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Grandchamp des Raux H, Ghilardi T, Soderberg C, Ossmy O. The role of action concepts in physical reasoning: insights from late childhood. Philos Trans R Soc Lond B Biol Sci 2024; 379:20230154. [PMID: 39155719 PMCID: PMC11391279 DOI: 10.1098/rstb.2023.0154] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 04/22/2024] [Accepted: 06/28/2024] [Indexed: 08/20/2024] Open
Abstract
A fundamental component of human cognition is the ability to intuitively reason about behaviours of objects and systems in the physical world without resorting to explicit scientific knowledge. This skill was traditionally considered a symbolic process. However, in the last decades, there has been a shift towards ideas of embodiment, suggesting that accessing physical knowledge and predicting physical outcomes is grounded in bodily interactions with the environment. Infants and children, who learn mainly through their embodied experiences, serve as a model to probe the link between reasoning and physical concepts. Here, we tested school-aged children (5- to 15-year-olds) in online reasoning games that involve different physical action concepts such as supporting, launching and clearing. We assessed changes in children's performance and strategies over development and their relationships with the different action concepts. Children reasoned more accurately in problems that involved supporting actions compared to launching or clearing actions. Moreover, when children failed, they were more strategic in subsequent attempts when problems involved support rather than launching or clearing. Children improved with age, but improvements differed across action concepts. Our findings suggest that accessing physical knowledge and predicting physical events are affected by action concepts, and those effects change over development. This article is part of the theme issue 'Minds in movement: embodied cognition in the age of artificial intelligence'.
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Affiliation(s)
- Hélène Grandchamp des Raux
- Centre for Brain and Cognitive Development, Birkbeck, University of London, 32 Torrington Square , London, WC1E 7JL, UK
- Department of Psychological Sciences, Birkbeck, University of London, 32 Torrington Square , London WC1E 7JL, UK
| | - Tommaso Ghilardi
- Centre for Brain and Cognitive Development, Birkbeck, University of London, 32 Torrington Square , London, WC1E 7JL, UK
- Department of Psychological Sciences, Birkbeck, University of London, 32 Torrington Square , London WC1E 7JL, UK
| | - Christina Soderberg
- Centre for Brain and Cognitive Development, Birkbeck, University of London, 32 Torrington Square , London, WC1E 7JL, UK
- Department of Psychological Sciences, Birkbeck, University of London, 32 Torrington Square , London WC1E 7JL, UK
| | - Ori Ossmy
- Centre for Brain and Cognitive Development, Birkbeck, University of London, 32 Torrington Square , London, WC1E 7JL, UK
- Department of Psychological Sciences, Birkbeck, University of London, 32 Torrington Square , London WC1E 7JL, UK
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2
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Yiu E, Kosoy E, Gopnik A. Transmission Versus Truth, Imitation Versus Innovation: What Children Can Do That Large Language and Language-and-Vision Models Cannot (Yet). PERSPECTIVES ON PSYCHOLOGICAL SCIENCE 2024; 19:874-883. [PMID: 37883796 PMCID: PMC11373165 DOI: 10.1177/17456916231201401] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Much discussion about large language models and language-and-vision models has focused on whether these models are intelligent agents. We present an alternative perspective. First, we argue that these artificial intelligence (AI) models are cultural technologies that enhance cultural transmission and are efficient and powerful imitation engines. Second, we explore what AI models can tell us about imitation and innovation by testing whether they can be used to discover new tools and novel causal structures and contrasting their responses with those of human children. Our work serves as a first step in determining which particular representations and competences, as well as which kinds of knowledge or skill, can be derived from particular learning techniques and data. In particular, we explore which kinds of cognitive capacities can be enabled by statistical analysis of large-scale linguistic data. Critically, our findings suggest that machines may need more than large-scale language and image data to allow the kinds of innovation that a small child can produce.
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Affiliation(s)
- Eunice Yiu
- Department of Psychology, University of California, Berkeley
| | - Eliza Kosoy
- Department of Psychology, University of California, Berkeley
| | - Alison Gopnik
- Department of Psychology, University of California, Berkeley
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3
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Tsay JS, Kim HE, McDougle SD, Taylor JA, Haith A, Avraham G, Krakauer JW, Collins AGE, Ivry RB. Fundamental processes in sensorimotor learning: Reasoning, refinement, and retrieval. eLife 2024; 13:e91839. [PMID: 39087986 PMCID: PMC11293869 DOI: 10.7554/elife.91839] [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: 08/14/2023] [Accepted: 07/22/2024] [Indexed: 08/02/2024] Open
Abstract
Motor learning is often viewed as a unitary process that operates outside of conscious awareness. This perspective has led to the development of sophisticated models designed to elucidate the mechanisms of implicit sensorimotor learning. In this review, we argue for a broader perspective, emphasizing the contribution of explicit strategies to sensorimotor learning tasks. Furthermore, we propose a theoretical framework for motor learning that consists of three fundamental processes: reasoning, the process of understanding action-outcome relationships; refinement, the process of optimizing sensorimotor and cognitive parameters to achieve motor goals; and retrieval, the process of inferring the context and recalling a control policy. We anticipate that this '3R' framework for understanding how complex movements are learned will open exciting avenues for future research at the intersection between cognition and action.
