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Kirschner H, Molla HM, Nassar MR, de Wit H, Ullsperger M. Methamphetamine-induced adaptation of learning rate dynamics depend on baseline performance. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.04.602054. [PMID: 39026741 PMCID: PMC11257491 DOI: 10.1101/2024.07.04.602054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
The ability to calibrate learning according to new information is a fundamental component of an organism's ability to adapt to changing conditions. Yet, the exact neural mechanisms guiding dynamic learning rate adjustments remain unclear. Catecholamines appear to play a critical role in adjusting the degree to which we use new information over time, but individuals vary widely in the manner in which they adjust to changes. Here, we studied the effects of a low dose of methamphetamine (MA), and individual differences in these effects, on probabilistic reversal learning dynamics in a within-subject, double-blind, randomized design. Participants first completed a reversal learning task during a drug-free baseline session to provide a measure of baseline performance. Then they completed the task during two sessions, one with MA (20 mg oral) and one with placebo (PL). First, we showed that, relative to PL, MA modulates the ability to dynamically adjust learning from prediction errors. Second, this effect was more pronounced in participants who performed poorly at baseline. These results present novel evidence for the involvement of catecholaminergic transmission on learning flexibility and highlights that baseline performance modulates the effect of the drug.
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Affiliation(s)
- Hans Kirschner
- Institute of Psychology, Otto-von-Guericke University, D-39106 Magdeburg, Germany
| | - Hanna M Molla
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, Illinois, USA
| | - Matthew R Nassar
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence RI 02912-1821, USA
- Department of Neuroscience, Brown University, Providence RI 02912-1821, USA
| | - Harriet de Wit
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, Illinois, USA
| | - Markus Ullsperger
- Institute of Psychology, Otto-von-Guericke University, D-39106 Magdeburg, Germany
- Center for Behavioral Brain Sciences, D-39106 Magdeburg, Germany
- German Center for Mental Health (DZPG), Center for Intervention and Research on Adaptive and Maladaptive Brain Circuits Underlying Mental Health (C-I-R-C), Halle-Jena-Magdeburg, Germany
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2
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Ullsperger M. Beyond peaks and troughs: Multiplexed performance monitoring signals in the EEG. Psychophysiology 2024; 61:e14553. [PMID: 38415791 DOI: 10.1111/psyp.14553] [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: 12/07/2023] [Revised: 02/08/2024] [Accepted: 02/10/2024] [Indexed: 02/29/2024]
Abstract
With the discovery of event-related potentials elicited by errors more than 30 years ago, a new avenue of research on performance monitoring, cognitive control, and decision making emerged. Since then, the field has developed and expanded fulminantly. After a brief overview on the EEG correlates of performance monitoring, this article reviews recent advancements based on single-trial analyses using independent component analysis, multiple regression, and multivariate pattern classification. Given the close interconnection between performance monitoring and reinforcement learning, computational modeling and model-based EEG analyses have made a particularly strong impact. The reviewed findings demonstrate that error- and feedback-related EEG dynamics represent variables reflecting how performance-monitoring signals are weighted and transformed into an adaptation signal that guides future decisions and actions. The model-based single-trial analysis approach goes far beyond conventional peak-and-trough analyses of event-related potentials and enables testing mechanistic theories of performance monitoring, cognitive control, and decision making.
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Affiliation(s)
- Markus Ullsperger
- Department of Neuropsychology, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
- Center for Behavioral Brain Sciences, Magdeburg, Germany
- German Center for Mental Health (DZPG), partner site Halle-Jena-Magdeburg, Magdeburg, Germany
- Center for Intervention and Research on adaptive and maladaptive brain Circuits underlying mental health (C-I-R-C), Halle-Jena-Magdeburg, Germany
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3
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Mohebi A, Wei W, Pelattini L, Kim K, Berke JD. Dopamine transients follow a striatal gradient of reward time horizons. Nat Neurosci 2024; 27:737-746. [PMID: 38321294 PMCID: PMC11001583 DOI: 10.1038/s41593-023-01566-3] [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: 05/12/2022] [Accepted: 12/21/2023] [Indexed: 02/08/2024]
Abstract
Animals make predictions to guide their behavior and update those predictions through experience. Transient increases in dopamine (DA) are thought to be critical signals for updating predictions. However, it is unclear how this mechanism handles a wide range of behavioral timescales-from seconds or less (for example, if singing a song) to potentially hours or more (for example, if hunting for food). Here we report that DA transients in distinct rat striatal subregions convey prediction errors based on distinct time horizons. DA dynamics systematically accelerated from ventral to dorsomedial to dorsolateral striatum, in the tempo of spontaneous fluctuations, the temporal integration of prior rewards and the discounting of future rewards. This spectrum of timescales for evaluative computations can help achieve efficient learning and adaptive motivation for a broad range of behaviors.
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Affiliation(s)
- Ali Mohebi
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Wei Wei
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Lilian Pelattini
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Kyoungjun Kim
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Joshua D Berke
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA.
- Neuroscience Graduate Program, University of California San Francisco, San Francisco, CA, USA.
- Kavli Institute for Fundamental Neuroscience, University of California San Francisco, San Francisco, CA, USA.
- Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA.
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4
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Muller TH, Butler JL, Veselic S, Miranda B, Wallis JD, Dayan P, Behrens TEJ, Kurth-Nelson Z, Kennerley SW. Distributional reinforcement learning in prefrontal cortex. Nat Neurosci 2024; 27:403-408. [PMID: 38200183 PMCID: PMC10917656 DOI: 10.1038/s41593-023-01535-w] [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: 08/03/2022] [Accepted: 11/29/2023] [Indexed: 01/12/2024]
Abstract
The prefrontal cortex is crucial for learning and decision-making. Classic reinforcement learning (RL) theories center on learning the expectation of potential rewarding outcomes and explain a wealth of neural data in the prefrontal cortex. Distributional RL, on the other hand, learns the full distribution of rewarding outcomes and better explains dopamine responses. In the present study, we show that distributional RL also better explains macaque anterior cingulate cortex neuronal responses, suggesting that it is a common mechanism for reward-guided learning.
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Affiliation(s)
- Timothy H Muller
- Department of Experimental Psychology, University of Oxford, Oxford, UK.
- Department of Clinical and Movement Neurosciences, University College London, London, UK.
| | - James L Butler
- Department of Experimental Psychology, University of Oxford, Oxford, UK
- Department of Clinical and Movement Neurosciences, University College London, London, UK
| | - Sebastijan Veselic
- Department of Experimental Psychology, University of Oxford, Oxford, UK
- Department of Clinical and Movement Neurosciences, University College London, London, UK
- Wellcome Trust Centre for Human Neuroimaging, University College London, London, UK
| | - Bruno Miranda
- Department of Clinical and Movement Neurosciences, University College London, London, UK
- Institute of Physiology and Institute of Molecular Medicine, Lisbon School of Medicine, University of Lisbon, Lisbon, Portugal
| | - Joni D Wallis
- Department of Psychology and Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA
| | - Peter Dayan
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- University of Tübingen, Tübingen, Germany
| | - Timothy E J Behrens
- Wellcome Trust Centre for Human Neuroimaging, University College London, London, UK
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, John Radcliffe Hospital, Oxford, UK
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London, UK
| | - Zeb Kurth-Nelson
- Google DeepMind, London, UK.
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, UK.
| | - Steven W Kennerley
- Department of Experimental Psychology, University of Oxford, Oxford, UK.
- Department of Clinical and Movement Neurosciences, University College London, London, UK.
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, John Radcliffe Hospital, Oxford, UK.
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5
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He Q, Liu JL, Eschapasse L, Zagora AK, Brown TI. The neural correlates of memory integration in value-based decision-making during human spatial navigation. Neuropsychologia 2024; 193:108758. [PMID: 38103679 DOI: 10.1016/j.neuropsychologia.2023.108758] [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: 05/25/2023] [Revised: 09/12/2023] [Accepted: 12/11/2023] [Indexed: 12/19/2023]
Abstract
In daily life, we often make decisions based on relative value of the options, and we often derive these values from segmenting or integrating the outcomes of past episodes in memory. The neural correlates involved in value-based decision-making have been extensively studied in the literature, but few studies have investigated this topic in decisions that require segmenting or integrating episodic memory from related sources, and even fewer studies examine it in the context of spatial navigation. Building on the computational models from our previous studies, the current study investigates the neural substrates involved in decisions that require people either segment or integrate wayfinding outcomes involving different goals, across virtual spatial navigation tasks with differing demands. We find that when decisions require computation of spatial distances for navigation options, but also evaluation of one's prior spatial navigation ability with the task, the estimated value of navigational choices (EV) modulates neural activity in the dorsomedial prefrontal (dmPFC) cortex and ventrolateral prefrontal (vlFPC) cortex. However, superior parietal cortex tracked EV when decision-making tasks only require spatial distance memory but not evaluation of spatial navigation ability. Our findings reveal divergent neural substrates of memory integration in value-based decision-making under different spatial processing demands.
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Affiliation(s)
- Qiliang He
- School of Psychology, Georgia Institute of Technology, USA.
| | - Jancy Ling Liu
- School of Economics, Georgia Institute of Technology, USA
| | - Lou Eschapasse
- School of Psychology, Georgia Institute of Technology, USA
| | - Anna K Zagora
- School of Biological Sciences, Georgia Institute of Technology, USA
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6
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Algermissen J, Swart JC, Scheeringa R, Cools R, den Ouden HEM. Prefrontal signals precede striatal signals for biased credit assignment in motivational learning biases. Nat Commun 2024; 15:19. [PMID: 38168089 PMCID: PMC10762147 DOI: 10.1038/s41467-023-44632-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: 11/17/2021] [Accepted: 12/22/2023] [Indexed: 01/05/2024] Open
Abstract
Actions are biased by the outcomes they can produce: Humans are more likely to show action under reward prospect, but hold back under punishment prospect. Such motivational biases derive not only from biased response selection, but also from biased learning: humans tend to attribute rewards to their own actions, but are reluctant to attribute punishments to having held back. The neural origin of these biases is unclear. Specifically, it remains open whether motivational biases arise primarily from the architecture of subcortical regions or also reflect cortical influences, the latter being typically associated with increased behavioral flexibility and control beyond stereotyped behaviors. Simultaneous EEG-fMRI allowed us to track which regions encoded biased prediction errors in which order. Biased prediction errors occurred in cortical regions (dorsal anterior and posterior cingulate cortices) before subcortical regions (striatum). These results highlight that biased learning is not a mere feature of the basal ganglia, but arises through prefrontal cortical contributions, revealing motivational biases to be a potentially flexible, sophisticated mechanism.
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Affiliation(s)
- Johannes Algermissen
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands.
| | - Jennifer C Swart
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - René Scheeringa
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
- Erwin L. Hahn Institute for Magnetic Resonance Imaging, University of Duisburg-Essen, Essen, Germany
| | - Roshan Cools
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Hanneke E M den Ouden
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands.
