aviv, Israel.
| | - Nitzan Shahar
- School of Psychological Sciences, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
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4
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Giron AP, Ciranka S, Schulz E, van den Bos W, Ruggeri A, Meder B, Wu CM. Developmental changes in exploration resemble stochastic optimization. Nat Hum Behav 2023; 7:1955-1967. [PMID: 37591981 PMCID: PMC10663152 DOI: 10.1038/s41562-023-01662-1] [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: 11/11/2022] [Accepted: 06/21/2023] [Indexed: 08/19/2023]
Abstract
Human development is often described as a 'cooling off' process, analogous to stochastic optimization algorithms that implement a gradual reduction in randomness over time. Yet there is ambiguity in how to interpret this analogy, due to a lack of concrete empirical comparisons. Using data from n = 281 participants ages 5 to 55, we show that cooling off does not only apply to the single dimension of randomness. Rather, human development resembles an optimization process of multiple learning parameters, for example, reward generalization, uncertainty-directed exploration and random temperature. Rapid changes in parameters occur during childhood, but these changes plateau and converge to efficient values in adulthood. We show that while the developmental trajectory of human parameters is strikingly similar to several stochastic optimization algorithms, there are important differences in convergence. None of the optimization algorithms tested were able to discover reliably better regions of the strategy space than adult participants on this task.
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Affiliation(s)
- Anna P Giron
- Human and Machine Cognition Lab, University of Tübingen, Tübingen, Germany
- Attention and Affect Lab, University of Tübingen, Tübingen, Germany
| | - Simon Ciranka
- Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany
| | - Eric Schulz
- MPRG Computational Principles of Intelligence, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Wouter van den Bos
- Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands
- Amsterdam Brain and Cognition, University of Amsterdam, Amsterdam, the Netherlands
| | - Azzurra Ruggeri
- MPRG iSearch, Max Planck Institute for Human Development, Berlin, Germany
- School of Social Sciences and Technology, Technical University Munich, Munich, Germany
- Central European University, Vienna, Austria
| | - Björn Meder
- MPRG iSearch, Max Planck Institute for Human Development, Berlin, Germany
- Institute for Mind, Brain and Behavior, Health and Medical University, Potsdam, Germany
| | - Charley M Wu
- Human and Machine Cognition Lab, University of Tübingen, Tübingen, Germany.
- Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany.
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5
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Lloyd A, Viding E, McKay R, Furl N. Understanding patch foraging strategies across development. Trends Cogn Sci 2023; 27:1085-1098. [PMID: 37500422 DOI: 10.1016/j.tics.2023.07.004] [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: 03/30/2023] [Revised: 07/05/2023] [Accepted: 07/06/2023] [Indexed: 07/29/2023]
Abstract
Patch foraging is a near-ubiquitous behaviour across the animal kingdom and characterises many decision-making domains encountered by humans. We review how a disposition to explore in adolescence may reflect the evolutionary conditions under which hunter-gatherers foraged for resources. We propose that neurocomputational mechanisms responsible for reward processing, learning, and cognitive control facilitate the transition from exploratory strategies in adolescence to exploitative strategies in adulthood - where individuals capitalise on known resources. This developmental transition may be disrupted by psychopathology, as there is emerging evidence of biases in explore/exploit choices in mental health problems. Explore/exploit choices may be an informative marker for mental health across development and future research should consider this feature of decision-making as a target for clinical intervention.