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Affiliation(s)
- Jonathan S Tsay
- Department of Psychology, Carnegie Mellon UniversityPittsburghUnited States
- Neuroscience Institute, Carnegie Mellon UniversityPittsburgUnited States
| | - Hyosub E Kim
- School of Kinesiology, University of British ColumbiaVancouverCanada
| | | | - Jordan A Taylor
- Department of Psychology, Princeton UniversityPrincetonUnited States
| | - Adrian Haith
- Department of Neurology, Johns Hopkins UniversityBaltimoreUnited States
| | - Guy Avraham
- Department of Psychology, University of California BerkeleyBerkeleyUnited States
- Helen Wills Neuroscience Institute, University of California BerkeleyBerkeleyUnited States
| | - John W Krakauer
- Department of Neurology, Johns Hopkins UniversityBaltimoreUnited States
- Department of Neuroscience, Johns Hopkins UniversityBaltimoreUnited States
- Santa Fe InstituteSanta FeUnited States
| | - Anne GE Collins
- Department of Psychology, University of California BerkeleyBerkeleyUnited States
- Helen Wills Neuroscience Institute, University of California BerkeleyBerkeleyUnited States
| | - Richard B Ivry
- Department of Psychology, University of California BerkeleyBerkeleyUnited States
- Helen Wills Neuroscience Institute, University of California BerkeleyBerkeleyUnited States
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4
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Brockbank E, Vul E. Repeated rock, paper, scissors play reveals limits in adaptive sequential behavior. Cogn Psychol 2024; 151:101654. [PMID: 38657419 DOI: 10.1016/j.cogpsych.2024.101654] [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: 02/20/2023] [Revised: 03/30/2024] [Accepted: 04/08/2024] [Indexed: 04/26/2024]
Abstract
How do people adapt to others in adversarial settings? Prior work has shown that people often violate rational models of adversarial decision-making in repeated interactions. In particular, in mixed strategy equilibrium (MSE) games, where optimal action selection entails choosing moves randomly, people often do not play randomly, but instead try to outwit their opponents. However, little is known about the adaptive reasoning that underlies these deviations from random behavior. Here, we examine strategic decision-making across repeated rounds of rock, paper, scissors, a well-known MSE game. In experiment 1, participants were paired with bot opponents that exhibited distinct stable move patterns, allowing us to identify the bounds of the complexity of opponent behavior that people can detect and adapt to. In experiment 2, bot opponents instead exploited stable patterns in the human participants' moves, providing a symmetrical bound on the complexity of patterns people can revise in their own behavior. Across both experiments, people exhibited a robust and flexible attention to transition patterns from one move to the next, exploiting these patterns in opponents and modifying them strategically in their own moves. However, their adaptive reasoning showed strong limitations with respect to more sophisticated patterns. Together, results provide a precise and consistent account of the surprisingly limited scope of people's adaptive decision-making in this setting.
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Affiliation(s)
| | - Edward Vul
- University of California San Diego, United States of America
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5
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Allen KR, Smith KA, Bird LA, Tenenbaum JB, Makin TR, Cowie D. Lifelong learning of cognitive styles for physical problem-solving: The effect of embodied experience. Psychon Bull Rev 2024; 31:1364-1375. [PMID: 38049575 PMCID: PMC11192818 DOI: 10.3758/s13423-023-02400-4] [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] [Accepted: 10/03/2023] [Indexed: 12/06/2023]
Abstract
'Embodied cognition' suggests that our bodily experiences broadly shape our cognitive capabilities. We study how embodied experience affects the abstract physical problem-solving styles people use in a virtual task where embodiment does not affect action capabilities. We compare how groups with different embodied experience - 25 children and 35 adults with congenital limb differences versus 45 children and 40 adults born with two hands - perform this task, and find that while there is no difference in overall competence, the groups use different cognitive styles to find solutions. People born with limb differences think more before acting but take fewer attempts to reach solutions. Conversely, development affects the particular actions children use, as well as their persistence with their current strategy. Our findings suggest that while development alters action choices and persistence, differences in embodied experience drive changes in the acquisition of cognitive styles for balancing acting with thinking.
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Affiliation(s)
- Kelsey R Allen
- Department of Brain and Cognitive Sciences, MIT and Center for Brains, Minds, and Machines, Cambridge, MA, USA.