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7
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Clairis N, Lopez-Persem A. Debates on the dorsomedial prefrontal/dorsal anterior cingulate cortex: insights for future research. Brain 2023; 146:4826-4844. [PMID: 37530487 PMCID: PMC10690029 DOI: 10.1093/brain/awad263] [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: 02/24/2023] [Revised: 07/19/2023] [Accepted: 07/22/2023] [Indexed: 08/03/2023] Open
Abstract
The dorsomedial prefrontal cortex/dorsal anterior cingulate cortex (dmPFC/dACC) is a brain area subject to many theories and debates over its function(s). Even its precise anatomical borders are subject to much controversy. In the past decades, the dmPFC/dACC has been associated with more than 15 different cognitive processes, which sometimes appear quite unrelated (e.g. body perception, cognitive conflict). As a result, understanding what the dmPFC/dACC does has become a real challenge for many neuroscientists. Several theories of this brain area's function(s) have been developed, leading to successive and competitive publications bearing different models, which sometimes contradict each other. During the last two decades, the lively scientific exchanges around the dmPFC/dACC have promoted fruitful research in cognitive neuroscience. In this review, we provide an overview of the anatomy of the dmPFC/dACC, summarize the state of the art of functions that have been associated with this brain area and present the main theories aiming at explaining the dmPFC/dACC function(s). We explore the commonalities and the arguments between the different theories. Finally, we explain what can be learned from these debates for future investigations of the dmPFC/dACC and other brain regions' functions.
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Affiliation(s)
- Nicolas Clairis
- Laboratory of Behavioral Genetics (LGC)- Brain Mind Institute (BMI)- Sciences de la Vie (SV), École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Alizée Lopez-Persem
- FrontLab, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, Sorbonne University, AP HP, Hôpital de la Pitié Salpêtrière, 75013 Paris, France
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8
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Danskin BP, Hattori R, Zhang YE, Babic Z, Aoi M, Komiyama T. Exponential history integration with diverse temporal scales in retrosplenial cortex supports hyperbolic behavior. SCIENCE ADVANCES 2023; 9:eadj4897. [PMID: 38019904 PMCID: PMC10686558 DOI: 10.1126/sciadv.adj4897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 10/27/2023] [Indexed: 12/01/2023]
Abstract
Animals use past experience to guide future choices. The integration of experiences typically follows a hyperbolic, rather than exponential, decay pattern with a heavy tail for distant history. Hyperbolic integration affords sensitivity to both recent environmental dynamics and long-term trends. However, it is unknown how the brain implements hyperbolic integration. We found that mouse behavior in a foraging task showed hyperbolic decay of past experience, but the activity of cortical neurons showed exponential decay. We resolved this apparent mismatch by observing that cortical neurons encode history information with heterogeneous exponential time constants that vary across neurons. A model combining these diverse timescales recreated the heavy-tailed, hyperbolic history integration observed in behavior. In particular, the time constants of retrosplenial cortex (RSC) neurons best matched the behavior, and optogenetic inactivation of RSC uniquely reduced behavioral history dependence. These results indicate that behavior-relevant history information is maintained across multiple timescales in parallel and that RSC is a critical reservoir of information guiding decision-making.
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Affiliation(s)
- Bethanny P. Danskin
- Department of Neurobiology, University of California San Diego, La Jolla, CA, USA
- Center for Neural Circuits and Behavior, University of California San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA
| | - Ryoma Hattori
- Department of Neurobiology, University of California San Diego, La Jolla, CA, USA
- Center for Neural Circuits and Behavior, University of California San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA
| | - Yu E. Zhang
- Department of Neurobiology, University of California San Diego, La Jolla, CA, USA
- Center for Neural Circuits and Behavior, University of California San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA
| | - Zeljana Babic
- Department of Neurobiology, University of California San Diego, La Jolla, CA, USA
- Center for Neural Circuits and Behavior, University of California San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA
| | - Mikio Aoi
- Department of Neurobiology, University of California San Diego, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA
| | - Takaki Komiyama
- Department of Neurobiology, University of California San Diego, La Jolla, CA, USA
- Center for Neural Circuits and Behavior, University of California San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA
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9
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Trudel N, Lockwood PL, Rushworth MFS, Wittmann MK. Neural activity tracking identity and confidence in social information. eLife 2023; 12:71315. [PMID: 36763582 PMCID: PMC9917428 DOI: 10.7554/elife.71315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 12/15/2022] [Indexed: 02/11/2023] Open
Abstract
Humans learn about the environment either directly by interacting with it or indirectly by seeking information about it from social sources such as conspecifics. The degree of confidence in the information obtained through either route should determine the impact that it has on adapting and changing behaviour. We examined whether and how behavioural and neural computations differ during non-social learning as opposed to learning from social sources. Trial-wise confidence judgements about non-social and social information sources offered a window into this learning process. Despite matching exactly the statistical features of social and non-social conditions, confidence judgements were more accurate and less changeable when they were made about social as opposed to non-social information sources. In addition to subjective reports of confidence, differences were also apparent in the Bayesian estimates of participants' subjective beliefs. Univariate activity in dorsomedial prefrontal cortex and posterior temporoparietal junction more closely tracked confidence about social as opposed to non-social information sources. In addition, the multivariate patterns of activity in the same areas encoded identities of social information sources compared to non-social information sources.
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Affiliation(s)
- Nadescha Trudel
- Wellcome Centre of Integrative Neuroimaging (WIN), Department of Experimental Psychology, University of OxfordOxfordUnited Kingdom
- Wellcome Centre for Human Neuroimaging, University College LondonLondonUnited Kingdom
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College LondonLondonUnited Kingdom
| | - Patricia L Lockwood
- Centre for Human Brain Health, School of Psychology, University of BirminghamBirminghamUnited Kingdom
- Institute for Mental Health, School of Psychology, University of BirminghamBirminghamUnited Kingdom
- Centre for Developmental Science, School of Psychology, University of BirminghamBirminghamUnited Kingdom
| | - Matthew FS Rushworth
- Wellcome Centre of Integrative Neuroimaging (WIN), Department of Experimental Psychology, University of OxfordOxfordUnited Kingdom
- Wellcome Centre of Integrative Neuroimaging (WIN), Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of OxfordOxfordUnited Kingdom
| | - Marco K Wittmann
- Wellcome Centre of Integrative Neuroimaging (WIN), Department of Experimental Psychology, University of OxfordOxfordUnited Kingdom
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College LondonLondonUnited Kingdom
- Department of Experimental Psychology, University College LondonLondonUnited Kingdom
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10
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Functional imaging analyses reveal prototype and exemplar representations in a perceptual single-category task. Commun Biol 2022; 5:896. [PMID: 36050393 PMCID: PMC9437087 DOI: 10.1038/s42003-022-03858-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 08/16/2022] [Indexed: 11/16/2022] Open
Abstract
Similarity-based categorization can be performed by memorizing category members as exemplars or by abstracting the central tendency of the category – the prototype. In similarity-based categorization of stimuli with clearly identifiable dimensions from two categories, prototype representations were previously located in the hippocampus and the ventromedial prefrontal cortex (vmPFC) and exemplar representations in areas supporting visual memory. However, the neural implementation of exemplar and prototype representations in perceptual similarity-based categorization of single categories is unclear. To investigate these representations, we applied model-based univariate and multivariate analyses of functional imaging data from a dot-pattern paradigm-based task. Univariate prototype and exemplar representations occurred bilaterally in visual areas. Multivariate analyses additionally identified prototype representations in parietal areas and exemplar representations in the hippocampus. Bayesian analyses supported the non-presence of prototype representations in the hippocampus and the vmPFC. We additionally demonstrate that some individuals form both representation types simultaneously, probably granting flexibility in categorization strategies. Model-based univariate and multivariate analyses of fMRI data from 62 healthy participants in a dot-pattern paradigm-based task provide further insight into the neural basis of similarity-based categorization.
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11
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Human inference reflects a normative balance of complexity and accuracy. Nat Hum Behav 2022; 6:1153-1168. [PMID: 35637296 PMCID: PMC9446026 DOI: 10.1038/s41562-022-01357-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Accepted: 04/20/2022] [Indexed: 02/03/2023]
Abstract
We must often infer latent properties of the world from noisy and changing observations. Complex, probabilistic approaches to this challenge such as Bayesian inference are accurate but cognitively demanding, relying on extensive working memory and adaptive processing. Simple heuristics are easy to implement but may be less accurate. What is the appropriate balance between complexity and accuracy? Here we model a hierarchy of strategies of variable complexity and find a power law of diminishing returns: increasing complexity gives progressively smaller gains in accuracy. The rate of diminishing returns depends systematically on the statistical uncertainty in the world, such that complex strategies do not provide substantial benefits over simple ones when uncertainty is either too high or too low. In between, there is a complexity dividend. In two psychophysical experiments, we confirm specific model predictions about how working memory and adaptivity should be modulated by uncertainty.
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12
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Klein-Flügge MC, Bongioanni A, Rushworth MFS. Medial and orbital frontal cortex in decision-making and flexible behavior. Neuron 2022; 110:2743-2770. [PMID: 35705077 DOI: 10.1016/j.neuron.2022.05.022] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 05/17/2022] [Accepted: 05/19/2022] [Indexed: 11/15/2022]
Abstract
The medial frontal cortex and adjacent orbitofrontal cortex have been the focus of investigations of decision-making, behavioral flexibility, and social behavior. We review studies conducted in humans, macaques, and rodents and argue that several regions with different functional roles can be identified in the dorsal anterior cingulate cortex, perigenual anterior cingulate cortex, anterior medial frontal cortex, ventromedial prefrontal cortex, and medial and lateral parts of the orbitofrontal cortex. There is increasing evidence that the manner in which these areas represent the value of the environment and specific choices is different from subcortical brain regions and more complex than previously thought. Although activity in some regions reflects distributions of reward and opportunities across the environment, in other cases, activity reflects the structural relationships between features of the environment that animals can use to infer what decision to take even if they have not encountered identical opportunities in the past.