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Affiliation(s)
- Alex Lloyd
- Clinical, Educational, and Health Psychology, Psychology and Language Sciences, University College London, 26 Bedford Way, London, WC1H 0AP, UK.
| | - Essi Viding
- Clinical, Educational, and Health Psychology, Psychology and Language Sciences, University College London, 26 Bedford Way, London, WC1H 0AP, UK
| | - Ryan McKay
- Department of Psychology, Royal Holloway, University of London, Egham Hill, Egham, TW20 0EX, UK
| | - Nicholas Furl
- Department of Psychology, Royal Holloway, University of London, Egham Hill, Egham, TW20 0EX, UK
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Sidorenko N, Chung HK, Grueschow M, Quednow BB, Hayward-Könnecke H, Jetter A, Tobler PN. Acetylcholine and noradrenaline enhance foraging optimality in humans. Proc Natl Acad Sci U S A 2023; 120:e2305596120. [PMID: 37639601 PMCID: PMC10483619 DOI: 10.1073/pnas.2305596120] [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: 04/12/2023] [Accepted: 07/26/2023] [Indexed: 08/31/2023] Open
Abstract
Foraging theory prescribes when optimal foragers should leave the current option for more rewarding alternatives. Actual foragers often exploit options longer than prescribed by the theory, but it is unclear how this foraging suboptimality arises. We investigated whether the upregulation of cholinergic, noradrenergic, and dopaminergic systems increases foraging optimality. In a double-blind, between-subject design, participants (N = 160) received placebo, the nicotinic acetylcholine receptor agonist nicotine, a noradrenaline reuptake inhibitor reboxetine, or a preferential dopamine reuptake inhibitor methylphenidate, and played the role of a farmer who collected milk from patches with different yield. Across all groups, participants on average overharvested. While methylphenidate had no effects on this bias, nicotine, and to some extent also reboxetine, significantly reduced deviation from foraging optimality, which resulted in better performance compared to placebo. Concurring with amplified goal-directedness and excluding heuristic explanations, nicotine independently also improved trial initiation and time perception. Our findings elucidate the neurochemical basis of behavioral flexibility and decision optimality and open unique perspectives on psychiatric disorders affecting these functions.
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Affiliation(s)
- Nick Sidorenko
- Department of Economics, Laboratory for Social and Neural Systems Research, University of Zurich, Zurich8006, Switzerland
- Department of Economics, Zurich Center for Neuroeconomics, University of Zurich, Zurich8006, Switzerland
| | - Hui-Kuan Chung
- Department of Economics, Laboratory for Social and Neural Systems Research, University of Zurich, Zurich8006, Switzerland
- Department of Economics, Zurich Center for Neuroeconomics, University of Zurich, Zurich8006, Switzerland
| | - Marcus Grueschow
- Department of Economics, Laboratory for Social and Neural Systems Research, University of Zurich, Zurich8006, Switzerland
- Department of Economics, Zurich Center for Neuroeconomics, University of Zurich, Zurich8006, Switzerland
| | - Boris B. Quednow
- Experimental and Clinical Pharmacopsychology, Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich8008, Switzerland
- Neuroscience Center Zurich, ETH Zurich and University of Zurich, Zurich8057, Switzerland
| | - Helen Hayward-Könnecke
- Department of Neurology, Section of Neuroimmunology and Multiple Sclerosis Research, University Hospital Zurich, Zurich8091, Switzerland
| | - Alexander Jetter
- National Poisons Information Centre, Tox Info Suisse, Associated Institute of the University of Zurich, Zurich8032, Switzerland
| | - Philippe N. Tobler
- Department of Economics, Laboratory for Social and Neural Systems Research, University of Zurich, Zurich8006, Switzerland
- Department of Economics, Zurich Center for Neuroeconomics, University of Zurich, Zurich8006, Switzerland
- Neuroscience Center Zurich, ETH Zurich and University of Zurich, Zurich8057, Switzerland
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Yan X, Ebitz RB, Grissom N, Darrow DP, Herman AB. A low dimensional manifold of human exploratory behavior reveals opposing roles for apathy and anxiety. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.19.545645. [PMID: 37425723 PMCID: PMC10327047 DOI: 10.1101/2023.06.19.545645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Exploration-exploitation decision-making is a feature of daily life that is altered in a number of neuropsychiatric conditions. Humans display a range of exploration and exploitation behaviors, which can be affected by apathy and anxiety. It remains unknown how factors underlying decision-making generate the spectrum of observed exploration-exploitation behavior and how they relate to states of anxiety and apathy. Here, we report a latent structure underlying sequential exploration and exploitation decisions that explains variation in anxiety and apathy. 1001 participants in a gender-balanced sample completed a three-armed restless bandit task along with psychiatric symptom surveys. Using dimensionality reduction methods, we found that decision sequences reduced to a low-dimensional manifold. The axes of this manifold explained individual differences in the balance between states of exploration and exploitation and the stability of those states, as determined by a statistical mechanics model of decision-making. Position along the balance axis was correlated with opposing symptoms of behavioral apathy and anxiety, while position along the stability axis correlated with the level of emotional apathy. This result resolves a paradox over how these symptoms can be correlated in samples but have opposite effects on behavior. Furthermore, this work provides a basis for using behavioral manifolds to reveal relationships between behavioral dynamics and affective states, with important implications for behavioral measurement approaches to neuropsychiatric conditions.