| | - Kevin A Smith
- Department of Brain and Cognitive Sciences, MIT and Center for Brains, Minds, and Machines, Cambridge, MA, USA
| | | | - Joshua B Tenenbaum
- Department of Brain and Cognitive Sciences, MIT and Center for Brains, Minds, and Machines, Cambridge, MA, USA
| | - Tamar R Makin
- MRC Cognition Brain Sciences Unit, University of Cambridge, Cambridge, UK
- Institute of Cognitive Neuroscience, University College London, London, UK
| | - Dorothy Cowie
- Department of Psychology, Durham University, Durham, UK
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6
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Allen K, Brändle F, Botvinick M, Fan JE, Gershman SJ, Gopnik A, Griffiths TL, Hartshorne JK, Hauser TU, Ho MK, de Leeuw JR, Ma WJ, Murayama K, Nelson JD, van Opheusden B, Pouncy T, Rafner J, Rahwan I, Rutledge RB, Sherson J, Şimşek Ö, Spiers H, Summerfield C, Thalmann M, Vélez N, Watrous AJ, Tenenbaum JB, Schulz E. Using games to understand the mind. Nat Hum Behav 2024; 8:1035-1043. [PMID: 38907029 DOI: 10.1038/s41562-024-01878-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 04/03/2024] [Indexed: 06/23/2024]
Abstract
Board, card or video games have been played by virtually every individual in the world. Games are popular because they are intuitive and fun. These distinctive qualities of games also make them ideal for studying the mind. By being intuitive, games provide a unique vantage point for understanding the inductive biases that support behaviour in more complex, ecological settings than traditional laboratory experiments. By being fun, games allow researchers to study new questions in cognition such as the meaning of 'play' and intrinsic motivation, while also supporting more extensive and diverse data collection by attracting many more participants. We describe the advantages and drawbacks of using games relative to standard laboratory-based experiments and lay out a set of recommendations on how to gain the most from using games to study cognition. We hope this Perspective will lead to a wider use of games as experimental paradigms, elevating the ecological validity, scale and robustness of research on the mind.
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Affiliation(s)
| | | | | | | | | | - Alison Gopnik
- University of California, Berkeley, Berkeley, CA, USA
| | | | | | - Tobias U Hauser
- University College London, London, UK
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London, UK
- University of Tübingen, Tübingen, Germany
| | - Mark K Ho
- Princeton University, Princeton, NJ, USA
| | | | - Wei Ji Ma
- New York University, New York, NY, USA
| | | | | | | | | | | | - Iyad Rahwan
- Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany
| | | | | | | | | | | | - Mirko Thalmann
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | | | | | | | - Eric Schulz
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany.
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7
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Huang T, Liu J. A stochastic world model on gravity for stability inference. eLife 2024; 12:RP88953. [PMID: 38712832 DOI: 10.7554/elife.88953] [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] [Indexed: 05/08/2024] Open
Abstract
The fact that objects without proper support will fall to the ground is not only a natural phenomenon, but also common sense in mind. Previous studies suggest that humans may infer objects' stability through a world model that performs mental simulations with a priori knowledge of gravity acting upon the objects. Here we measured participants' sensitivity to gravity to investigate how the world model works. We found that the world model on gravity was not a faithful replica of the physical laws, but instead encoded gravity's vertical direction as a Gaussian distribution. The world model with this stochastic feature fit nicely with participants' subjective sense of objects' stability and explained the illusion that taller objects are perceived as more likely to fall. Furthermore, a computational model with reinforcement learning revealed that the stochastic characteristic likely originated from experience-dependent comparisons between predictions formed by internal simulations and the realities observed in the external world, which illustrated the ecological advantage of stochastic representation in balancing accuracy and speed for efficient stability inference. The stochastic world model on gravity provides an example of how a priori knowledge of the physical world is implemented in mind that helps humans operate flexibly in open-ended environments.
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Affiliation(s)
- Taicheng Huang
- Department of Psychological and Cognitive Sciences & Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing, China
| | - Jia Liu
- Department of Psychological and Cognitive Sciences & Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing, China
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8
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Tsay JS, Asmerian H, Germine LT, Wilmer J, Ivry RB, Nakayama K. Large-scale citizen science reveals predictors of sensorimotor adaptation. Nat Hum Behav 2024; 8:510-525. [PMID: 38291127 DOI: 10.1038/s41562-023-01798-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 12/04/2023] [Indexed: 02/01/2024]
Abstract
Sensorimotor adaptation is essential for keeping our movements well calibrated in response to changes in the body and environment. For over a century, researchers have studied sensorimotor adaptation in laboratory settings that typically involve small sample sizes. While this approach has proved useful for characterizing different learning processes, laboratory studies are not well suited for exploring the myriad of factors that may modulate human performance. Here, using a citizen science website, we collected over 2,000 sessions of data on a visuomotor rotation task. This unique dataset has allowed us to replicate, reconcile and challenge classic findings in the learning and memory literature, as well as discover unappreciated demographic constraints associated with implicit and explicit processes that support sensorimotor adaptation. More generally, this study exemplifies how a large-scale exploratory approach can complement traditional hypothesis-driven laboratory research in advancing sensorimotor neuroscience.
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Affiliation(s)
- Jonathan S Tsay
- Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA.
| | - Hrach Asmerian
- Department of Psychology, University of California, Berkeley, Berkeley, CA, USA.
| | - Laura T Germine
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, USA
| | - Jeremy Wilmer
- Department of Psychology, Wellesley College, Wellesley, MA, USA
| | - Richard B Ivry
- Department of Psychology, University of California, Berkeley, Berkeley, CA, USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Ken Nakayama
- Department of Psychology, University of California, Berkeley, Berkeley, CA, USA
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9
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Wise T, Emery K, Radulescu A. Naturalistic reinforcement learning. Trends Cogn Sci 2024; 28:144-158. [PMID: 37777463 PMCID: PMC10878983 DOI: 10.1016/j.tics.2023.08.016] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 08/23/2023] [Accepted: 08/24/2023] [Indexed: 10/02/2023]
Abstract
Humans possess a remarkable ability to make decisions within real-world environments that are expansive, complex, and multidimensional. Human cognitive computational neuroscience has sought to exploit reinforcement learning (RL) as a framework within which to explain human decision-making, often focusing on constrained, artificial experimental tasks. In this article, we review recent efforts that use naturalistic approaches to determine how humans make decisions in complex environments that better approximate the real world, providing a clearer picture of how humans navigate the challenges posed by real-world decisions. These studies purposely embed elements of naturalistic complexity within experimental paradigms, rather than focusing on simplification, generating insights into the processes that likely underpin humans' ability to navigate complex, multidimensional real-world environments so successfully.