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Affiliation(s)
- Miriam C Klein-Flügge
- Wellcome Centre for Integrative Neuroimaging (WIN), Department of Experimental Psychology, University of Oxford, Tinsley Building, Mansfield Road, Oxford OX1 3TA, UK; Wellcome Centre for Integrative Neuroimaging (WIN), Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Nuffield Department of Clinical Neurosciences, Level 6, West Wing, John Radcliffe Hospital, Oxford OX3 9DU, UK; Department of Psychiatry, University of Oxford, Warneford Lane, Headington, Oxford OX3 7JX, UK.
| | - Alessandro Bongioanni
- Wellcome Centre for Integrative Neuroimaging (WIN), Department of Experimental Psychology, University of Oxford, Tinsley Building, Mansfield Road, Oxford OX1 3TA, UK
| | - Matthew F S Rushworth
- Wellcome Centre for Integrative Neuroimaging (WIN), Department of Experimental Psychology, University of Oxford, Tinsley Building, Mansfield Road, Oxford OX1 3TA, UK; Wellcome Centre for Integrative Neuroimaging (WIN), Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Nuffield Department of Clinical Neurosciences, Level 6, West Wing, John Radcliffe Hospital, Oxford OX3 9DU, UK
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13
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Fontanier V, Sarazin M, Stoll FM, Delord B, Procyk E. Inhibitory control of frontal metastability sets the temporal signature of cognition. eLife 2022; 11:63795. [PMID: 35635439 PMCID: PMC9200403 DOI: 10.7554/elife.63795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 05/27/2022] [Indexed: 11/13/2022] Open
Abstract
Cortical dynamics are organized over multiple anatomical and temporal scales. The mechanistic origin of the temporal organization and its contribution to cognition remain unknown. Here we demonstrate the cause of this organization by studying a specific temporal signature (time constant and latency) of neural activity. In monkey frontal areas, recorded during flexible decisions, temporal signatures display specific area-dependent ranges, as well as anatomical and cell-type distributions. Moreover, temporal signatures are functionally adapted to behaviorally relevant timescales. Fine-grained biophysical network models, constrained to account for experimentally observed temporal signatures, reveal that after-hyperpolarization potassium and inhibitory GABA-B conductances critically determine areas' specificity. They mechanistically account for temporal signatures by organizing activity into metastable states, with inhibition controlling state stability and transitions. As predicted by models, state durations non-linearly scale with temporal signatures in monkey, matching behavioral timescales. Thus, local inhibitory-controlled metastability constitutes the dynamical core specifying the temporal organization of cognitive functions in frontal areas.
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Affiliation(s)
| | - Matthieu Sarazin
- Institute of Intelligent Systems and Robotics (ISIR) - UMR 7222, Sorbonne Université, CNRS, Paris, France
| | - Frederic M Stoll
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, United States
| | - Bruno Delord
- Institute of Intelligent Systems and Robotics (ISIR) - UMR 7222, Sorbonne Université, CNRS, Paris, France
| | - Emmanuel Procyk
- Stem Cell and Brain Research Institute U1208, Inserm, Lyon, France
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14
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Puelma Touzel M, Cisek P, Lajoie G. Performance-gated deliberation: A context-adapted strategy in which urgency is opportunity cost. PLoS Comput Biol 2022; 18:e1010080. [PMID: 35617370 PMCID: PMC9176815 DOI: 10.1371/journal.pcbi.1010080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 06/08/2022] [Accepted: 04/05/2022] [Indexed: 11/18/2022] Open
Abstract
Finding the right amount of deliberation, between insufficient and excessive, is a hard decision making problem that depends on the value we place on our time. Average-reward, putatively encoded by tonic dopamine, serves in existing reinforcement learning theory as the opportunity cost of time, including deliberation time. Importantly, this cost can itself vary with the environmental context and is not trivial to estimate. Here, we propose how the opportunity cost of deliberation can be estimated adaptively on multiple timescales to account for non-stationary contextual factors. We use it in a simple decision-making heuristic based on average-reward reinforcement learning (AR-RL) that we call Performance-Gated Deliberation (PGD). We propose PGD as a strategy used by animals wherein deliberation cost is implemented directly as urgency, a previously characterized neural signal effectively controlling the speed of the decision-making process. We show PGD outperforms AR-RL solutions in explaining behaviour and urgency of non-human primates in a context-varying random walk prediction task and is consistent with relative performance and urgency in a context-varying random dot motion task. We make readily testable predictions for both neural activity and behaviour.
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Affiliation(s)
- Maximilian Puelma Touzel
- Mila, Québec AI Institute, Montréal, Canada
- Department of Computer Science & Operations Research, Université de Montréal, Montréal, Canada
- * E-mail:
| | - Paul Cisek
- Department of Neuroscience, Université de Montréal, Montréal, Canada
| | - Guillaume Lajoie
- Mila, Québec AI Institute, Montréal, Canada
- Department of Mathematics & Statistics, Université de Montréal, Montréal, Canada
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15
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Rational arbitration between statistics and rules in human sequence processing. Nat Hum Behav 2022; 6:1087-1103. [PMID: 35501360 DOI: 10.1038/s41562-021-01259-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 11/17/2021] [Indexed: 01/29/2023]
Abstract
Detecting and learning temporal regularities is essential to accurately predict the future. A long-standing debate in cognitive science concerns the existence in humans of a dissociation between two systems, one for handling statistical regularities governing the probabilities of individual items and their transitions, and another for handling deterministic rules. Here, to address this issue, we used finger tracking to continuously monitor the online build-up of evidence, confidence, false alarms and changes-of-mind during sequence processing. All these aspects of behaviour conformed tightly to a hierarchical Bayesian inference model with distinct hypothesis spaces for statistics and rules, yet linked by a single probabilistic currency. Alternative models based either on a single statistical mechanism or on two non-commensurable systems were rejected. Our results indicate that a hierarchical Bayesian inference mechanism, capable of operating over distinct hypothesis spaces for statistics and rules, underlies the human capability for sequence processing.
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16
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Monosov IE, Rushworth MFS. Interactions between ventrolateral prefrontal and anterior cingulate cortex during learning and behavioural change. Neuropsychopharmacology 2022; 47:196-210. [PMID: 34234288 PMCID: PMC8617208 DOI: 10.1038/s41386-021-01079-2] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 05/27/2021] [Accepted: 06/15/2021] [Indexed: 02/06/2023]
Abstract
Hypotheses and beliefs guide credit assignment - the process of determining which previous events or actions caused an outcome. Adaptive hypothesis formation and testing are crucial in uncertain and changing environments in which associations and meanings are volatile. Despite primates' abilities to form and test hypotheses, establishing what is causally responsible for the occurrence of particular outcomes remains a fundamental challenge for credit assignment and learning. Hypotheses about what surprises are due to stochasticity inherent in an environment as opposed to real, systematic changes are necessary for identifying the environment's predictive features, but are often hard to test. We review evidence that two highly interconnected frontal cortical regions, anterior cingulate cortex and ventrolateral prefrontal area 47/12o, provide a biological substrate for linking two crucial components of hypothesis-formation and testing: the control of information seeking and credit assignment. Neuroimaging, targeted disruptions, and neurophysiological studies link an anterior cingulate - 47/12o circuit to generation of exploratory behaviour, non-instrumental information seeking, and interpretation of subsequent feedback in the service of credit assignment. Our observations support the idea that information seeking and credit assignment are linked at the level of neural circuits and explain why this circuit is important for ensuring behaviour is flexible and adaptive.
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Affiliation(s)
- Ilya E Monosov
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, USA.
- Department of Biomedical Engineering, Washington University, St. Louis, MO, USA.
- Department of Electrical Engineering, Washington University, St. Louis, MO, USA.
- Department of Neurosurgery, Washington University, St. Louis, MO, USA.
- Pain Center, Washington University, St. Louis, MO, USA.
| | - Matthew F S Rushworth
- Wellcome Centre for Integrative Neuroimaging (WIN), Department of Experimental Psychology, University of Oxford, Oxford, UK.
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17
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Rudebeck PH, Izquierdo A. Foraging with the frontal cortex: A cross-species evaluation of reward-guided behavior. Neuropsychopharmacology 2022; 47:134-146. [PMID: 34408279 PMCID: PMC8617092 DOI: 10.1038/s41386-021-01140-0] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 07/30/2021] [Accepted: 07/30/2021] [Indexed: 02/07/2023]
Abstract
Efficient foraging is essential to survival and depends on frontal cortex in mammals. Because of its role in psychiatric disorders, frontal cortex and its contributions to reward procurement have been studied extensively in both rodents and non-human primates. How frontal cortex of these animal models compares is a source of intense debate. Here we argue that translating findings from rodents to non-human primates requires an appreciation of both the niche in which each animal forages as well as the similarities in frontal cortex anatomy and function. Consequently, we highlight similarities and differences in behavior and anatomy, before focusing on points of convergence in how parts of frontal cortex contribute to distinct aspects of foraging in rats and macaques, more specifically. In doing so, our aim is to emphasize where translation of frontal cortex function between species is clearer, where there is divergence, and where future work should focus. We finish by highlighting aspects of foraging for which have received less attention but we believe are critical to uncovering how frontal cortex promotes survival in each species.
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Affiliation(s)
| | - Alicia Izquierdo
- Department of Psychology, UCLA, Los Angeles, CA, USA.
- The Brain Research Institute, UCLA, Los Angeles, CA, USA.
- Integrative Center for Learning and Memory, UCLA, Los Angeles, CA, USA.
- Integrative Center for Addictions, UCLA, Los Angeles, CA, USA.
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18
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Panizza F, Vostroknutov A, Coricelli G. How conformity can lead to polarised social behaviour. PLoS Comput Biol 2021; 17:e1009530. [PMID: 34669694 PMCID: PMC8559952 DOI: 10.1371/journal.pcbi.1009530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 11/01/2021] [Accepted: 10/05/2021] [Indexed: 11/18/2022] Open
Abstract
Learning social behaviour of others strongly influences one’s own social attitudes. We compare several distinct explanations of this phenomenon, testing their predictions using computational modelling across four experimental conditions. In the experiment, participants chose repeatedly whether to pay for increasing (prosocial) or decreasing (antisocial) the earnings of an unknown other. Halfway through the task, participants predicted the choices of an extremely prosocial or antisocial agent (either a computer, a single participant, or a group of participants). Our analyses indicate that participants polarise their social attitude mainly due to normative expectations. Specifically, most participants conform to presumed demands by the authority (vertical influence), or because they learn that the observed human agents follow the norm very closely (horizontal influence). What drives people to extreme acts of generosity? What causes behaviour that is unduly spiteful? This study explored how our social decisions polarise. Participants chose whether to spend money to increase or decrease the earnings of an unknown person. Halfway through this task, they observed another agent playing. The agent took participants’ choices to the extremes: if for instance the participant was moderately generous, it spent considerable sums to help the other. Participants conformed regardless of whether the agent was a computer algorithm, a person, or a group of people. We tested several competing explanations of why this happened with the help of cognitive modelling. Our analyses identify two factors behind polarisation: willingness to comply with the experimenter expectations (social desirability), and concern about appropriate behaviour (norm conformity). Our approach provided insight into how social choices are influenced by others, and could be applied in the study of conformity in other types of decisions.
