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Lenaerts T, Saponara M, Pacheco JM, Santos FC. Evolution of a theory of mind. iScience 2024; 27:108862. [PMID: 38303708 PMCID: PMC10830857 DOI: 10.1016/j.isci.2024.108862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Revised: 12/04/2023] [Accepted: 01/08/2024] [Indexed: 02/03/2024] Open
Abstract
Even though the Theory of Mind in upper primates has been under investigation for decades, how it may evolve remains an open problem. We propose here an evolutionary game theoretical model where a finite population of individuals may use reasoning strategies to infer a response to the anticipated behavior of others within the context of a sequential dilemma, i.e., the Centipede Game. We show that strategies with bounded reasoning evolve and flourish under natural selection, provided they are allowed to make reasoning mistakes and a temptation for higher future gains is in place. We further show that non-deterministic reasoning co-evolves with an optimism bias that may lead to the selection of new equilibria, closely associated with average behavior observed in experimental data. This work reveals both a novel perspective on the evolution of bounded rationality and a co-evolutionary link between the evolution of Theory of Mind and the emergence of misbeliefs.
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Affiliation(s)
- Tom Lenaerts
- Machine Learning Group, Département d’Informatique, Université Libre de Bruxelles, 1050 Brussels, Belgium
- Artificial Intelligence Lab, Vakgroep Computerwetenschappen, Vrije Universiteit Brussel, 1050 Brussels, Belgium
- Center for Human-Compatible AI, University of California, Berkeley, Berkeley, CA 94702, USA
| | - Marco Saponara
- Machine Learning Group, Département d’Informatique, Université Libre de Bruxelles, 1050 Brussels, Belgium
| | - Jorge M. Pacheco
- Centro de Biologia Molecular e Ambiental, Universidade do Minho, 4710 - 057 Braga, Portugal
- Departamento de Matemática e Aplicações, Universidade do Minho, 4710 - 057 Braga, Portugal
- ATP-group, P-2744-016 Porto Salvo, Portugal
| | - Francisco C. Santos
- ATP-group, P-2744-016 Porto Salvo, Portugal
- INESC-ID and Instituto Superior Técnico, Universidade de Lisboa, IST-Taguspark, 2744-016 Porto Salvo, Portugal
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2
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Gorgan Mohammadi A, Ganjtabesh M. On computational models of theory of mind and the imitative reinforcement learning in spiking neural networks. Sci Rep 2024; 14:1945. [PMID: 38253595 PMCID: PMC10803361 DOI: 10.1038/s41598-024-52299-7] [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: 09/10/2023] [Accepted: 01/16/2024] [Indexed: 01/24/2024] Open
Abstract
Theory of Mind is referred to the ability of inferring other's mental states, and it plays a crucial role in social cognition and learning. Biological evidences indicate that complex circuits are involved in this ability, including the mirror neuron system. The mirror neuron system influences imitation abilities and action understanding, leading to learn through observing others. To simulate this imitative learning behavior, a Theory-of-Mind-based Imitative Reinforcement Learning (ToM-based ImRL) framework is proposed. Employing the bio-inspired spiking neural networks and the mechanisms of the mirror neuron system, ToM-based ImRL is a bio-inspired computational model which enables an agent to effectively learn how to act in an interactive environment through observing an expert, inferring its goals, and imitating its behaviors. The aim of this paper is to review some computational attempts in modeling ToM and to explain the proposed ToM-based ImRL framework which is tested in the environment of River Raid game from Atari 2600 series.
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Affiliation(s)
- Ashena Gorgan Mohammadi
- Department of Computer Science, School of Mathematics, Statistics, and Computer Science, College of Science, University of Tehran, Tehran, Iran
| | - Mohammad Ganjtabesh
- Department of Computer Science, School of Mathematics, Statistics, and Computer Science, College of Science, University of Tehran, Tehran, Iran.
