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  1. Article ; Online: Realizing the promise of AI: a new calling for cognitive science.

    Botvinick, Matthew M

    Trends in cognitive sciences

    2022  Volume 26, Issue 12, Page(s) 1013–1014

    Abstract: Rapid progress in artificial intelligence (AI) places a new spotlight on a long-standing question: how can we best develop AI to maximize its benefits to humanity? Answering this question in a satisfying and timely way represents an exciting challenge ... ...

    Abstract Rapid progress in artificial intelligence (AI) places a new spotlight on a long-standing question: how can we best develop AI to maximize its benefits to humanity? Answering this question in a satisfying and timely way represents an exciting challenge not only for AI research but also for all member disciplines of cognitive science.
    MeSH term(s) Humans ; Artificial Intelligence ; Cognitive Science
    Language English
    Publishing date 2022-09-20
    Publishing country England
    Document type Journal Article
    ZDB-ID 2010989-1
    ISSN 1879-307X ; 1364-6613
    ISSN (online) 1879-307X
    ISSN 1364-6613
    DOI 10.1016/j.tics.2022.08.004
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  2. Article ; Online: Value representations in the rodent orbitofrontal cortex drive learning, not choice.

    Miller, Kevin J / Botvinick, Matthew M / Brody, Carlos D

    eLife

    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 ... ...

    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
    MeSH term(s) Animals ; Choice Behavior/physiology ; Cognition/physiology ; Decision Making/physiology ; Humans ; Prefrontal Cortex/physiology ; Rats ; Reward ; Rodentia
    Language English
    Publishing date 2022-08-17
    Publishing country England
    Document type Journal Article
    ZDB-ID 2687154-3
    ISSN 2050-084X ; 2050-084X
    ISSN (online) 2050-084X
    ISSN 2050-084X
    DOI 10.7554/eLife.64575
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  3. 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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  4. Article ; Online: Compositional Sequence Generation in the Entorhinal-Hippocampal System.

    McNamee, Daniel C / Stachenfeld, Kimberly L / Botvinick, Matthew M / Gershman, Samuel J

    Entropy (Basel, Switzerland)

    2022  Volume 24, Issue 12

    Abstract: Neurons in the medial entorhinal cortex exhibit multiple, periodically organized, firing fields which collectively appear to form an internal representation of space. Neuroimaging data suggest that this grid coding is also present in other cortical areas ...

    Abstract Neurons in the medial entorhinal cortex exhibit multiple, periodically organized, firing fields which collectively appear to form an internal representation of space. Neuroimaging data suggest that this grid coding is also present in other cortical areas such as the prefrontal cortex, indicating that it may be a general principle of neural functionality in the brain. In a recent analysis through the lens of dynamical systems theory, we showed how grid coding can lead to the generation of a diversity of empirically observed sequential reactivations of hippocampal place cells corresponding to traversals of cognitive maps. Here, we extend this sequence generation model by describing how the synthesis of multiple dynamical systems can support compositional cognitive computations. To empirically validate the model, we simulate two experiments demonstrating compositionality in space or in time during sequence generation. Finally, we describe several neural network architectures supporting various types of compositionality based on grid coding and highlight connections to recent work in machine learning leveraging analogous techniques.
    Language English
    Publishing date 2022-12-08
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2014734-X
    ISSN 1099-4300 ; 1099-4300
    ISSN (online) 1099-4300
    ISSN 1099-4300
    DOI 10.3390/e24121791
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  5. Book ; Online: Minimum Description Length Control

    Moskovitz, Ted / Kao, Ta-Chu / Sahani, Maneesh / Botvinick, Matthew M.

    2022  

    Abstract: We propose a novel framework for multitask reinforcement learning based on the minimum description length (MDL) principle. In this approach, which we term MDL-control (MDL-C), the agent learns the common structure among the tasks with which it is faced ... ...

    Abstract We propose a novel framework for multitask reinforcement learning based on the minimum description length (MDL) principle. In this approach, which we term MDL-control (MDL-C), the agent learns the common structure among the tasks with which it is faced and then distills it into a simpler representation which facilitates faster convergence and generalization to new tasks. In doing so, MDL-C naturally balances adaptation to each task with epistemic uncertainty about the task distribution. We motivate MDL-C via formal connections between the MDL principle and Bayesian inference, derive theoretical performance guarantees, and demonstrate MDL-C's empirical effectiveness on both discrete and high-dimensional continuous control tasks.
    Keywords Computer Science - Machine Learning
    Publishing date 2022-07-17
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Meta-Learned Models of Cognition.

    Binz, Marcel / Dasgupta, Ishita / Jagadish, Akshay K / Botvinick, Matthew / Wang, Jane X / Schulz, Eric

    The Behavioral and brain sciences

    2023  , Page(s) 1–38

    Abstract: Psychologists and neuroscientists extensively rely on computational models for studying and analyzing the human mind. Traditionally, such computational models have been hand-designed by expert researchers. Two prominent examples are cognitive ... ...