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Affiliation(s)
- Folco Panizza
- Molecular Mind Laboratory, IMT School for Advanced Studies Lucca, Italy
- Center for Mind/Brain Sciences, University of Trento, Mattarello (TN), Italy
- * E-mail:
| | | | - Giorgio Coricelli
- Department of Economics, University of Southern California, Los Angeles, California, United States of America
- LaPsyDÉ, UMR CNRS 8240, La Sorbonne, Paris, France
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19
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Wang S, Feng SF, Bornstein AM. Mixing memory and desire: How memory reactivation supports deliberative decision-making. WILEY INTERDISCIPLINARY REVIEWS. COGNITIVE SCIENCE 2021; 13:e1581. [PMID: 34665529 DOI: 10.1002/wcs.1581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 08/24/2021] [Accepted: 09/16/2021] [Indexed: 11/09/2022]
Abstract
Memories affect nearly every aspect of our mental life. They allow us to both resolve uncertainty in the present and to construct plans for the future. Recently, renewed interest in the role memory plays in adaptive behavior has led to new theoretical advances and empirical observations. We review key findings, with particular emphasis on how the retrieval of many kinds of memories affects deliberative action selection. These results are interpreted in a sequential inference framework, in which reinstatements from memory serve as "samples" of potential action outcomes. The resulting model suggests a central role for the dynamics of memory reactivation in determining the influence of different kinds of memory in decisions. We propose that representation-specific dynamics can implement a bottom-up "product of experts" rule that integrates multiple sets of action-outcome predictions weighted based on their uncertainty. We close by reviewing related findings and identifying areas for further research. This article is categorized under: Psychology > Reasoning and Decision Making Neuroscience > Cognition Neuroscience > Computation.
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Affiliation(s)
- Shaoming Wang
- Department of Psychology, New York University, New York, New York, USA
| | - Samuel F Feng
- Department of Mathematics, Khalifa University of Science and Technology, Abu Dhabi, UAE.,Khalifa University Centre for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, UAE
| | - Aaron M Bornstein
- Department of Cognitive Sciences, University of California-Irvine, Irvine, California, USA.,Center for the Neurobiology of Learning & Memory, University of California-Irvine, Irvine, California, USA.,Institute for Mathematical Behavioral Sciences, University of California-Irvine, Irvine, California, USA
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20
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Choice history effects in mice and humans improve reward harvesting efficiency. PLoS Comput Biol 2021; 17:e1009452. [PMID: 34606493 PMCID: PMC8516315 DOI: 10.1371/journal.pcbi.1009452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 10/14/2021] [Accepted: 09/15/2021] [Indexed: 12/04/2022] Open
Abstract
Choice history effects describe how future choices depend on the history of past choices. In experimental tasks this is typically framed as a bias because it often diminishes the experienced reward rates. However, in natural habitats, choices made in the past constrain choices that can be made in the future. For foraging animals, the probability of earning a reward in a given patch depends on the degree to which the animals have exploited the patch in the past. One problem with many experimental tasks that show choice history effects is that such tasks artificially decouple choice history from its consequences on reward availability over time. To circumvent this, we use a variable interval (VI) reward schedule that reinstates a more natural contingency between past choices and future reward availability. By examining the behavior of optimal agents in the VI task we discover that choice history effects observed in animals serve to maximize reward harvesting efficiency. We further distil the function of choice history effects by manipulating first- and second-order statistics of the environment. We find that choice history effects primarily reflect the growth rate of the reward probability of the unchosen option, whereas reward history effects primarily reflect environmental volatility. Based on observed choice history effects in animals, we develop a reinforcement learning model that explicitly incorporates choice history over multiple time scales into the decision process, and we assess its predictive adequacy in accounting for the associated behavior. We show that this new variant, known as the double trace model, has a higher performance in predicting choice data, and shows near optimal reward harvesting efficiency in simulated environments. These results suggests that choice history effects may be adaptive for natural contingencies between consumption and reward availability. This concept lends credence to a normative account of choice history effects that extends beyond its description as a bias. Animals foraging for food in natural habitats compete to obtain better quality food patches. To achieve this goal, animals can rely on memory and choose the same patches that have provided higher quality of food in the past. However, in natural habitats simply identifying better food patches may not be sufficient to successfully compete with their conspecifics, as food resources can grow over time. Therefore, it makes sense to visit from time to time those patches that were associated with lower food quality in the past. This demands optimal foraging animals to keep in memory not only which food patches provided the best food quality, but also which food patches they visited recently. To see if animals track their history of visits and use it to maximize the food harvesting efficiency, we subjected them to experimental conditions that mimicked natural foraging behavior. In our behavioral tasks, we replaced food foraging behavior with a two choice task that provided rewards to mice and humans. By developing a new computational model and subjecting animals to various behavioral manipulations, we demonstrate that keeping a memory of past visits helps the animals to optimize the efficiency with which they can harvest rewards.
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21
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Abstract
We live in a world that changes on many timescales. To learn and make decisions appropriately, the human brain has evolved to integrate various types of information, such as sensory evidence and reward feedback, on multiple timescales. This is reflected in cortical hierarchies of timescales consisting of heterogeneous neuronal activities and expression of genes related to neurotransmitters critical for learning. We review the recent findings on how timescales of sensory and reward integration are affected by the temporal properties of sensory and reward signals in the environment. Despite existing evidence linking behavioral and neuronal timescales, future studies must examine how neural computations at multiple timescales are adjusted and combined to influence behavior flexibly.
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Affiliation(s)
- Alireza Soltani
- Department of Psychological and Brain Sciences, Dartmouth College, Moore Hall, 3 Maynard St, Hanover, NH 03755
| | - John D. Murray
- Department of Psychiatry, Yale School of Medicine, 300 George Street, New Haven, CT 06511
| | - Hyojung Seo
- Department of Psychiatry, Yale School of Medicine, 300 George Street, New Haven, CT 06511
| | - Daeyeol Lee
- The Zanvyl Krieger Mind/Brain Institute, Department of Neuroscience, Department of Psychological Sciences, Kavli Neuroscience Discovery Institute, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218
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22
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Müller T, Klein-Flügge MC, Manohar SG, Husain M, Apps MAJ. Neural and computational mechanisms of momentary fatigue and persistence in effort-based choice. Nat Commun 2021; 12:4593. [PMID: 34321478 PMCID: PMC8319292 DOI: 10.1038/s41467-021-24927-7] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Accepted: 07/13/2021] [Indexed: 11/09/2022] Open
Abstract
From a gym workout, to deciding whether to persevere at work, many activities require us to persist in deciding that rewards are ‘worth the effort’ even as we become fatigued. However, studies examining effort-based decisions typically assume that the willingness to work is static. Here, we use computational modelling on two effort-based tasks, one behavioural and one during fMRI. We show that two hidden states of fatigue fluctuate on a moment-to-moment basis on different timescales but both reduce the willingness to exert effort for reward. The value of one state increases after effort but is ‘recoverable’ by rests, whereas a second ‘unrecoverable’ state gradually increases with work. The BOLD response in separate medial and lateral frontal sub-regions covaried with these states when making effort-based decisions, while a distinct fronto-striatal system integrated fatigue with value. These results provide a computational framework for understanding the brain mechanisms of persistence and momentary fatigue. The willingness to exert effort into demanding tasks often declines over time through fatigue. Here the authors provide a computational account of the moment-to-moment dynamics of fatigue and its impact on effort-based choices, and reveal the neural mechanisms that underlie such computations.
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Affiliation(s)
- Tanja Müller
- Department of Experimental Psychology, University of Oxford, Oxford, UK. .,Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK.
| | - Miriam C Klein-Flügge
- Department of Experimental Psychology, University of Oxford, Oxford, UK.,Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Sanjay G Manohar
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK.,Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Masud Husain
- Department of Experimental Psychology, University of Oxford, Oxford, UK.,Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK.,Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Matthew A J Apps
- Department of Experimental Psychology, University of Oxford, Oxford, UK. .,Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK. .,Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK. .,Institute for Mental Health, School of Psychology, University of Birmingham, Birmingham, UK.
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23
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Miyamoto K, Trudel N, Kamermans K, Lim MC, Lazari A, Verhagen L, Wittmann MK, Rushworth MFS. Identification and disruption of a neural mechanism for accumulating prospective metacognitive information prior to decision-making. Neuron 2021; 109:1396-1408.e7. [PMID: 33730554 PMCID: PMC8063717 DOI: 10.1016/j.neuron.2021.02.024] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 01/13/2021] [Accepted: 02/19/2021] [Indexed: 12/11/2022]
Abstract
More than one type of probability must be considered when making decisions. It is as necessary to know one's chance of performing choices correctly as it is to know the chances that desired outcomes will follow choices. We refer to these two choice contingencies as internal and external probability. Neural activity across many frontal and parietal areas reflected internal and external probabilities in a similar manner during decision-making. However, neural recording and manipulation approaches suggest that one area, the anterior lateral prefrontal cortex (alPFC), is highly specialized for making prospective, metacognitive judgments on the basis of internal probability; it is essential for knowing which decisions to tackle, given its assessment of how well they will be performed. Its activity predicted prospective metacognitive judgments, and individual variation in activity predicted individual variation in metacognitive judgments. Its disruption altered metacognitive judgments, leading participants to tackle perceptual decisions they were likely to fail.
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Affiliation(s)
- Kentaro Miyamoto
- Wellcome Centre for Integrative Neuroimaging (WIN), Department of Experimental Psychology, Tinsley Building, University of Oxford, Mansfield Road, Oxford OX1 3TA, UK.
| | - Nadescha Trudel
- Wellcome Centre for Integrative Neuroimaging (WIN), Department of Experimental Psychology, Tinsley Building, University of Oxford, Mansfield Road, Oxford OX1 3TA, UK
| | - Kevin Kamermans
- Wellcome Centre for Integrative Neuroimaging (WIN), Department of Experimental Psychology, Tinsley Building, University of Oxford, Mansfield Road, Oxford OX1 3TA, UK
| | - Michele C Lim
- Wellcome Centre for Integrative Neuroimaging (WIN), Department of Experimental Psychology, Tinsley Building, University of Oxford, Mansfield Road, Oxford OX1 3TA, UK
| | - Alberto Lazari
- Wellcome Centre for Integrative Neuroimaging (WIN), FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
| | - Lennart Verhagen
- Wellcome Centre for Integrative Neuroimaging (WIN), Department of Experimental Psychology, Tinsley Building, University of Oxford, Mansfield Road, Oxford OX1 3TA, UK
| | - Marco K Wittmann
- Wellcome Centre for Integrative Neuroimaging (WIN), Department of Experimental Psychology, Tinsley Building, University of Oxford, Mansfield Road, Oxford OX1 3TA, UK
| | - Matthew F S Rushworth
- Wellcome Centre for Integrative Neuroimaging (WIN), Department of Experimental Psychology, Tinsley Building, University of Oxford, Mansfield Road, Oxford OX1 3TA, UK
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24
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Morville T, Madsen KH, Siebner HR, Hulme OJ. Reward signalling in brainstem nuclei under fluctuating blood glucose. PLoS One 2021; 16:e0243899. [PMID: 33826633 PMCID: PMC8026025 DOI: 10.1371/journal.pone.0243899] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Accepted: 12/01/2020] [Indexed: 11/18/2022] Open
Abstract
Phasic dopamine release from mid-brain dopaminergic neurons is thought to signal errors of reward prediction (RPE). If reward maximisation is to maintain homeostasis, then the value of primary rewards should be coupled to the homeostatic errors they remediate. This leads to the prediction that RPE signals should be configured as a function of homeostatic state and thus diminish with the attenuation of homeostatic error. To test this hypothesis, we collected a large volume of functional MRI data from five human volunteers on four separate days. After fasting for 12 hours, subjects consumed preloads that differed in glucose concentration. Participants then underwent a Pavlovian cue-conditioning paradigm in which the colour of a fixation-cross was stochastically associated with the delivery of water or glucose via a gustometer. This design afforded computation of RPE separately for better- and worse-than expected outcomes during ascending and descending trajectories of serum glucose fluctuations. In the parabrachial nuclei, regional activity coding positive RPEs scaled positively with serum glucose for both ascending and descending glucose levels. The ventral tegmental area and substantia nigra became more sensitive to negative RPEs when glucose levels were ascending. Together, the results suggest that RPE signals in key brainstem structures are modulated by homeostatic trajectories of naturally occurring glycaemic flux, revealing a tight interplay between homeostatic state and the neural encoding of primary reward in the human brain.