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8
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Liu XQ, Ji XY, Weng X, Zhang YF. Artificial intelligence ecosystem for computational psychiatry: Ideas to practice. World J Meta-Anal 2023; 11:79-91. [DOI: 10.13105/wjma.v11.i4.79] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 03/18/2023] [Accepted: 04/04/2023] [Indexed: 04/14/2023] Open
Abstract
Computational psychiatry is an emerging field that not only explores the biological basis of mental illness but also considers the diagnoses and identifies the underlying mechanisms. One of the key strengths of computational psychiatry is that it may identify patterns in large datasets that are not easily identifiable. This may help researchers develop more effective treatments and interventions for mental health problems. This paper is a narrative review that reviews the literature and produces an artificial intelligence ecosystem for computational psychiatry. The artificial intelligence ecosystem for computational psychiatry includes data acquisition, preparation, modeling, application, and evaluation. This approach allows researchers to integrate data from a variety of sources, such as brain imaging, genetics, and behavioral experiments, to obtain a more complete understanding of mental health conditions. Through the process of data preprocessing, training, and testing, the data that are required for model building can be prepared. By using machine learning, neural networks, artificial intelligence, and other methods, researchers have been able to develop diagnostic tools that can accurately identify mental health conditions based on a patient’s symptoms and other factors. Despite the continuous development and breakthrough of computational psychiatry, it has not yet influenced routine clinical practice and still faces many challenges, such as data availability and quality, biological risks, equity, and data protection. As we move progress in this field, it is vital to ensure that computational psychiatry remains accessible and inclusive so that all researchers may contribute to this significant and exciting field.
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Affiliation(s)
- Xin-Qiao Liu
- School of Education, Tianjin University, Tianjin 300350, China
| | - Xin-Yu Ji
- School of Education, Tianjin University, Tianjin 300350, China
| | - Xing Weng
- Huzhou Educational Science & Research Center, Huzhou 313000, Zhejiang Province, China
| | - Yi-Fan Zhang
- School of Education, Tianjin University, Tianjin 300350, China
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9
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Wise T, Robinson OJ, Gillan CM. Identifying Transdiagnostic Mechanisms in Mental Health Using Computational Factor Modeling. Biol Psychiatry 2023; 93:690-703. [PMID: 36725393 PMCID: PMC10017264 DOI: 10.1016/j.biopsych.2022.09.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 09/09/2022] [Accepted: 09/27/2022] [Indexed: 02/03/2023]
Abstract
Most psychiatric disorders do not occur in isolation, and most psychiatric symptom dimensions are not uniquely expressed within a single diagnostic category. Current treatments fail to work for around 25% to 40% of individuals, perhaps due at least in part to an overreliance on diagnostic categories in treatment development and allocation. In this review, we describe ongoing efforts in the field to surmount these challenges and precisely characterize psychiatric symptom dimensions using large-scale studies of unselected samples via remote, online, and "citizen science" efforts that take a dimensional, mechanistic approach. We discuss the importance that efforts to identify meaningful psychiatric dimensions be coupled with careful computational modeling to formally specify, test, and potentially falsify candidate mechanisms that underlie transdiagnostic symptom dimensions. We refer to this approach, i.e., where symptom dimensions are identified and validated against computationally well-defined neurocognitive processes, as computational factor modeling. We describe in detail some recent applications of this method to understand transdiagnostic cognitive processes that include model-based planning, metacognition, appetitive processing, and uncertainty estimation. In this context, we highlight how computational factor modeling has been used to identify specific associations between cognition and symptom dimensions and reveal previously obscured relationships, how findings generalize to smaller in-person clinical and nonclinical samples, and how the method is being adapted and optimized beyond its original instantiation. Crucially, we discuss next steps for this area of research, highlighting the value of more direct investigations of treatment response that bridge the gap between basic research and the clinic.