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Affiliation(s)
- Toby Wise
- Department of Neuroimaging, King's College London, London, UK.
| | - Kara Emery
- Center for Data Science, New York University, New York, NY, USA
| | - Angela Radulescu
- Center for Computational Psychiatry, Icahn School of Medicine at Mt. Sinai, New York, NY, USA
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10
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Liu Y, Ayzenberg V, Lourenco SF. Object geometry serves humans' intuitive physics of stability. Sci Rep 2024; 14:1701. [PMID: 38242998 PMCID: PMC10799025 DOI: 10.1038/s41598-024-51677-5] [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: 04/18/2023] [Accepted: 01/08/2024] [Indexed: 01/21/2024] Open
Abstract
How do humans judge physical stability? A prevalent account emphasizes the mental simulation of physical events implemented by an intuitive physics engine in the mind. Here we test the extent to which the perceptual features of object geometry are sufficient for supporting judgments of falling direction. In all experiments, adults and children judged the falling direction of a tilted object and, across experiments, objects differed in the geometric features (i.e., geometric centroid, object height, base size and/or aspect ratio) relevant to the judgment. Participants' performance was compared to computational models trained on geometric features, as well as a deep convolutional neural network (ResNet-50), none of which incorporated mental simulation. Adult and child participants' performance was well fit by models of object geometry, particularly the geometric centroid. ResNet-50 also provided a good account of human performance. Altogether, our findings suggest that object geometry may be sufficient for judging the falling direction of tilted objects, independent of mental simulation.
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Affiliation(s)
- Yaxin Liu
- Emory University, 36 Eagle Row, Atlanta, GA, 30322, USA.
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11
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McCarthy WP, Kirsh D, Fan JE. Consistency and Variation in Reasoning About Physical Assembly. Cogn Sci 2023; 47:e13397. [PMID: 38146204 DOI: 10.1111/cogs.13397] [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: 01/17/2023] [Revised: 10/27/2023] [Accepted: 12/06/2023] [Indexed: 12/27/2023]
Abstract
The ability to reason about how things were made is a pervasive aspect of how humans make sense of physical objects. Such reasoning is useful for a range of everyday tasks, from assembling a piece of furniture to making a sandwich and knitting a sweater. What enables people to reason in this way even about novel objects, and how do people draw upon prior experience with an object to continually refine their understanding of how to create it? To explore these questions, we developed a virtual task environment to investigate how people come up with step-by-step procedures for recreating block towers whose composition was not readily apparent, and analyzed how the procedures they used to build them changed across repeated attempts. Specifically, participants (N = 105) viewed 2D silhouettes of eight unique block towers in a virtual environment simulating rigid-body physics, and aimed to reconstruct each one in less than 60 s. We found that people built each tower more accurately and quickly across repeated attempts, and that this improvement reflected both group-level convergence upon a tiny fraction of all possible viable procedures, as well as error-dependent updating across successive attempts by the same individual. Taken together, our study presents a scalable approach to measuring consistency and variation in how people infer solutions to physical assembly problems.
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Affiliation(s)
| | - David Kirsh
- Department of Cognitive Science, University of California San Diego
| | - Judith E Fan
- Department of Psychology, University of California San Diego
- Department of Psychology, Stanford University
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12
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Abstract
Planning underpins the impressive flexibility of goal-directed behavior. However, even when planning, people can display surprising rigidity in how they think about problems (e.g., "functional fixedness") that lead them astray. How can our capacity for behavioral flexibility be reconciled with our susceptibility to conceptual inflexibility? We propose that these tendencies reflect avoidance of two cognitive costs: the cost of representing task details and the cost of switching between representations. To test this hypothesis, we developed a novel paradigm that affords participants opportunities to choose different families of simplified representations to plan. In two preregistered, online studies (Ns = 377 and 294 adults), we found that participants' optimal behavior, suboptimal behavior, and reaction time were explained by a computational model that formalized people's avoidance of representational complexity and switching. These results demonstrate how the selection of simplified, rigid representations leads to the otherwise puzzling combination of flexibility and inflexibility observed in problem solving.