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Affiliation(s)
- Tobias Morville
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
| | - Kristoffer H. Madsen
- DTU Compute, Department of Informatics and Mathematical Modelling, Technical University of Denmark, Copenhagen, Denmark
| | - Hartwig R. Siebner
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
- Department of Neurology, Copenhagen University Hospital Bispebjerg, Copenhagen, Denmark
| | - Oliver J. Hulme
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
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25
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Procyk E, Fontanier V, Sarazin M, Delord B, Goussi C, Wilson CRE. The midcingulate cortex and temporal integration. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2021; 158:395-419. [PMID: 33785153 DOI: 10.1016/bs.irn.2020.12.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
The ability to integrate information across time at multiple timescales is a vital element of adaptive behavior, because it provides the capacity to link events separated in time, extract useful information from previous events and actions, and to construct plans for behavior over time. Here we make the argument that this information integration capacity is a central function of the midcingulate cortex (MCC), by reviewing the anatomical, intrinsic network, neurophysiological, and behavioral properties of MCC. The MCC is the region of the medial wall situated dorsal to the corpus callosum and sometimes referred to as dACC. It is positioned within the densely connected core network of the primate brain, with a rich diversity of cognitive, somatomotor and autonomic connections. Furthermore, the MCC shows strong local network inhibition which appears to control the metastability of the region-an established feature of many cortical networks in which the neural dynamics move through a series of quasi-stationary states. We propose that the strong local inhibition in MCC leads to particularly long dynamic state durations, and so less frequent transitions. Apparently as a result of these anatomical features and synaptic and ionic determinants, the MCC cells display the longest neuronal timescales among a range of recorded cortical areas. We conclude that the anatomical position, intrinsic properties, and local network interactions of MCC make it a uniquely positioned cortical area to perform the integration of diverse information over time that is necessary for behavioral adaptation.
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Affiliation(s)
- Emmanuel Procyk
- Univ Lyon, Université Claude Bernard Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, Bron, France.
| | - Vincent Fontanier
- Univ Lyon, Université Claude Bernard Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, Bron, France
| | - Matthieu Sarazin
- Institute of Intelligent Systems and Robotics (ISIR), Sorbonne Université, Centre National de la Recherche Scientifique, UMR 7222, Paris, France
| | - Bruno Delord
- Institute of Intelligent Systems and Robotics (ISIR), Sorbonne Université, Centre National de la Recherche Scientifique, UMR 7222, Paris, France
| | - Clément Goussi
- Univ Lyon, Université Claude Bernard Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, Bron, France
| | - Charles R E Wilson
- Univ Lyon, Université Claude Bernard Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, Bron, France.
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26
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Abstract
OCD has lagged behind other psychiatric illnesses in the identification of molecular treatment targets, due in part to a lack of significant findings in genome-wide association studies. However, while progress in this area is being made, OCD's symptoms of obsessions, compulsions, and anxiety can be deconstructed into distinct neural functions that can be dissected in animal models. Studies in rodents and non-human primates have highlighted the importance of cortico-basal ganglia-thalamic circuits in OCD pathophysiology, and emerging studies in human post-mortem brain tissue point to glutamatergic synapse abnormalities as a potential cellular substrate for observed dysfunctional behaviors. In addition, accumulated evidence points to a potential role for neuromodulators including serotonin and dopamine in both OCD pathology and treatment. Here, we review current efforts to use animal models for the identification of molecules, cell types, and circuits relevant to OCD pathophysiology. We start by describing features of OCD that can be modeled in animals, including circuit abnormalities and genetic findings. We then review different strategies that have been used to study OCD using animal model systems, including transgenic models, circuit manipulations, and dissection of OCD-relevant neural constructs. Finally, we discuss how these findings may ultimately help to develop new treatment strategies for OCD and other related disorders.
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Affiliation(s)
- Brittany L Chamberlain
- Department of Psychiatry, Translational Neuroscience Program, University of Pittsburgh, Pittsburgh, PA, USA.,Center for Neuroscience Program and Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA
| | - Susanne E Ahmari
- Department of Psychiatry, Translational Neuroscience Program, University of Pittsburgh, Pittsburgh, PA, USA. .,Center for Neuroscience Program and Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA.
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27
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Cavanagh SE, Hunt LT, Kennerley SW. A Diversity of Intrinsic Timescales Underlie Neural Computations. Front Neural Circuits 2020; 14:615626. [PMID: 33408616 PMCID: PMC7779632 DOI: 10.3389/fncir.2020.615626] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 11/18/2020] [Indexed: 12/05/2022] Open
Abstract
Neural processing occurs across a range of temporal scales. To facilitate this, the brain uses fast-changing representations reflecting momentary sensory input alongside more temporally extended representations, which integrate across both short and long temporal windows. The temporal flexibility of these representations allows animals to behave adaptively. Short temporal windows facilitate adaptive responding in dynamic environments, while longer temporal windows promote the gradual integration of information across time. In the cognitive and motor domains, the brain sets overarching goals to be achieved within a long temporal window, which must be broken down into sequences of actions and precise movement control processed across much shorter temporal windows. Previous human neuroimaging studies and large-scale artificial network models have ascribed different processing timescales to different cortical regions, linking this to each region's position in an anatomical hierarchy determined by patterns of inter-regional connectivity. However, even within cortical regions, there is variability in responses when studied with single-neuron electrophysiology. Here, we review a series of recent electrophysiology experiments that demonstrate the heterogeneity of temporal receptive fields at the level of single neurons within a cortical region. This heterogeneity appears functionally relevant for the computations that neurons perform during decision-making and working memory. We consider anatomical and biophysical mechanisms that may give rise to a heterogeneity of timescales, including recurrent connectivity, cortical layer distribution, and neurotransmitter receptor expression. Finally, we reflect on the computational relevance of each brain region possessing a heterogeneity of neuronal timescales. We argue that this architecture is of particular importance for sensory, motor, and cognitive computations.
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Affiliation(s)
- Sean E. Cavanagh
- Department of Clinical and Movement Neurosciences, University College London, London, United Kingdom
| | - Laurence T. Hunt
- Department of Clinical and Movement Neurosciences, University College London, London, United Kingdom
- Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom
- Max Planck-UCL Centre for Computational Psychiatry and Aging, University College London, London, United Kingdom
- Department of Psychiatry, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Steven W. Kennerley
- Department of Clinical and Movement Neurosciences, University College London, London, United Kingdom
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28
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Monosov IE, Haber SN, Leuthardt EC, Jezzini A. Anterior Cingulate Cortex and the Control of Dynamic Behavior in Primates. Curr Biol 2020; 30:R1442-R1454. [PMID: 33290716 PMCID: PMC8197026 DOI: 10.1016/j.cub.2020.10.009] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The brain mechanism for controlling continuous behavior in dynamic contexts must mediate action selection and learning across many timescales, responding differentially to the level of environmental uncertainty and volatility. In this review, we argue that a part of the frontal cortex known as the anterior cingulate cortex (ACC) is particularly well suited for this function. First, the ACC is interconnected with prefrontal, parietal, and subcortical regions involved in valuation and action selection. Second, the ACC integrates diverse, behaviorally relevant information across multiple timescales, producing output signals that temporally encapsulate decision and learning processes and encode high-dimensional information about the value and uncertainty of future outcomes and subsequent behaviors. Third, the ACC signals behaviorally relevant information flexibly, displaying the capacity to represent information about current and future states in a valence-, context-, task- and action-specific manner. Fourth, the ACC dynamically controls instrumental- and non-instrumental information seeking behaviors to resolve uncertainty about future outcomes. We review electrophysiological and circuit disruption studies in primates to develop this point, discuss its relationship to novel therapeutics for neuropsychiatric disorders in humans, and conclude by relating ongoing research in primates to studies of medial frontal cortical regions in rodents.
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Affiliation(s)
- Ilya E Monosov
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Biomedical Engineering, Washington University, St. Louis, MO 63130, USA; Department of Electrical Engineering, Washington University, St. Louis, MO 63130, USA; Department of Neurosurgery School of Medicine, Washington University, St. Louis, MO 63110, USA; Pain Center, Washington University School of Medicine, St. Louis, MO 63110, USA.
| | - Suzanne N Haber
- Department of Pharmacology and Physiology, University of Rochester, Rochester, NY 14627, USA; Basic Neuroscience, McLean Hospital, Harvard Medical School, Belmont, MA 02478, USA
| | - Eric C Leuthardt
- Department of Biomedical Engineering, Washington University, St. Louis, MO 63130, USA; Department of Neurosurgery School of Medicine, Washington University, St. Louis, MO 63110, USA
| | - Ahmad Jezzini
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110, USA
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29
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Rolls ET, Vatansever D, Li Y, Cheng W, Feng J. Rapid Rule-Based Reward Reversal and the Lateral Orbitofrontal Cortex. Cereb Cortex Commun 2020; 1:tgaa087. [PMID: 34296143 PMCID: PMC8152898 DOI: 10.1093/texcom/tgaa087] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 11/04/2020] [Accepted: 11/10/2020] [Indexed: 12/14/2022] Open
Abstract
Humans and other primates can reverse their choice of stimuli in one trial when the rewards delivered by the stimuli change or reverse. Rapidly changing our behavior when the rewards change is important for many types of behavior, including emotional and social behavior. It is shown in a one-trial rule-based Go-NoGo deterministic visual discrimination reversal task to obtain points, that the human right lateral orbitofrontal cortex and adjoining inferior frontal gyrus is activated on reversal trials, when an expected reward is not obtained, and the non-reward allows the human to switch choices based on a rule. This reward reversal goes beyond model-free reinforcement learning. This functionality of the right lateral orbitofrontal cortex shown here in very rapid, one-trial, rule-based changes in human behavior when a reward is not received is related to the emotional and social changes that follow orbitofrontal cortex damage, and to depression in which this non-reward system is oversensitive and over-connected.