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3
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Gatti D, Stagnitto SM, Basile C, Mazzoni G, Vecchi T, Rinaldi L, Lecce S. Individual differences in theory of mind correlate with the occurrence of false memory: A study with the DRM task. Q J Exp Psychol (Hove) 2023; 76:2107-2121. [PMID: 36245220 DOI: 10.1177/17470218221135178] [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] [Indexed: 11/16/2022]
Abstract
Although long-term memory and Theory of Mind (ToM) are closely related across the whole lifespan, little is known about the relationship between ToM and semantic memory. Clinical studies have documented the co-occurrence of ToM impairments and semantic memory abnormalities in individuals with autism or semantic dementia. However, to date, no study has directly investigated the existence of a relationship between ToM and semantic memory in the typical population. We addressed this gap on a sample of 103 healthy adults (M age = 22.96 years; age range = 19-35 years). Participants completed a classical false memory task tapping on semantic processes, the Deese-Roediger-McDermott (DRM) task, and two ToM tasks, the Triangles and the Reading the Mind in the Eyes task. They also completed the vocabulary scale from the Wechsler Adult Intelligence Scale. Results showed that participants' semantic performance in the DRM task was significantly related to that in the Triangles task. Specifically, the higher participants' ToM in the Triangles task, the higher participants' reliance on semantic memory while making false memories in the DRM task. Our findings are consistent with the Fuzzy Trace Theory and the Weak Central Coherence account and suggest that a (partially) common cognitive process responsible for global versus detailed-focus information processing could underlie these two abilities.
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Affiliation(s)
- Daniele Gatti
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | | | - Chiara Basile
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Giuliana Mazzoni
- Faculty of Medicine and Psychology, University La Sapienza, Rome, Italy
- School of Life Sciences, University of Hull, Hull, UK
| | - Tomaso Vecchi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Cognitive Psychology Unit, IRCCS Mondino Foundation, Pavia, Italy
| | - Luca Rinaldi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Cognitive Psychology Unit, IRCCS Mondino Foundation, Pavia, Italy
| | - Serena Lecce
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
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4
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Alon N, Schulz L, Rosenschein JS, Dayan P. A (Dis-)information Theory of Revealed and Unrevealed Preferences: Emerging Deception and Skepticism via Theory of Mind. Open Mind (Camb) 2023; 7:608-624. [PMID: 37840764 PMCID: PMC10575559 DOI: 10.1162/opmi_a_00097] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 07/19/2023] [Indexed: 10/17/2023] Open
Abstract
In complex situations involving communication, agents might attempt to mask their intentions, exploiting Shannon's theory of information as a theory of misinformation. Here, we introduce and analyze a simple multiagent reinforcement learning task where a buyer sends signals to a seller via its actions, and in which both agents are endowed with a recursive theory of mind. We show that this theory of mind, coupled with pure reward-maximization, gives rise to agents that selectively distort messages and become skeptical towards one another. Using information theory to analyze these interactions, we show how savvy buyers reduce mutual information between their preferences and actions, and how suspicious sellers learn to reinterpret or discard buyers' signals in a strategic manner.
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Affiliation(s)
- Nitay Alon
- Department of Computer Science, The Hebrew University of Jerusalem, Jerusalem, Israel
- Department of Computational Neuroscience, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Lion Schulz
- Department of Computational Neuroscience, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | | | - Peter Dayan
- Department of Computer Science, The Hebrew University of Jerusalem, Jerusalem, Israel
- Department of Computer Science, University of Tübingen, Tübingen, Germany
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5
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Waade PT, Enevoldsen KC, Vermillet AQ, Simonsen A, Fusaroli R. Introducing tomsup: Theory of mind simulations using Python. Behav Res Methods 2023; 55:2197-2231. [PMID: 35953661 DOI: 10.3758/s13428-022-01827-2] [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] [Accepted: 03/07/2022] [Indexed: 11/08/2022]
Abstract
Theory of mind (ToM) is considered crucial for understanding social-cognitive abilities and impairments. However, verbal theories of the mechanisms underlying ToM are often criticized as under-specified and mutually incompatible. This leads to measures of ToM being unreliable, to the extent that even canonical experimental tasks do not require representation of others' mental states. There have been attempts at making computational models of ToM, but these are not easily available for broad research application. In order to help meet these challenges, we here introduce the Python package tomsup: Theory of mind simulations using Python. The package provides a computational eco-system for investigating and comparing computational models of hypothesized ToM mechanisms and for using them as experimental stimuli. The package notably includes an easy-to-use implementation of the variational recursive Bayesian k-ToM model developed by (Devaine, Hollard, & Daunizeau, 2014b) and of simpler non-recursive decision models, for comparison. We provide a series of tutorials on how to: (i) simulate agents relying on the k-ToM model and on a range of simpler types of mechanisms; (ii) employ those agents to generate online experimental stimuli; (iii) analyze the data generated in such experimental setup, and (iv) specify new custom ToM and heuristic cognitive models.