    Abstract Psychologists and neuroscientists extensively rely on computational models for studying and analyzing the human mind. Traditionally, such computational models have been hand-designed by expert researchers. Two prominent examples are cognitive architectures and Bayesian models of cognition. While the former requires the specification of a fixed set of computational structures and a definition of how these structures interact with each other, the latter necessitates the commitment to a particular prior and a likelihood function which - in combination with Bayes' rule - determine the model's behavior. In recent years, a new framework has established itself as a promising tool for building models of human cognition: the framework of meta-learning. In contrast to the previously mentioned model classes, meta-learned models acquire their inductive biases from experience, i.e., by repeatedly interacting with an environment. However, a coherent research program around meta-learned models of cognition is still missing to this day. The purpose of this article is to synthesize previous work in this field and establish such a research program. We accomplish this by pointing out that meta-learning can be used to construct Bayes-optimal learning algorithms, allowing us to draw strong connections to the rational analysis of cognition. We then discuss several advantages of the meta-learning framework over traditional methods and reexamine prior work in the context of these new insights.
    Language English
    Publishing date 2023-11-23
    Publishing country England
    Document type Journal Article
    ZDB-ID 423721-3
    ISSN 1469-1825 ; 0140-525X
    ISSN (online) 1469-1825
    ISSN 0140-525X
    DOI 10.1017/S0140525X23003266
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1470: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  7. Article ; Online: Intuitive physics learning in a deep-learning model inspired by developmental psychology.

    Piloto, Luis S / Weinstein, Ari / Battaglia, Peter / Botvinick, Matthew

    Nature human behaviour

    2022  Volume 6, Issue 9, Page(s) 1257–1267

    Abstract: Intuitive physics' enables our pragmatic engagement with the physical world and forms a key component of 'common sense' aspects of thought. Current artificial intelligence systems pale in their understanding of intuitive physics, in comparison to even ... ...

    Abstract 'Intuitive physics' enables our pragmatic engagement with the physical world and forms a key component of 'common sense' aspects of thought. Current artificial intelligence systems pale in their understanding of intuitive physics, in comparison to even very young children. Here we address this gap between humans and machines by drawing on the field of developmental psychology. First, we introduce and open-source a machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology. Second, we build a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children. We demonstrate that our model can learn a diverse set of physical concepts, which depends critically on object-level representations, consistent with findings from developmental psychology. We consider the implications of these results both for AI and for research on human cognition.
    MeSH term(s) Artificial Intelligence ; Child ; Child, Preschool ; Deep Learning ; Humans ; Learning ; Physics ; Psychology, Developmental
    Language English
    Publishing date 2022-07-11
    Publishing country England
    Document type Journal Article
    ISSN 2397-3374
    ISSN (online) 2397-3374
    DOI 10.1038/s41562-022-01394-8
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  8. Article ; Online: Publisher Correction: Intuitive physics learning in a deep-learning model inspired by developmental psychology.

    Piloto, Luis S / Weinstein, Ari / Battaglia, Peter / Botvinick, Matthew

    Nature human behaviour

    2022  Volume 6, Issue 8, Page(s) 1181

    Language English
    Publishing date 2022-07-20
    Publishing country England
    Document type Published Erratum
    ISSN 2397-3374
    ISSN (online) 2397-3374
    DOI 10.1038/s41562-022-01432-5
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  9. Book ; Online: A Unified Theory of Dual-Process Control

    Moskovitz, Ted / Miller, Kevin / Sahani, Maneesh / Botvinick, Matthew M.

    2022  

    Abstract: Dual-process theories play a central role in both psychology and neuroscience, figuring prominently in fields ranging from executive control to reward-based learning to judgment and decision making. In each of these domains, two mechanisms appear to ... ...

    Abstract Dual-process theories play a central role in both psychology and neuroscience, figuring prominently in fields ranging from executive control to reward-based learning to judgment and decision making. In each of these domains, two mechanisms appear to operate concurrently, one relatively high in computational complexity, the other relatively simple. Why is neural information processing organized in this way? We propose an answer to this question based on the notion of compression. The key insight is that dual-process structure can enhance adaptive behavior by allowing an agent to minimize the description length of its own behavior. We apply a single model based on this observation to findings from research on executive control, reward-based learning, and judgment and decision making, showing that seemingly diverse dual-process phenomena can be understood as domain-specific consequences of a single underlying set of computational principles.
    Keywords Quantitative Biology - Neurons and Cognition
    Publishing date 2022-11-13
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: Author Correction: The hippocampus as a predictive map.

    Stachenfeld, Kimberly L / Botvinick, Matthew M / Gershman, Samuel J

    Nature neuroscience

    2018  Volume 21, Issue 6, Page(s) 895

    Abstract: In the version of this article initially published, equation (7) read. ...

    Abstract In the version of this article initially published, equation (7) read.
    Language English
    Publishing date 2018-05-08
    Publishing country United States
    Document type Published Erratum
    ZDB-ID 1420596-8
    ISSN 1546-1726 ; 1097-6256
    ISSN (online) 1546-1726
    ISSN 1097-6256
    DOI 10.1038/s41593-018-0133-1
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 4891: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)
    Zs.MG 67: Show issues

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