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Affiliation(s)
- Edmund T Rolls
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, China.,Oxford Centre for Computational Neuroscience, Oxford, UK.,Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
| | - Deniz Vatansever
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, China
| | - Yuzhu Li
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, China.,Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
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30
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Trudel N, Scholl J, Klein-Flügge MC, Fouragnan E, Tankelevitch L, Wittmann MK, Rushworth MFS. Polarity of uncertainty representation during exploration and exploitation in ventromedial prefrontal cortex. Nat Hum Behav 2020; 5:83-98. [PMID: 32868885 DOI: 10.1038/s41562-020-0929-3] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Accepted: 07/17/2020] [Indexed: 11/09/2022]
Abstract
Environments furnish multiple information sources for making predictions about future events. Here we use behavioural modelling and functional magnetic resonance imaging to describe how humans select predictors that might be most relevant. First, during early encounters with potential predictors, participants' selections were explorative and directed towards subjectively uncertain predictors (positive uncertainty effect). This was particularly the case when many future opportunities remained to exploit knowledge gained. Then, preferences for accurate predictors increased over time, while uncertain predictors were avoided (negative uncertainty effect). The behavioural transition from positive to negative uncertainty-driven selections was accompanied by changes in the representations of belief uncertainty in ventromedial prefrontal cortex (vmPFC). The polarity of uncertainty representations (positive or negative encoding of uncertainty) changed between exploration and exploitation periods. Moreover, the two periods were separated by a third transitional period in which beliefs about predictors' accuracy predominated. The vmPFC signals a multiplicity of decision variables, the strength and polarity of which vary with behavioural context.
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Affiliation(s)
- Nadescha Trudel
- Wellcome Integrative Neuroimaging (WIN), Department of Experimental Psychology, University of Oxford, Oxford, UK.
| | - Jacqueline Scholl
- Wellcome Integrative Neuroimaging (WIN), Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Miriam C Klein-Flügge
- Wellcome Integrative Neuroimaging (WIN), Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Elsa Fouragnan
- Wellcome Integrative Neuroimaging (WIN), Department of Experimental Psychology, University of Oxford, Oxford, UK.,School of Psychology, University of Plymouth, Plymouth, UK
| | - Lev Tankelevitch
- Wellcome Integrative Neuroimaging (WIN), Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Marco K Wittmann
- Wellcome Integrative Neuroimaging (WIN), Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Matthew F S Rushworth
- Wellcome Integrative Neuroimaging (WIN), Department of Experimental Psychology, University of Oxford, Oxford, UK
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31
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Multiple timescales of neural dynamics and integration of task-relevant signals across cortex. Proc Natl Acad Sci U S A 2020; 117:22522-22531. [PMID: 32839338 DOI: 10.1073/pnas.2005993117] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
A long-lasting challenge in neuroscience has been to find a set of principles that could be used to organize the brain into distinct areas with specific functions. Recent studies have proposed the orderly progression in the time constants of neural dynamics as an organizational principle of cortical computations. However, relationships between these timescales and their dependence on response properties of individual neurons are unknown, making it impossible to determine how mechanisms underlying such a computational principle are related to other aspects of neural processing. Here, we developed a comprehensive method to simultaneously estimate multiple timescales in neuronal dynamics and integration of task-relevant signals along with selectivity to those signals. By applying our method to neural and behavioral data during a dynamic decision-making task, we found that most neurons exhibited multiple timescales in their response, which consistently increased from parietal to prefrontal and cingulate cortex. While predicting rates of behavioral adjustments, these timescales were not correlated across individual neurons in any cortical area, resulting in independent parallel hierarchies of timescales. Additionally, none of these timescales depended on selectivity to task-relevant signals. Our results not only suggest the existence of multiple canonical mechanisms for increasing timescales of neural dynamics across cortex but also point to additional mechanisms that allow decorrelation of these timescales to enable more flexibility.
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32
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Global reward state affects learning and activity in raphe nucleus and anterior insula in monkeys. Nat Commun 2020; 11:3771. [PMID: 32724052 PMCID: PMC7387352 DOI: 10.1038/s41467-020-17343-w] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Accepted: 06/05/2020] [Indexed: 01/27/2023] Open
Abstract
People and other animals learn the values of choices by observing the contingencies between them and their outcomes. However, decisions are not guided by choice-linked reward associations alone; macaques also maintain a memory of the general, average reward rate - the global reward state - in an environment. Remarkably, global reward state affects the way that each choice outcome is valued and influences future decisions so that the impact of both choice success and failure is different in rich and poor environments. Successful choices are more likely to be repeated but this is especially the case in rich environments. Unsuccessful choices are more likely to be abandoned but this is especially likely in poor environments. Functional magnetic resonance imaging (fMRI) revealed two distinct patterns of activity, one in anterior insula and one in the dorsal raphe nucleus, that track global reward state as well as specific outcome events.
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33
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Chau BKH, Law CK, Lopez-Persem A, Klein-Flügge MC, Rushworth MFS. Consistent patterns of distractor effects during decision making. eLife 2020; 9:e53850. [PMID: 32628109 PMCID: PMC7371422 DOI: 10.7554/elife.53850] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Accepted: 07/06/2020] [Indexed: 01/24/2023] Open
Abstract
The value of a third potential option or distractor can alter the way in which decisions are made between two other options. Two hypotheses have received empirical support: that a high value distractor improves the accuracy with which decisions between two other options are made and that it impairs accuracy. Recently, however, it has been argued that neither observation is replicable. Inspired by neuroimaging data showing that high value distractors have different impacts on prefrontal and parietal regions, we designed a dual route decision-making model that mimics the neural signals of these regions. Here we show in the dual route model and empirical data that both enhancement and impairment effects are robust phenomena but predominate in different parts of the decision space defined by the options' and the distractor's values. However, beyond these constraints, both effects co-exist under similar conditions. Moreover, both effects are robust and observable in six experiments.
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Affiliation(s)
- Bolton KH Chau
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic UniversityHong KongHong Kong
- University Research Facility in Behavioral and Systems Neuroscience, The Hong Kong Polytechnic UniversityHong KongHong Kong
| | - Chun-Kit Law
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic UniversityHong KongHong Kong
| | - Alizée Lopez-Persem
- Wellcome Centre for Integrative Neuroimaging (WIN), Department of Experimental Psychology, University of OxfordOxfordUnited Kingdom
- FrontLab, Paris Brain Institute (ICM), Inserm U 1127, CNRS UMR 7225, Sorbonne UniversitéParisFrance
| | - Miriam C Klein-Flügge
- Wellcome Centre for Integrative Neuroimaging (WIN), Department of Experimental Psychology, University of OxfordOxfordUnited Kingdom
| | - Matthew FS Rushworth
- Wellcome Centre for Integrative Neuroimaging (WIN), Department of Experimental Psychology, University of OxfordOxfordUnited Kingdom
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34
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Badman RP, Hills TT, Akaishi R. Multiscale Computation and Dynamic Attention in Biological and Artificial Intelligence. Brain Sci 2020; 10:E396. [PMID: 32575758 PMCID: PMC7348831 DOI: 10.3390/brainsci10060396] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Revised: 05/23/2020] [Accepted: 06/17/2020] [Indexed: 11/16/2022] Open
Abstract
Biological and artificial intelligence (AI) are often defined by their capacity to achieve a hierarchy of short-term and long-term goals that require incorporating information over time and space at both local and global scales. More advanced forms of this capacity involve the adaptive modulation of integration across scales, which resolve computational inefficiency and explore-exploit dilemmas at the same time. Research in neuroscience and AI have both made progress towards understanding architectures that achieve this. Insight into biological computations come from phenomena such as decision inertia, habit formation, information search, risky choices and foraging. Across these domains, the brain is equipped with mechanisms (such as the dorsal anterior cingulate and dorsolateral prefrontal cortex) that can represent and modulate across scales, both with top-down control processes and by local to global consolidation as information progresses from sensory to prefrontal areas. Paralleling these biological architectures, progress in AI is marked by innovations in dynamic multiscale modulation, moving from recurrent and convolutional neural networks-with fixed scalings-to attention, transformers, dynamic convolutions, and consciousness priors-which modulate scale to input and increase scale breadth. The use and development of these multiscale innovations in robotic agents, game AI, and natural language processing (NLP) are pushing the boundaries of AI achievements. By juxtaposing biological and artificial intelligence, the present work underscores the critical importance of multiscale processing to general intelligence, as well as highlighting innovations and differences between the future of biological and artificial intelligence.
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Affiliation(s)
| | | | - Rei Akaishi
- Center for Brain Science, RIKEN, Saitama 351-0198, Japan
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35
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Sallet J, Noonan MP, Thomas A, O’Reilly JX, Anderson J, Papageorgiou GK, Neubert FX, Ahmed B, Smith J, Bell AH, Buckley MJ, Roumazeilles L, Cuell S, Walton ME, Krug K, Mars RB, Rushworth MFS. Behavioral flexibility is associated with changes in structure and function distributed across a frontal cortical network in macaques. PLoS Biol 2020; 18:e3000605. [PMID: 32453728 PMCID: PMC7274449 DOI: 10.1371/journal.pbio.3000605] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Revised: 06/05/2020] [Accepted: 04/30/2020] [Indexed: 01/08/2023] Open
Abstract
One of the most influential accounts of central orbitofrontal cortex-that it mediates behavioral flexibility-has been challenged by the finding that discrimination reversal in macaques, the classic test of behavioral flexibility, is unaffected when lesions are made by excitotoxin injection rather than aspiration. This suggests that the critical brain circuit mediating behavioral flexibility in reversal tasks lies beyond the central orbitofrontal cortex. To determine its identity, a group of nine macaques were taught discrimination reversal learning tasks, and its impact on gray matter was measured. Magnetic resonance imaging scans were taken before and after learning and compared with scans from two control groups, each comprising 10 animals. One control group learned discrimination tasks that were similar but lacked any reversal component, and the other control group engaged in no learning. Gray matter changes were prominent in posterior orbitofrontal cortex/anterior insula but were also found in three other frontal cortical regions: lateral orbitofrontal cortex (orbital part of area 12 [12o]), cingulate cortex, and lateral prefrontal cortex. In a second analysis, neural activity in posterior orbitofrontal cortex/anterior insula was measured at rest, and its pattern of coupling with the other frontal cortical regions was assessed. Activity coupling increased significantly in the reversal learning group in comparison with controls. In a final set of experiments, we used similar structural imaging procedures and analyses to demonstrate that aspiration lesion of central orbitofrontal cortex, of the type known to affect discrimination learning, affected structure and activity in the same frontal cortical circuit. The results identify a distributed frontal cortical circuit associated with behavioral flexibility.