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Affiliation(s)
- Mark K Ho
- Department of Psychology, Princeton University
- Department of Computer Science, Princeton University
| | | | - Thomas L Griffiths
- Department of Psychology, Princeton University
- Department of Computer Science, Princeton University
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13
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Bramley NR, Xu F. Active inductive inference in children and adults: A constructivist perspective. Cognition 2023; 238:105471. [PMID: 37236019 DOI: 10.1016/j.cognition.2023.105471] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 02/27/2023] [Accepted: 04/24/2023] [Indexed: 05/28/2023]
Abstract
A defining aspect of being human is an ability to reason about the world by generating and adapting ideas and hypotheses. Here we explore how this ability develops by comparing children's and adults' active search and explicit hypothesis generation patterns in a task that mimics the open-ended process of scientific induction. In our experiment, 54 children (aged 8.97±1.11) and 50 adults performed inductive inferences about a series of causal rules through active testing. Children were more elaborate in their testing behavior and generated substantially more complex guesses about the hidden rules. We take a 'computational constructivist' perspective to explaining these patterns, arguing that these inferences are driven by a combination of thinking (generating and modifying symbolic concepts) and exploring (discovering and investigating patterns in the physical world). We show how this framework and rich new dataset speak to questions about developmental differences in hypothesis generation, active learning and inductive generalization. In particular, we find children's learning is driven by less fine-tuned construction mechanisms than adults', resulting in a greater diversity of ideas but less reliable discovery of simple explanations.
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Affiliation(s)
- Neil R Bramley
- Department of Psychology, University of Edinburgh, Scotland, United Kingdom.
| | - Fei Xu
- Psychology Department, University of California, Berkeley, USA
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14
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Singh M, Mehr SA. Universality, domain-specificity, and development of psychological responses to music. NATURE REVIEWS PSYCHOLOGY 2023; 2:333-346. [PMID: 38143935 PMCID: PMC10745197 DOI: 10.1038/s44159-023-00182-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/30/2023] [Indexed: 12/26/2023]
Abstract
Humans can find music happy, sad, fearful, or spiritual. They can be soothed by it or urged to dance. Whether these psychological responses reflect cognitive adaptations that evolved expressly for responding to music is an ongoing topic of study. In this Review, we examine three features of music-related psychological responses that help to elucidate whether the underlying cognitive systems are specialized adaptations: universality, domain-specificity, and early expression. Focusing on emotional and behavioural responses, we find evidence that the relevant psychological mechanisms are universal and arise early in development. However, the existing evidence cannot establish that these mechanisms are domain-specific. To the contrary, many findings suggest that universal psychological responses to music reflect more general properties of emotion, auditory perception, and other human cognitive capacities that evolved for non-musical purposes. Cultural evolution, driven by the tinkering of musical performers, evidently crafts music to compellingly appeal to shared psychological mechanisms, resulting in both universal patterns (such as form-function associations) and culturally idiosyncratic styles.
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Affiliation(s)
- Manvir Singh
- Institute for Advanced Study in Toulouse, University of
Toulouse 1 Capitole, Toulouse, France
| | - Samuel A. Mehr
- Yale Child Study Center, Yale University, New Haven, CT,
USA
- School of Psychology, University of Auckland, Auckland,
New Zealand
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15
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Xue C, Pinto V, Gamage C, Nikonova E, Zhang P, Renz J. Phy-Q as a measure for physical reasoning intelligence. NAT MACH INTELL 2023. [DOI: 10.1038/s42256-022-00583-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
AbstractHumans are well versed in reasoning about the behaviours of physical objects and choosing actions accordingly to accomplish tasks, while this remains a major challenge for artificial intelligence. To facilitate research addressing this problem, we propose a new testbed that requires an agent to reason about physical scenarios and take an action appropriately. Inspired by the physical knowledge acquired in infancy and the capabilities required for robots to operate in real-world environments, we identify 15 essential physical scenarios. We create a wide variety of distinct task templates, and we ensure that all the task templates within the same scenario can be solved by using one specific strategic physical rule. By having such a design, we evaluate two distinct levels of generalization, namely local generalization and broad generalization. We conduct an extensive evaluation with human players, learning agents with various input types and architectures, and heuristic agents with different strategies. Inspired by how the human intelligence quotient is calculated, we define the physical reasoning quotient (Phy-Q score) that reflects the physical reasoning intelligence of an agent using the physical scenarios we considered. Our evaluation shows that (1) all the agents are far below human performance, and (2) learning agents, even with good local generalization ability, struggle to learn the underlying physical reasoning rules and fail to generalize broadly. We encourage the development of intelligent agents that can reach the human-level Phy-Q score.
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16
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Osiurak F, Claidière N, Federico G. Bringing cumulative technological culture beyond copying versus reasoning. Trends Cogn Sci 2023; 27:30-42. [PMID: 36283920 DOI: 10.1016/j.tics.2022.09.024] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 09/28/2022] [Accepted: 09/29/2022] [Indexed: 11/06/2022]
Abstract
The dominant view of cumulative technological culture suggests that high-fidelity transmission rests upon a high-fidelity copying ability, which allows individuals to reproduce the tool-use actions performed by others without needing to understand them (i.e., without causal reasoning). The opposition between copying versus reasoning is well accepted but with little supporting evidence. In this article, we investigate this distinction by examining the cognitive science literature on tool use. Evidence indicates that the ability to reproduce others' tool-use actions requires causal understanding, which questions the copying versus reasoning distinction and the cognitive reality of the so-called copying ability. We conclude that new insights might be gained by considering causal understanding as a key driver of cumulative technological culture.