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Affiliation(s)
- Toby Wise
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Oliver J Robinson
- Neuroscience and Mental Health Group, Institute of Cognitive Neuroscience, University College London, London, United Kingdom; Research Department of Clinical Education and Health Psychology, University College London, London, United Kingdom
| | - Claire M Gillan
- School of Psychology, Trinity College Dublin, Dublin 2, Ireland; Global Brain Health Institute, Trinity College Dublin, Dublin 2, Ireland; Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland.
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Cisler JM, Tamman AJF, Fonzo GA. Diminished prospective mental representations of reward mediate reward learning strategies among youth with internalizing symptoms. Psychol Med 2023; 53:1-11. [PMID: 36878892 PMCID: PMC10600826 DOI: 10.1017/s0033291723000478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/09/2023] [Accepted: 02/08/2023] [Indexed: 03/08/2023]
Abstract
BACKGROUND Adolescent internalizing symptoms and trauma exposure have been linked with altered reward learning processes and decreased ventral striatal responses to rewarding cues. Recent computational work on decision-making highlights an important role for prospective representations of the imagined outcomes of different choices. This study tested whether internalizing symptoms and trauma exposure among youth impact the generation of prospective reward representations during decision-making and potentially mediate altered behavioral strategies during reward learning. METHODS Sixty-one adolescent females with varying exposure to interpersonal violence exposure (n = 31 with histories of physical or sexual assault) and severity of internalizing symptoms completed a social reward learning task during fMRI. Multivariate pattern analyses (MVPA) were used to decode neural reward representations at the time of choice. RESULTS MVPA demonstrated that rewarding outcomes could accurately be decoded within several large-scale distributed networks (e.g. frontoparietal and striatum networks), that these reward representations were reactivated prospectively at the time of choice in proportion to the expected probability of receiving reward, and that youth with behavioral strategies that favored exploiting high reward options demonstrated greater prospective generation of reward representations. Youth internalizing symptoms, but not trauma exposure characteristics, were negatively associated with both the behavioral strategy of exploiting high reward options as well as the prospective generation of reward representations in the striatum. CONCLUSIONS These data suggest diminished prospective mental simulation of reward as a mechanism of altered reward learning strategies among youth with internalizing symptoms.
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Affiliation(s)
- Josh M. Cisler
- Department of Psychiatry and Behavioral Sciences, Dell Medical School, University of Texas at Austin, USA
- Institute for Early Life Adversity Research, Dell Medical School, University of Texas at Austin, USA
| | - Amanda J. F. Tamman
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA
| | - Greg A. Fonzo
- Department of Psychiatry and Behavioral Sciences, Dell Medical School, University of Texas at Austin, USA
- Institute for Early Life Adversity Research, Dell Medical School, University of Texas at Austin, USA
- Center for Psychedelic Research and Therapy, Dell Medical School, University of Texas at Austin, USA
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