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Affiliation(s)
- Jérôme Sallet
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
- Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, Bron, France
| | - MaryAnn P. Noonan
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
| | - Adam Thomas
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
- National Institute of Mental Health, Magnuson Clinical Center, Bethesda, Maryland, United States of America
| | - Jill X. O’Reilly
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
| | - Jesper Anderson
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
| | - Georgios K. Papageorgiou
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
- McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Franz X. Neubert
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
| | - Bashir Ahmed
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom
| | - Jackson Smith
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom
| | - Andrew H. Bell
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
| | - Mark J. Buckley
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
| | - Léa Roumazeilles
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
| | - Steven Cuell
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
| | - Mark E. Walton
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
| | - Kristine Krug
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom
- Otto-von-Guericke-Universität, Magdeburg, Germany
- Leibniz-Institut für Neurobiologie, Magdeburg, Germany
| | - Rogier B. Mars
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, the Netherlands
| | - Matthew F. S. Rushworth
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
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36
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Kao CH, Khambhati AN, Bassett DS, Nassar MR, McGuire JT, Gold JI, Kable JW. Functional brain network reconfiguration during learning in a dynamic environment. Nat Commun 2020; 11:1682. [PMID: 32245973 PMCID: PMC7125157 DOI: 10.1038/s41467-020-15442-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2019] [Accepted: 03/06/2020] [Indexed: 11/09/2022] Open
Abstract
When learning about dynamic and uncertain environments, people should update their beliefs most strongly when new evidence is most informative, such as when the environment undergoes a surprising change or existing beliefs are highly uncertain. Here we show that modulations of surprise and uncertainty are encoded in a particular, temporally dynamic pattern of whole-brain functional connectivity, and this encoding is enhanced in individuals that adapt their learning dynamics more appropriately in response to these factors. The key feature of this whole-brain pattern of functional connectivity is stronger connectivity, or functional integration, between the fronto-parietal and other functional systems. Our results provide new insights regarding the association between dynamic adjustments in learning and dynamic, large-scale changes in functional connectivity across the brain.
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Affiliation(s)
- Chang-Hao Kao
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, 19104, USA.
| | - Ankit N Khambhati
- Department of Neurological Surgery, University of California, San Francisco, CA, 94122, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Santa Fe Institute, Santa Fe, NM, 87501, USA
| | - Matthew R Nassar
- Department of Neuroscience, Brown University, Providence, RI, 02912, USA.,Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, RI, 02912, USA
| | - Joseph T McGuire
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, 02215, USA
| | - Joshua I Gold
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Joseph W Kable
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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37
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Soltani A, Izquierdo A. Adaptive learning under expected and unexpected uncertainty. Nat Rev Neurosci 2020; 20:635-644. [PMID: 31147631 DOI: 10.1038/s41583-019-0180-y] [Citation(s) in RCA: 114] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The outcome of a decision is often uncertain, and outcomes can vary over repeated decisions. Whether decision outcomes should substantially affect behaviour and learning depends on whether they are representative of a typically experienced range of outcomes or signal a change in the reward environment. Successful learning and decision-making therefore require the ability to estimate expected uncertainty (related to the variability of outcomes) and unexpected uncertainty (related to the variability of the environment). Understanding the bases and effects of these two types of uncertainty and the interactions between them - at the computational and the neural level - is crucial for understanding adaptive learning. Here, we examine computational models and experimental findings to distil computational principles and neural mechanisms for adaptive learning under uncertainty.
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Affiliation(s)
- Alireza Soltani
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA.
| | - Alicia Izquierdo
- Department of Psychology, The Brain Research Institute, University of California, Los Angeles, Los Angeles, CA, USA.
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38
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Yoo SBM, Hayden BY. The Transition from Evaluation to Selection Involves Neural Subspace Reorganization in Core Reward Regions. Neuron 2020; 105:712-724.e4. [PMID: 31836322 PMCID: PMC7035164 DOI: 10.1016/j.neuron.2019.11.013] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Revised: 10/13/2019] [Accepted: 11/08/2019] [Indexed: 11/29/2022]
Abstract
Economic choice proceeds from evaluation, in which we contemplate options, to selection, in which we weigh options and choose one. These stages must be differentiated so that decision makers do not proceed to selection before evaluation is complete. We examined responses of neurons in two core reward regions, orbitofrontal (OFC) and ventromedial prefrontal cortex (vmPFC), during two-option choice with asynchronous offer presentation. Our data suggest that neurons selective during the first (presumed evaluation) and second (presumed comparison and selection) offer epochs come from a single pool. Stage transition is accompanied by a shift toward orthogonality in the low-dimensional population response manifold. Nonetheless, the relative position of each option in driving responses in the population subspace is preserved. The orthogonalization we observe supports the hypothesis that the transition from evaluation to selection leads to reorganization of response subspace and suggests a mechanism by which value-related signals are prevented from prematurely driving choice.
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Affiliation(s)
- Seng Bum Michael Yoo
- Department of Neuroscience, Center for Magnetic Resonance Research, Center for Neuroengineering, University of Minnesota, Minneapolis, MN 55455, USA.
| | - Benjamin Y Hayden
- Department of Neuroscience, Center for Magnetic Resonance Research, Center for Neuroengineering, University of Minnesota, Minneapolis, MN 55455, USA
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39
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Khalighinejad N, Bongioanni A, Verhagen L, Folloni D, Attali D, Aubry JF, Sallet J, Rushworth MFS. A Basal Forebrain-Cingulate Circuit in Macaques Decides It Is Time to Act. Neuron 2019; 105:370-384.e8. [PMID: 31813653 PMCID: PMC6975166 DOI: 10.1016/j.neuron.2019.10.030] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 10/02/2019] [Accepted: 10/22/2019] [Indexed: 12/22/2022]
Abstract
The medial frontal cortex has been linked to voluntary action, but an explanation of why decisions to act emerge at particular points in time has been lacking. We show that, in macaques, decisions about whether and when to act are predicted by a set of features defining the animal’s current and past context; for example, respectively, cues indicating the current average rate of reward and recent previous voluntary action decisions. We show that activity in two brain areas—the anterior cingulate cortex and basal forebrain—tracks these contextual factors and mediates their effects on behavior in distinct ways. We use focused transcranial ultrasound to selectively and effectively stimulate deep in the brain, even as deep as the basal forebrain, and demonstrate that alteration of activity in the two areas changes decisions about when to act. Likelihood and timing of voluntary action in macaques can be partially predicted Recent experience and present context influence when voluntary action occurs A basal forebrain-cingulate circuit mediated effects of these factors on behavior Stimulation of this circuit by ultrasound changed decisions about when to act
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Affiliation(s)
- Nima Khalighinejad
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford OX1 3SR, UK.
| | - Alessandro Bongioanni
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford OX1 3SR, UK
| | - Lennart Verhagen
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford OX1 3SR, UK; Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen 6525 XZ, the Netherlands
| | - Davide Folloni
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford OX1 3SR, UK
| | - David Attali
- Physics for Medicine Paris, INSERM U1273, ESPCI Paris, CNRS FRE 2031, PSL Research University, Paris 75012, France; Pathophysiology of Psychiatric Disorders Laboratory, Inserm U1266, Institute of Psychiatry and Neuroscience of Paris, Paris Descartes University, Paris University, Paris 75014, France; Service Hospitalo-Universitaire, Sainte-Anne Hospital, UGH Paris Psychiatry and Neurosciences, Paris 75014, France
| | - Jean-Francois Aubry
- Physics for Medicine Paris, INSERM U1273, ESPCI Paris, CNRS FRE 2031, PSL Research University, Paris 75012, France
| | - Jerome Sallet
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford OX1 3SR, UK
| | - Matthew F S Rushworth
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford OX1 3SR, UK
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Computational noise in reward-guided learning drives behavioral variability in volatile environments. Nat Neurosci 2019; 22:2066-2077. [PMID: 31659343 DOI: 10.1038/s41593-019-0518-9] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Accepted: 09/17/2019] [Indexed: 11/09/2022]
Abstract
When learning the value of actions in volatile environments, humans often make seemingly irrational decisions that fail to maximize expected value. We reasoned that these 'non-greedy' decisions, instead of reflecting information seeking during choice, may be caused by computational noise in the learning of action values. Here using reinforcement learning models of behavior and multimodal neurophysiological data, we show that the majority of non-greedy decisions stem from this learning noise. The trial-to-trial variability of sequential learning steps and their impact on behavior could be predicted both by blood oxygen level-dependent responses to obtained rewards in the dorsal anterior cingulate cortex and by phasic pupillary dilation, suggestive of neuromodulatory fluctuations driven by the locus coeruleus-norepinephrine system. Together, these findings indicate that most behavioral variability, rather than reflecting human exploration, is due to the limited computational precision of reward-guided learning.
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41
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Klein-Flügge MC, Wittmann MK, Shpektor A, Jensen DEA, Rushworth MFS. Multiple associative structures created by reinforcement and incidental statistical learning mechanisms. Nat Commun 2019; 10:4835. [PMID: 31645545 PMCID: PMC6811627 DOI: 10.1038/s41467-019-12557-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Accepted: 09/16/2019] [Indexed: 01/07/2023] Open
Abstract
Learning the structure of the world can be driven by reinforcement but also occurs incidentally through experience. Reinforcement learning theory has provided insight into how prediction errors drive updates in beliefs but less attention has been paid to the knowledge resulting from such learning. Here we contrast associative structures formed through reinforcement and experience of task statistics. BOLD neuroimaging in human volunteers demonstrates rigid representations of rewarded sequences in temporal pole and posterior orbito-frontal cortex, which are constructed backwards from reward. By contrast, medial prefrontal cortex and a hippocampal-amygdala border region carry reward-related knowledge but also flexible statistical knowledge of the currently relevant task model. Intriguingly, ventral striatum encodes prediction error responses but not the full RL- or statistically derived task knowledge. In summary, representations of task knowledge are derived via multiple learning processes operating at different time scales that are associated with partially overlapping and partially specialized anatomical regions. Associative learning occurs through reinforcement mechanisms as well as incidentally through experience of statistical relationships. Here, the authors report that these two learning processes are associated with specialized anatomical regions that operate at different time scales.