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Affiliation(s)
- Peter T Waade
- School of Communication and Culture, Aarhus University, Aarhus, Denmark.
- The Interacting Minds Centre, Aarhus University, Aarhus, Denmark.
| | - Kenneth C Enevoldsen
- School of Communication and Culture, Aarhus University, Aarhus, Denmark.
- The Interacting Minds Centre, Aarhus University, Aarhus, Denmark.
- Center for Humanities Computing Aarhus, Aarhus University, Aarhus, Denmark.
| | | | - Arndis Simonsen
- The Interacting Minds Centre, Aarhus University, Aarhus, Denmark
- Psychosis Research Unit, Aarhus University Hospital Psychiatry, Aarhus, Denmark
| | - Riccardo Fusaroli
- School of Communication and Culture, Aarhus University, Aarhus, Denmark
- The Interacting Minds Centre, Aarhus University, Aarhus, Denmark
- Department of Linguistics, Cognitive Science and Semiotics, Aarhus University, Aarhus, Denmark
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6
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Kwon M, Lee SH, Ahn WY. Adaptive Design Optimization as a Promising Tool for Reliable and Efficient Computational Fingerprinting. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:798-804. [PMID: 36805245 DOI: 10.1016/j.bpsc.2022.12.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 11/21/2022] [Accepted: 12/11/2022] [Indexed: 12/23/2022]
Abstract
A key challenge in understanding mental (dys)functions is their etiological and functional heterogeneity, and several multidimensional assessments have been proposed for their comprehensive characterization. However, such assessments require lengthy testing, which may hinder reliable and efficient characterization of individual differences due to increased fatigue and distraction, especially in clinical populations. Computational modeling may address this challenge as it often provides more reliable measures of latent neurocognitive processes underlying observed behaviors and captures individual differences better than traditional assessments. However, even with a state-of-the-art hierarchical modeling approach, reliable estimation of model parameters still requires a large number of trials. Recent work suggests that Bayesian adaptive design optimization (ADO) is a promising way to address these challenges. With ADO, experimental design is optimized adaptively from trial to trial to extract the maximum amount of information about an individual's characteristics. In this review, we first describe the ADO methodology and then summarize recent work demonstrating that ADO increases the reliability and efficiency of latent neurocognitive measures. We conclude by discussing the challenges and future directions of ADO and proposing development of ADO-based computational fingerprints to reliably and efficiently characterize the heterogeneous profiles of psychiatric disorders.
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Affiliation(s)
- Mina Kwon
- Department of Psychology, Seoul National University, Seoul, Korea
| | - Sang Ho Lee
- Department of Psychology, Seoul National University, Seoul, Korea; Department of Brain and Cognitive Sciences, Seoul National University, Seoul, Korea
| | - Woo-Young Ahn
- Department of Psychology, Seoul National University, Seoul, Korea; Department of Brain and Cognitive Sciences, Seoul National University, Seoul, Korea.