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Affiliation(s)
- François Osiurak
- Laboratoire d'Étude des Mécanismes Cognitifs, Université de Lyon, 5 avenue Pierre Mendès France, 69676 Bron Cedex, France; Institut Universitaire de France, 1 rue Descartes, 75231 Paris Cedex 5, France.
| | - Nicolas Claidière
- Aix-Marseille Univ, CNRS, LPC, 3 Place Victor Hugo, 13331 Marseille, France
| | - Giovanni Federico
- IRCCS Synlab SDN S.p.A., Via Emanuele Gianturco 113, 80143, Naples, Italy
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17
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Singh M. Subjective selection and the evolution of complex culture. Evol Anthropol 2022; 31:266-280. [PMID: 36165208 DOI: 10.1002/evan.21948] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 11/30/2021] [Accepted: 05/28/2022] [Indexed: 12/27/2022]
Abstract
Why is culture the way it is? Here I argue that a major force shaping culture is subjective (cultural) selection, or the selective retention of cultural variants that people subjectively perceive as satisfying their goals. I show that people evaluate behaviors and beliefs according to how useful they are, especially for achieving goals. As they adopt and pass on those variants that seem best, they iteratively craft culture into increasingly effective-seeming forms. I argue that this process drives the development of many cumulatively complex cultural products, including effective technology, magic and ritual, aesthetic traditions, and institutions. I show that it can explain cultural dependencies, such as how certain beliefs create corresponding new practices, and I outline how it interacts with other cultural evolutionary processes. Cultural practices everywhere, from spears to shamanism, develop because people subjectively evaluate them to be effective means of satisfying regular goals.
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Affiliation(s)
- Manvir Singh
- Institute for Advanced Study in Toulouse, Université de Toulouse 1 Capitole, Toulouse, France
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18
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Riochet R, Castro MY, Bernard M, Lerer A, Fergus R, Izard V, Dupoux E. IntPhys 2019: A Benchmark for Visual Intuitive Physics Understanding. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022; 44:5016-5025. [PMID: 34038357 DOI: 10.1109/tpami.2021.3083839] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
In order to reach human performance on complex visual tasks, artificial systems need to incorporate a significant amount of understanding of the world in terms of macroscopic objects, movements, forces, etc. Inspired by work on intuitive physics in infants, we propose an evaluation benchmark which diagnoses how much a given system understands about physics by testing whether it can tell apart well matched videos of possible versus impossible events constructed with a game engine. The test requires systems to compute a physical plausibility score over an entire video. To prevent perceptual biases, the dataset is made of pixel matched quadruplets of videos, enforcing systems to focus on high level temporal dependencies between frames rather than pixel-level details. We then describe two Deep Neural Networks systems aimed at learning intuitive physics in an unsupervised way, using only physically possible videos. The systems are trained with a future semantic mask prediction objective and tested on the possible versus impossible discrimination task. The analysis of their results compared to human data gives novel insights in the potentials and limitations of next frame prediction architectures.
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19
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Pramod RT, Cohen MA, Tenenbaum JB, Kanwisher N. Invariant representation of physical stability in the human brain. eLife 2022; 11:e71736. [PMID: 35635277 PMCID: PMC9150889 DOI: 10.7554/elife.71736] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 05/10/2022] [Indexed: 11/13/2022] Open
Abstract
Successful engagement with the world requires the ability to predict what will happen next. Here, we investigate how the brain makes a fundamental prediction about the physical world: whether the situation in front of us is stable, and hence likely to stay the same, or unstable, and hence likely to change in the immediate future. Specifically, we ask if judgments of stability can be supported by the kinds of representations that have proven to be highly effective at visual object recognition in both machines and brains, or instead if the ability to determine the physical stability of natural scenes may require generative algorithms that simulate the physics of the world. To find out, we measured responses in both convolutional neural networks (CNNs) and the brain (using fMRI) to natural images of physically stable versus unstable scenarios. We find no evidence for generalizable representations of physical stability in either standard CNNs trained on visual object and scene classification (ImageNet), or in the human ventral visual pathway, which has long been implicated in the same process. However, in frontoparietal regions previously implicated in intuitive physical reasoning we find both scenario-invariant representations of physical stability, and higher univariate responses to unstable than stable scenes. These results demonstrate abstract representations of physical stability in the dorsal but not ventral pathway, consistent with the hypothesis that the computations underlying stability entail not just pattern classification but forward physical simulation.
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Affiliation(s)
- RT Pramod
- Center for Brains, Minds and Machines, Massachusetts Institute of TechnologyCambridgeUnited States
- Department of Brain and Cognitive Sciences, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Michael A Cohen
- Department of Brain and Cognitive Sciences, Massachusetts Institute of TechnologyCambridgeUnited States
- Amherst CollegeAmherstUnited States
| | - Joshua B Tenenbaum
- Center for Brains, Minds and Machines, Massachusetts Institute of TechnologyCambridgeUnited States
- Department of Brain and Cognitive Sciences, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Nancy Kanwisher
- Center for Brains, Minds and Machines, Massachusetts Institute of TechnologyCambridgeUnited States
- Department of Brain and Cognitive Sciences, Massachusetts Institute of TechnologyCambridgeUnited States
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20
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Baumard J, Lesourd M, Guézouli L, Osiurak F. Physical understanding in neurodegenerative diseases. Cogn Neuropsychol 2022; 38:490-514. [PMID: 35549825 DOI: 10.1080/02643294.2022.2071152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
This quantitative review gives an overview of physical understanding (i.e., the ability to represent and use the laws of physics to interact with the physical world) impairments in Alzheimer's disease (AD), semantic dementia (SD), and corticobasal syndrome (CBS), as assessed mainly with mechanical problem-solving and tool use tests. This review shows that: (1) SD patients have apraxia of tool use because of semantic tool knowledge deficits, but normal performance in tests of physical understanding; (2) AD and CBS patients show impaired performance in mechanical problem-solving tests, probably not because of intrinsic deficits of physical understanding, but rather because of additional cognitive (AD) or motor impairments (CBS); (3) As a result, the performance in mechanical problem-solving tests is not a good predictor of familiar tool use in dementia; (4) Actual deficits of physical understanding are probably observed only in late stages of neurodegenerative diseases, and associated with functional loss.