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Affiliation(s)
- Miriam C Klein-Flügge
- Department of Experimental Psychology, University of Oxford, Tinsley Building, Mansfield Road, Oxford, OX1 3TA, UK. .,Wellcome Centre for Integrative Neuroimaging (WIN), University of Oxford, Nuffield Department of Clinical Neurosciences, Level 6, West Wing, John Radcliffe Hospital, Oxford, OX3 9DU, UK.
| | - Marco K Wittmann
- Department of Experimental Psychology, University of Oxford, Tinsley Building, Mansfield Road, Oxford, OX1 3TA, UK.,Wellcome Centre for Integrative Neuroimaging (WIN), University of Oxford, Nuffield Department of Clinical Neurosciences, Level 6, West Wing, John Radcliffe Hospital, Oxford, OX3 9DU, UK
| | - Anna Shpektor
- Wellcome Centre for Integrative Neuroimaging (WIN), University of Oxford, Nuffield Department of Clinical Neurosciences, Level 6, West Wing, John Radcliffe Hospital, Oxford, OX3 9DU, UK
| | - Daria E A Jensen
- Department of Experimental Psychology, University of Oxford, Tinsley Building, Mansfield Road, Oxford, OX1 3TA, UK.,Wellcome Centre for Integrative Neuroimaging (WIN), University of Oxford, Nuffield Department of Clinical Neurosciences, Level 6, West Wing, John Radcliffe Hospital, Oxford, OX3 9DU, UK.,Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, OX3 7JX, UK
| | - Matthew F S Rushworth
- Department of Experimental Psychology, University of Oxford, Tinsley Building, Mansfield Road, Oxford, OX1 3TA, UK.,Wellcome Centre for Integrative Neuroimaging (WIN), University of Oxford, Nuffield Department of Clinical Neurosciences, Level 6, West Wing, John Radcliffe Hospital, Oxford, OX3 9DU, UK
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42
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Cools R. Chemistry of the Adaptive Mind: Lessons from Dopamine. Neuron 2019; 104:113-131. [DOI: 10.1016/j.neuron.2019.09.035] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Revised: 09/19/2019] [Accepted: 09/20/2019] [Indexed: 12/21/2022]
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43
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Meder D, Herz DM, Rowe JB, Lehéricy S, Siebner HR. The role of dopamine in the brain - lessons learned from Parkinson's disease. Neuroimage 2019; 190:79-93. [DOI: 10.1016/j.neuroimage.2018.11.021] [Citation(s) in RCA: 78] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Revised: 10/25/2018] [Accepted: 11/16/2018] [Indexed: 11/30/2022] Open
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44
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A Network for Computing Value Equilibrium in the Human Medial Prefrontal Cortex. Neuron 2019; 101:977-987.e3. [DOI: 10.1016/j.neuron.2018.12.029] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Revised: 10/31/2018] [Accepted: 12/20/2018] [Indexed: 12/14/2022]
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45
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Maheu M, Dehaene S, Meyniel F. Brain signatures of a multiscale process of sequence learning in humans. eLife 2019; 8:41541. [PMID: 30714904 PMCID: PMC6361584 DOI: 10.7554/elife.41541] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Accepted: 01/18/2019] [Indexed: 01/08/2023] Open
Abstract
Extracting the temporal structure of sequences of events is crucial for perception, decision-making, and language processing. Here, we investigate the mechanisms by which the brain acquires knowledge of sequences and the possibility that successive brain responses reflect the progressive extraction of sequence statistics at different timescales. We measured brain activity using magnetoencephalography in humans exposed to auditory sequences with various statistical regularities, and we modeled this activity as theoretical surprise levels using several learning models. Successive brain waves related to different types of statistical inferences. Early post-stimulus brain waves denoted a sensitivity to a simple statistic, the frequency of items estimated over a long timescale (habituation). Mid-latency and late brain waves conformed qualitatively and quantitatively to the computational properties of a more complex inference: the learning of recent transition probabilities. Our findings thus support the existence of multiple computational systems for sequence processing involving statistical inferences at multiple scales.
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Affiliation(s)
- Maxime Maheu
- Cognitive Neuroimaging Unit, CEA DRF/JOLIOT, INSERM, Université Paris-Sud, Université Paris-Saclay, NeuroSpin center, Gif-sur-Yvette, France.,Université Paris Descartes, Sorbonne Paris Cité, Paris, France
| | - Stanislas Dehaene
- Cognitive Neuroimaging Unit, CEA DRF/JOLIOT, INSERM, Université Paris-Sud, Université Paris-Saclay, NeuroSpin center, Gif-sur-Yvette, France.,Collège de France, Paris, France
| | - Florent Meyniel
- Cognitive Neuroimaging Unit, CEA DRF/JOLIOT, INSERM, Université Paris-Sud, Université Paris-Saclay, NeuroSpin center, Gif-sur-Yvette, France
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46
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Kolling N, O'Reilly JX. State-change decisions and dorsomedial prefrontal cortex: the importance of time. Curr Opin Behav Sci 2018; 22:152-160. [PMID: 30123818 PMCID: PMC6095941 DOI: 10.1016/j.cobeha.2018.06.017] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Different kinds of decision making can be categorized by their differential effect on the agent’s current and future states as well as the computational challenges they pose. Here, we draw a distinction between within-state and state-change decision-making, and propose that a dedicated decision mechanism exists in dorsomedial prefrontal cortex (dmPFC) that is specialized for state-change decisions. We set out a formal framework in which state change decisions may be made on the basis of the integrated momentary reward rate, over the intended time to be spent in a state. A key feature of this framework is that reward rate is expressed as a function of continuous time. We argue that dmPFC is suited for this type of decision making partly due to its ability to track the passage of time. This proposed function of dmPFC is placed in contrast to other evaluative systems such as the orbitofrontal cortex, which is important for careful deliberation within a specific model-space or option-space and within a decision strategy.
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Affiliation(s)
- Nils Kolling
- Wellcome Integrative Neuroimaging (WIN), Department of Experimental Psychology, University of Oxford, Oxford, UK.,Oxford Centre of Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, UK
| | - Jill X O'Reilly
- Wellcome Integrative Neuroimaging (WIN), Department of Experimental Psychology, University of Oxford, Oxford, UK.,Wellcome Integrative Neuroimaging (WIN), Centre for Functional MRI of the Brain (MRI), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, UK.,Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
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47
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Massi B, Donahue CH, Lee D. Volatility Facilitates Value Updating in the Prefrontal Cortex. Neuron 2018; 99:598-608.e4. [PMID: 30033151 DOI: 10.1016/j.neuron.2018.06.033] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Revised: 05/13/2018] [Accepted: 06/21/2018] [Indexed: 01/24/2023]
Abstract
Adaptation of learning and decision-making might depend on the regulation of activity in the prefrontal cortex. Here we examined how volatility of reward probabilities influences learning and neural activity in the primate prefrontal cortex. We found that animals selected recently rewarded targets more often when reward probabilities of different options fluctuated across trials than when they were fixed. Additionally, neurons in the orbitofrontal cortex displayed more sustained activity related to the outcomes of their previous choices when reward probabilities changed over time. Such volatility also enhanced signals in the dorsolateral prefrontal cortex related to the current but not the previous location of the previously rewarded target. These results suggest that prefrontal activity related to choice and reward is dynamically regulated by the volatility of the environment and underscore the role of the prefrontal cortex in identifying aspects of the environment that are responsible for previous outcomes and should be learned.
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Affiliation(s)
- Bart Massi
- Interdeparmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06510, USA
| | | | - Daeyeol Lee
- Interdeparmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06510, USA; Department of Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA; Department of Psychiatry, Yale School of Medicine, New Haven, CT 06510, USA; Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA; Department of Psychology, Yale University, New Haven, CT 06520, USA.
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48
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Anggraini D, Glasauer S, Wunderlich K. Neural signatures of reinforcement learning correlate with strategy adoption during spatial navigation. Sci Rep 2018; 8:10110. [PMID: 29973606 PMCID: PMC6031619 DOI: 10.1038/s41598-018-28241-z] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2018] [Accepted: 06/19/2018] [Indexed: 12/14/2022] Open
Abstract
Human navigation is generally believed to rely on two types of strategy adoption, route-based and map-based strategies. Both types of navigation require making spatial decisions along the traversed way although formal computational and neural links between navigational strategies and mechanisms of value-based decision making have so far been underexplored in humans. Here we employed functional magnetic resonance imaging (fMRI) while subjects located different objects in a virtual environment. We then modelled their paths using reinforcement learning (RL) algorithms, which successfully explained decision behavior and its neural correlates. Our results show that subjects used a mixture of route and map-based navigation and their paths could be well explained by the model-free and model-based RL algorithms. Furthermore, the value signals of model-free choices during route-based navigation modulated the BOLD signals in the ventro-medial prefrontal cortex (vmPFC), whereas the BOLD signals in parahippocampal and hippocampal regions pertained to model-based value signals during map-based navigation. Our findings suggest that the brain might share computational mechanisms and neural substrates for navigation and value-based decisions such that model-free choice guides route-based navigation and model-based choice directs map-based navigation. These findings open new avenues for computational modelling of wayfinding by directing attention to value-based decision, differing from common direction and distances approaches.
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Affiliation(s)
- Dian Anggraini
- Department of Psychology, Ludwig-Maximilians-Universität München, Munich, 80802, Germany.,Graduate School of Systemic Neuroscience LMU Munich, Planegg, Martinsried, 82152, Germany
| | - Stefan Glasauer
- Center for Sensorimotor Research, Department of Neurology, Ludwig-Maximilians-Universitaet München Klinikum Grosshadern, Munich, 81377, Germany.,Bernstein Center for Computational Neuroscience Munich, Planegg, Martinsried, 82152, Germany.,Graduate School of Systemic Neuroscience LMU Munich, Planegg, Martinsried, 82152, Germany
| | - Klaus Wunderlich
- Department of Psychology, Ludwig-Maximilians-Universität München, Munich, 80802, Germany. .,Bernstein Center for Computational Neuroscience Munich, Planegg, Martinsried, 82152, Germany. .,Graduate School of Systemic Neuroscience LMU Munich, Planegg, Martinsried, 82152, Germany.
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Pezzulo G, Rigoli F, Friston KJ. Hierarchical Active Inference: A Theory of Motivated Control. Trends Cogn Sci 2018; 22:294-306. [PMID: 29475638 PMCID: PMC5870049 DOI: 10.1016/j.tics.2018.01.009] [Citation(s) in RCA: 135] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2017] [Revised: 01/23/2018] [Accepted: 01/30/2018] [Indexed: 12/17/2022]
Abstract
Motivated control refers to the coordination of behaviour to achieve affectively valenced outcomes or goals. The study of motivated control traditionally assumes a distinction between control and motivational processes, which map to distinct (dorsolateral versus ventromedial) brain systems. However, the respective roles and interactions between these processes remain controversial. We offer a novel perspective that casts control and motivational processes as complementary aspects - goal propagation and prioritization, respectively - of active inference and hierarchical goal processing under deep generative models. We propose that the control hierarchy propagates prior preferences or goals, but their precision is informed by the motivational context, inferred at different levels of the motivational hierarchy. The ensuing integration of control and motivational processes underwrites action and policy selection and, ultimately, motivated behaviour, by enabling deep inference to prioritize goals in a context-sensitive way.
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Affiliation(s)
- Giovanni Pezzulo
- Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy.
| | - Francesco Rigoli
- City, University of London, London, UK; Wellcome Trust Centre for Neuroimaging, UCL, London, UK
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