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7
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Gupta P, Nguyen TN, Gonzalez C, Woolley AW. Fostering Collective Intelligence in Human-AI Collaboration: Laying the Groundwork for COHUMAIN. Top Cogn Sci 2023. [PMID: 37384870 DOI: 10.1111/tops.12679] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 06/12/2023] [Accepted: 06/12/2023] [Indexed: 07/01/2023]
Abstract
Artificial Intelligence (AI) powered machines are increasingly mediating our work and many of our managerial, economic, and cultural interactions. While technology enhances individual capability in many ways, how do we know that the sociotechnical system as a whole, consisting of a complex web of hundreds of human-machine interactions, is exhibiting collective intelligence? Research on human-machine interactions has been conducted within different disciplinary silos, resulting in social science models that underestimate technology and vice versa. Bringing together these different perspectives and methods at this juncture is critical. To truly advance our understanding of this important and quickly evolving area, we need vehicles to help research connect across disciplinary boundaries. This paper advocates for establishing an interdisciplinary research domain-Collective Human-Machine Intelligence (COHUMAIN). It outlines a research agenda for a holistic approach to designing and developing the dynamics of sociotechnical systems. In illustrating the kind of approach, we envision in this domain, we describe recent work on a sociocognitive architecture, the transactive systems model of collective intelligence, that articulates the critical processes underlying the emergence and maintenance of collective intelligence and extend it to human-AI systems. We connect this with synergistic work on a compatible cognitive architecture, instance-based learning theory and apply it to the design of AI agents that collaborate with humans. We present this work as a call to researchers working on related questions to not only engage with our proposal but also develop their own sociocognitive architectures and unlock the real potential of human-machine intelligence.
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Affiliation(s)
- Pranav Gupta
- Gies College of Business, University of Illinois, Urbana-Champaign
| | - Thuy Ngoc Nguyen
- Department of Social & Decision Sciences, Carnegie Mellon University
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8
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Gratch J. The promise and peril of interactive embodied agents for studying non-verbal communication: a machine learning perspective. Philos Trans R Soc Lond B Biol Sci 2023; 378:20210475. [PMID: 36871588 PMCID: PMC9985969 DOI: 10.1098/rstb.2021.0475] [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: 06/30/2022] [Accepted: 01/27/2023] [Indexed: 03/07/2023] Open
Abstract
In face-to-face interactions, parties rapidly react and adapt to each other's words, movements and expressions. Any science of face-to-face interaction must develop approaches to hypothesize and rigorously test mechanisms that explain such interdependent behaviour. Yet conventional experimental designs often sacrifice interactivity to establish experimental control. Interactive virtual and robotic agents have been offered as a way to study true interactivity while enforcing a measure of experimental control by allowing participants to interact with realistic but carefully controlled partners. But as researchers increasingly turn to machine learning to add realism to such agents, they may unintentionally distort the very interactivity they seek to illuminate, particularly when investigating the role of non-verbal signals such as emotion or active-listening behaviours. Here I discuss some of the methodological challenges that may arise when machine learning is used to model the behaviour of interaction partners. By articulating and explicitly considering these commitments, researchers can transform 'unintentional distortions' into valuable methodological tools that yield new insights and better contextualize existing experimental findings that rely on learning technology. This article is part of a discussion meeting issue 'Face2face: advancing the science of social interaction'.
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Affiliation(s)
- Jonathan Gratch
- Department of Computer Science, University of Southern California, Los Angeles, CA 90292, USA
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9
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Barnby JM, Dayan P, Bell V. Formalising social representation to explain psychiatric symptoms. Trends Cogn Sci 2023; 27:317-332. [PMID: 36609016 DOI: 10.1016/j.tics.2022.12.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 12/09/2022] [Accepted: 12/13/2022] [Indexed: 01/06/2023]
Abstract
Recent work in social cognition has moved beyond a focus on how people process social rewards to examine how healthy people represent other agents and how this is altered in psychiatric disorders. However, formal modelling of social representation has not kept pace with these changes, impeding our understanding of how core aspects of social cognition function, and fail, in psychopathology. Here, we suggest that belief-based computational models provide a basis for an integrated sociocognitive approach to psychiatry, with the potential to address important but unexamined pathologies of social representation, such as maladaptive schemas and illusory social agents.