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Affiliation(s)
- Josselin Baumard
- Normandie Univ, UNIROUEN, CRFDP (EA 7475), 76000 Rouen, France.,Centre de Recherche sur les Fonctionnements et Dysfonctionnements Psychologiques (EA 7475), Mont-Saint-Aignan Cedex, France
| | - Mathieu Lesourd
- Laboratoire de Recherches Intégratives en Neurosciences et Psychologie Cognitive, Université Bourgogne Franche-Comté Besançon, France.,MSHE Ledoux, CNRS, Université de Bourgogne Franche-Comté, Besançon, France
| | - Léna Guézouli
- Normandie Univ, UNIROUEN, CRFDP (EA 7475), 76000 Rouen, France.,Centre de Recherche sur les Fonctionnements et Dysfonctionnements Psychologiques (EA 7475), Mont-Saint-Aignan Cedex, France
| | - François Osiurak
- Laboratoire d'Etude des Mécanismes Cognitifs (EA 3082), Université de Lyon, Bron Cedex, France.,Institut Universitaire de France, Paris, France
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21
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Neupärtl N, Tatai F, Rothkopf CA. Naturalistic embodied interactions elicit intuitive physical behaviour in accordance with Newtonian physics. Cogn Neuropsychol 2021; 38:440-454. [PMID: 34877918 DOI: 10.1080/02643294.2021.2008890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
The success of visuomotor interactions in everyday activities such as grasping or sliding a cup is inescapably governed by the laws of physics. Research on intuitive physics has predominantly investigated reasoning about objects' behaviour involving binary forced choice responses. We investigated how the type of visuomotor response influences participants' beliefs about physical quantities and their lawful relationship implicit in their active behaviour. Participants propelled pucks towards targets positioned at different distances. Analysis with a probabilistic model of interactions showed that subjects adopted the non-linear control prescribed by Newtonian physics when sliding real pucks in a virtual environment even in the absence of visual feedback. However, they used a linear heuristic when viewing the scene on a monitor and interactions were implemented through key presses. These results support the notion of probabilistic internal physics models but additionally suggest that humans can take advantage of embodied, sensorimotor, multimodal representations in physical scenarios.
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Affiliation(s)
- Nils Neupärtl
- Institute of Psychology, TU Darmstadt, Darmstadt, Germany.,Centre for Cognitive Science, TU Darmstadt, Darmstadt, Germany
| | - Fabian Tatai
- Institute of Psychology, TU Darmstadt, Darmstadt, Germany.,Centre for Cognitive Science, TU Darmstadt, Darmstadt, Germany
| | - Constantin A Rothkopf
- Institute of Psychology, TU Darmstadt, Darmstadt, Germany.,Centre for Cognitive Science, TU Darmstadt, Darmstadt, Germany.,Frankfurt Institute for Advanced Studies, Goethe University, Frankfurt, Germany
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22
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Friston K, Moran RJ, Nagai Y, Taniguchi T, Gomi H, Tenenbaum J. World model learning and inference. Neural Netw 2021; 144:573-590. [PMID: 34634605 DOI: 10.1016/j.neunet.2021.09.011] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 07/28/2021] [Accepted: 09/09/2021] [Indexed: 11/19/2022]
Abstract
Understanding information processing in the brain-and creating general-purpose artificial intelligence-are long-standing aspirations of scientists and engineers worldwide. The distinctive features of human intelligence are high-level cognition and control in various interactions with the world including the self, which are not defined in advance and are vary over time. The challenge of building human-like intelligent machines, as well as progress in brain science and behavioural analyses, robotics, and their associated theoretical formalisations, speaks to the importance of the world-model learning and inference. In this article, after briefly surveying the history and challenges of internal model learning and probabilistic learning, we introduce the free energy principle, which provides a useful framework within which to consider neuronal computation and probabilistic world models. Next, we showcase examples of human behaviour and cognition explained under that principle. We then describe symbol emergence in the context of probabilistic modelling, as a topic at the frontiers of cognitive robotics. Lastly, we review recent progress in creating human-like intelligence by using novel probabilistic programming languages. The striking consensus that emerges from these studies is that probabilistic descriptions of learning and inference are powerful and effective ways to create human-like artificial intelligent machines and to understand intelligence in the context of how humans interact with their world.
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Affiliation(s)
- Karl Friston
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London (UCL), WC1N 3BG, UK.
| | - Rosalyn J Moran
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, SE5 8AF, UK.
| | - Yukie Nagai
- International Research Center for Neurointelligence (IRCN), The University of Tokyo, Tokyo, Japan.
| | - Tadahiro Taniguchi
- College of Information Science and Engineering, Ritsumeikan University, Shiga, Japan.
| | - Hiroaki Gomi
- NTT Communication Science Labs., Nippon Telegraph and Telephone, Kanawaga, Japan.
| | - Josh Tenenbaum
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA; The Center for Brains, Minds and Machines, MIT, Cambridge, MA, USA.
