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  1. Article ; Online: Value representations in the rodent orbitofrontal cortex drive learning, not choice

    Kevin J Miller / Matthew M Botvinick / Carlos D Brody

    eLife, Vol

    2022  Volume 11

    Abstract: Humans and animals make predictions about the rewards they expect to receive in different situations. In formal models of behavior, these predictions are known as value representations, and they play two very different roles. Firstly, they drive choice: ... ...

    Abstract Humans and animals make predictions about the rewards they expect to receive in different situations. In formal models of behavior, these predictions are known as value representations, and they play two very different roles. Firstly, they drive choice: the expected values of available options are compared to one another, and the best option is selected. Secondly, they support learning: expected values are compared to rewards actually received, and future expectations are updated accordingly. Whether these different functions are mediated by different neural representations remains an open question. Here, we employ a recently developed multi-step task for rats that computationally separates learning from choosing. We investigate the role of value representations in the rodent orbitofrontal cortex, a key structure for value-based cognition. Electrophysiological recordings and optogenetic perturbations indicate that these representations do not directly drive choice. Instead, they signal expected reward information to a learning process elsewhere in the brain that updates choice mechanisms.
    Keywords reinforcement learning ; planning ; orbitofrontal cortex ; learning ; decision making ; electrophysiology ; Medicine ; R ; Science ; Q ; Biology (General) ; QH301-705.5
    Subject code 006
    Language English
    Publishing date 2022-08-01T00:00:00Z
    Publisher eLife Sciences Publications Ltd
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Dissociable neural mechanisms track evidence accumulation for selection of attention versus action

    Amitai Shenhav / Mark A. Straccia / Sebastian Musslick / Jonathan D. Cohen / Matthew M. Botvinick

    Nature Communications, Vol 9, Iss 1, Pp 1-

    2018  Volume 10

    Abstract: Decision-making involves parallel information processing regarding what stimulus dimension to pay attention to and what action to take. Here, the authors show that vmPFC tracks the value of the attended attribute while dACC tracks the degree to which it ... ...

    Abstract Decision-making involves parallel information processing regarding what stimulus dimension to pay attention to and what action to take. Here, the authors show that vmPFC tracks the value of the attended attribute while dACC tracks the degree to which it is attended.
    Keywords Science ; Q
    Language English
    Publishing date 2018-06-01T00:00:00Z
    Publisher Nature Publishing Group
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Dissociable neural mechanisms track evidence accumulation for selection of attention versus action

    Amitai Shenhav / Mark A. Straccia / Sebastian Musslick / Jonathan D. Cohen / Matthew M. Botvinick

    Nature Communications, Vol 9, Iss 1, Pp 1-

    2018  Volume 10

    Abstract: Decision-making involves parallel information processing regarding what stimulus dimension to pay attention to and what action to take. Here, the authors show that vmPFC tracks the value of the attended attribute while dACC tracks the degree to which it ... ...

    Abstract Decision-making involves parallel information processing regarding what stimulus dimension to pay attention to and what action to take. Here, the authors show that vmPFC tracks the value of the attended attribute while dACC tracks the degree to which it is attended.
    Keywords Science ; Q
    Language English
    Publishing date 2018-06-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Predictive representations can link model-based reinforcement learning to model-free mechanisms.

    Evan M Russek / Ida Momennejad / Matthew M Botvinick / Samuel J Gershman / Nathaniel D Daw

    PLoS Computational Biology, Vol 13, Iss 9, p e

    2017  Volume 1005768

    Abstract: Humans and animals are capable of evaluating actions by considering their long-run future rewards through a process described using model-based reinforcement learning (RL) algorithms. The mechanisms by which neural circuits perform the computations ... ...

    Abstract Humans and animals are capable of evaluating actions by considering their long-run future rewards through a process described using model-based reinforcement learning (RL) algorithms. The mechanisms by which neural circuits perform the computations prescribed by model-based RL remain largely unknown; however, multiple lines of evidence suggest that neural circuits supporting model-based behavior are structurally homologous to and overlapping with those thought to carry out model-free temporal difference (TD) learning. Here, we lay out a family of approaches by which model-based computation may be built upon a core of TD learning. The foundation of this framework is the successor representation, a predictive state representation that, when combined with TD learning of value predictions, can produce a subset of the behaviors associated with model-based learning, while requiring less decision-time computation than dynamic programming. Using simulations, we delineate the precise behavioral capabilities enabled by evaluating actions using this approach, and compare them to those demonstrated by biological organisms. We then introduce two new algorithms that build upon the successor representation while progressively mitigating its limitations. Because this framework can account for the full range of observed putatively model-based behaviors while still utilizing a core TD framework, we suggest that it represents a neurally plausible family of mechanisms for model-based evaluation.
    Keywords Biology (General) ; QH301-705.5
    Subject code 006
    Language English
    Publishing date 2017-09-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: Neural and behavioral evidence for an intrinsic cost of self-control.