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23
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Peters B, Kriegeskorte N. Capturing the objects of vision with neural networks. Nat Hum Behav 2021; 5:1127-1144. [PMID: 34545237 DOI: 10.1038/s41562-021-01194-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2019] [Accepted: 08/06/2021] [Indexed: 01/31/2023]
Abstract
Human visual perception carves a scene at its physical joints, decomposing the world into objects, which are selectively attended, tracked and predicted as we engage our surroundings. Object representations emancipate perception from the sensory input, enabling us to keep in mind that which is out of sight and to use perceptual content as a basis for action and symbolic cognition. Human behavioural studies have documented how object representations emerge through grouping, amodal completion, proto-objects and object files. By contrast, deep neural network models of visual object recognition remain largely tethered to sensory input, despite achieving human-level performance at labelling objects. Here, we review related work in both fields and examine how these fields can help each other. The cognitive literature provides a starting point for the development of new experimental tasks that reveal mechanisms of human object perception and serve as benchmarks driving the development of deep neural network models that will put the object into object recognition.
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Affiliation(s)
- Benjamin Peters
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA.
| | - Nikolaus Kriegeskorte
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA. .,Department of Psychology, Columbia University, New York, NY, USA. .,Department of Neuroscience, Columbia University, New York, NY, USA. .,Department of Electrical Engineering, Columbia University, New York, NY, USA.
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24
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Rounis E, Halai A, Pizzamiglio G, Lambon Ralph MA. Characterising factors underlying praxis deficits in chronic left hemisphere stroke patients. Cortex 2021; 142:154-168. [PMID: 34271260 DOI: 10.1016/j.cortex.2021.04.019] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 03/02/2021] [Accepted: 04/29/2021] [Indexed: 11/17/2022]
Abstract
Limb apraxia, a disorder of skilled action not consequent on primary motor or sensory deficits, has traditionally been defined according to errors patients make on neuropsychological tasks. Previous models of the disorder have failed to provide a unified account of patients' deficits, due to heterogeneity in the patients and tasks used. In this study we hypothesised that we may be able to map apraxic deficits onto principal components, some of which may be specific, whilst others may align with other cognitive disorders. We implemented principal component analysis (PCA) to elucidate core factors of the disorder in a preliminary cohort of 41 unselected left hemisphere chronic stroke patients who were tested on a comprehensive and validated apraxia screen. Three principal components were identified: posture selection, semantic control and multi-demand sequencing. These were submitted to a lesion symptom mapping (VBCM) analysis in a subset of 24 patients, controlled for lesion volume, age and time post-stroke. The first component revealed no significant structural correlates. The second component was related to regions in inferior frontal gyrus, primary motor area, and adjacent parietal opercular (including inferior parietal and supramarginal gyrus) areas. The third component was associated with lesions within the white matter underlying the left sensorimotor cortex, likely involving the 2nd branch of the left superior longitudinal fasciculus as well as the posterior orbitofrontal cortex (pOFC). These results highlight a significant role of common cognitive functions in apraxia, which include action selection, and sequencing, whilst more specific deficits may relate to semantic control. Moreover, they suggest that previously described 'ideomotor' and 'ideational' deficits may have a common neural basis within semantic control. Further research using this technique would help elucidate the cognitive processes underlying limb apraxia, its neural correlates and their relationship with other cognitive disorders.
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Affiliation(s)
- Elisabeth Rounis
- Chelsea and Westminster NHS Foundation Trust, West Middlesex University Hospital, Isleworth, UK; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
| | - Ajay Halai
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
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25
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Ota K, Jha DK, Romeres D, van Baar J, Smith KA, Semitsu T, Oiki T, Sullivan A, Nikovski D, Tenenbaum JB. Data-Efficient Learning for Complex and Real-Time Physical Problem Solving Using Augmented Simulation. IEEE Robot Autom Lett 2021. [DOI: 10.1109/lra.2021.3068887] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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26
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Abstract
The Ouroboros Model has been proposed as a biologically-inspired comprehensive cognitive architecture for general intelligence, comprising natural and artificial manifestations. The approach addresses very diverse fundamental desiderata of research in natural cognition and also artificial intelligence, AI. Here, it is described how the postulated structures have met with supportive evidence over recent years. The associated hypothesized processes could remedy pressing problems plaguing many, and even the most powerful current implementations of AI, including in particular deep neural networks. Some selected recent findings from very different fields are summoned, which illustrate the status and substantiate the proposal.
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27
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Shiffrin RM, Bassett DS, Kriegeskorte N, Tenenbaum JB. The brain produces mind by modeling. Proc Natl Acad Sci U S A 2020; 117:29299-29301. [PMID: 33229525 PMCID: PMC7703556 DOI: 10.1073/pnas.1912340117] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/28/2023] Open
Affiliation(s)
- Richard M Shiffrin
- Psychological and Brain Sciences Department, Indiana University, Bloomington, IN 47405;
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104
| | | | - Joshua B Tenenbaum
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139-4307
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