    Wouter Kool / Joseph T McGuire / Gary J Wang / Matthew M Botvinick

    PLoS ONE, Vol 8, Iss 8, p e

    2013  Volume 72626

    Abstract: The capacity for self-control is critical to adaptive functioning, yet our knowledge of the underlying processes and mechanisms is presently only inchoate. Theoretical work in economics has suggested a model of self-control centering on two key ... ...

    Abstract The capacity for self-control is critical to adaptive functioning, yet our knowledge of the underlying processes and mechanisms is presently only inchoate. Theoretical work in economics has suggested a model of self-control centering on two key assumptions: (1) a division within the decision-maker between two 'selves' with differing preferences; (2) the idea that self-control is intrinsically costly. Neuroscience has recently generated findings supporting the 'dual-self' assumption. The idea of self-control costs, in contrast, has remained speculative. We report the first independent evidence for self-control costs. Through a neuroimaging meta-analysis, we establish an anatomical link between self-control and the registration of cognitive effort costs. This link predicts that individuals who strongly avoid cognitive demand should also display poor self-control. To test this, we conducted a behavioral experiment leveraging a measure of demand avoidance along with two measures of self-control. The results obtained provide clear support for the idea of self-control costs.
    Keywords Medicine ; R ; Science ; Q
    Subject code 150
    Language English
    Publishing date 2013-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article: Rats and Humans Can Optimally Accumulate Evidence for Decision-Making

    Brunton, Bingni W / Carlos D. Brody / Matthew M. Botvinick

    Science. 2013 Apr. 5, v. 340, no. 6128

    2013  

    Abstract: How to Make Decisions Recently, a number of methods to probe internal properties of nonlinear neural systems have been developed. In these methods, highly variable stimuli are used to explore the input space of the system. Neural responses are then ... ...

    Abstract How to Make Decisions Recently, a number of methods to probe internal properties of nonlinear neural systems have been developed. In these methods, highly variable stimuli are used to explore the input space of the system. Neural responses are then studied using models that take advantage of the known trial-by-trial stimulus information. Brunton et al. (p. 95) adapted this combined approach to decision-making. Both in rats and humans, the diffusion constant of the drift-diffusion model of decision-making was zero, implying that the noise is all in the processing of sensory input and not in the evidence accumulator. In addition, rats gradually accumulated evidence for decision-making, with strong effects of sensory adaptation on gradual accumulation of evidence.
    Keywords decision making ; diffusivity ; humans ; models ; neurophysiology ; rats
    Language English
    Dates of publication 2013-0405
    Size p. 95-98.
    Publishing place American Association for the Advancement of Science
    Document type Article
    ZDB-ID 128410-1
    ISSN 1095-9203 ; 0036-8075
    ISSN (online) 1095-9203
    ISSN 0036-8075
    DOI 10.1126/science.1233912
    Database NAL-Catalogue (AGRICOLA)

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  7. Article ; Online: Optimal behavioral hierarchy.

    Alec Solway / Carlos Diuk / Natalia Córdova / Debbie Yee / Andrew G Barto / Yael Niv / Matthew M Botvinick

    PLoS Computational Biology, Vol 10, Iss 8, p e

    2014  Volume 1003779

    Abstract: Human behavior has long been recognized to display hierarchical structure: actions fit together into subtasks, which cohere into extended goal-directed activities. Arranging actions hierarchically has well established benefits, allowing behaviors to be ... ...

    Abstract Human behavior has long been recognized to display hierarchical structure: actions fit together into subtasks, which cohere into extended goal-directed activities. Arranging actions hierarchically has well established benefits, allowing behaviors to be represented efficiently by the brain, and allowing solutions to new tasks to be discovered easily. However, these payoffs depend on the particular way in which actions are organized into a hierarchy, the specific way in which tasks are carved up into subtasks. We provide a mathematical account for what makes some hierarchies better than others, an account that allows an optimal hierarchy to be identified for any set of tasks. We then present results from four behavioral experiments, suggesting that human learners spontaneously discover optimal action hierarchies.
    Keywords Biology (General) ; QH301-705.5
    Language English
    Publishing date 2014-08-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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