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Affiliation(s)
- Joseph M Barnby
- Social Computation and Cognitive Representation Lab, Department of Psychology, Royal Holloway, University of London, Egham TW20 0EX, UK.
| | - Peter Dayan
- Max Planck Institute for Biological Cybernetics, Tübingen, 72076, Germany; University of Tübingen, Tübingen, 72074, Germany
| | - Vaughan Bell
- Clinical, Educational, and Health Psychology, University College London, London WC1E 7HB, UK; South London and Maudsley NHS Foundation Trust, London SE5 8AZ, UK
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10
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Strategic complexity and cognitive skills affect brain response in interactive decision-making. Sci Rep 2022; 12:15896. [PMID: 36151117 PMCID: PMC9508177 DOI: 10.1038/s41598-022-17951-0] [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: 02/15/2022] [Accepted: 08/03/2022] [Indexed: 11/30/2022] Open
Abstract
Deciding the best action in social settings requires decision-makers to consider their and others’ preferences, since the outcome depends on the actions of both. Numerous empirical investigations have demonstrated variability of behavior across individuals in strategic situations. While prosocial, moral, and emotional factors have been intensively investigated to explain this diversity, neuro-cognitive determinants of strategic decision-making and their relation with intelligence remain mostly unknown. This study presents a new model of the process of strategic decision-making in repeated interactions, first providing a precise measure of the environment’s complexity, and then analyzing how this complexity affects subjects’ performance and neural response. The results confirm the theoretical predictions of the model. The frequency of deviations from optimal behavior is explained by a combination of higher complexity of the strategic environment and cognitive skills of the individuals. Brain response correlates with strategic complexity, but only in the subgroups with higher cognitive skills. Furthermore, neural effects were only observed in a fronto-parietal network typically involved in single-agent tasks (the Multiple Demand Network), thus suggesting that neural processes dealing with cognitively demanding individual tasks also have a central role in interactive decision-making. Our findings contribute to understanding how cognitive factors shape strategic decision-making and may provide the neural pathway of the reported association between strategic sophistication and fluid intelligence.
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11
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Hofmans L, van den Bos W. Social learning across adolescence: A Bayesian neurocognitive perspective. Dev Cogn Neurosci 2022; 58:101151. [PMID: 36183664 PMCID: PMC9526184 DOI: 10.1016/j.dcn.2022.101151] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 09/14/2022] [Accepted: 09/15/2022] [Indexed: 01/13/2023] Open
Abstract
Adolescence is a period of social re-orientation in which we are generally more prone to peer influence and the updating of our beliefs based on social information, also called social learning, than in any other stage of our life. However, how do we know when to use social information and whose information to use and how does this ability develop across adolescence? Here, we review the social learning literature from a behavioral, neural and computational viewpoint, focusing on the development of brain systems related to executive functioning, value-based decision-making and social cognition. We put forward a Bayesian reinforcement learning framework that incorporates social learning about value associated with particular behavior and uncertainty in our environment and experiences. We discuss how this framework can inform us about developmental changes in social learning, including how the assessment of uncertainty and the ability to adaptively discriminate between information from different social sources change across adolescence. By combining reward-based decision-making in the domains of both informational and normative influence, this framework explains both negative and positive social peer influence in adolescence.
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Affiliation(s)
- Lieke Hofmans
- Department of Developmental Psychology, University of Amsterdam, Amsterdam, the Netherlands,Correspondence to: Nieuwe Achtergracht 129, room G1.05, 1018WS Amsterdam, the Netherlands.
| | - Wouter van den Bos
- Department of Developmental Psychology, University of Amsterdam, Amsterdam, the Netherlands,Amsterdam Brain and Cognition Center, University of Amsterdam, Amsterdam, the Netherlands,Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany
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12
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Kim HE, Kwon JH, Kim JJ. Did It Change Your Mind? Neural Substrates of Purchase Intention Change and Product Information. Front Neurosci 2022; 16:871353. [PMID: 35615281 PMCID: PMC9125177 DOI: 10.3389/fnins.2022.871353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 04/06/2022] [Indexed: 12/01/2022] Open
Abstract
Price and customer ratings are perhaps the two most important pieces of information consumers rely on when shopping online. This study aimed to elucidate the neural mechanism by which the introduction of these two types of information influences the purchase intention of potential consumers for hedonic products. Participants performed a lip-care product shopping task during functional magnetic resonance imaging, in which they re-disclosed purchase intentions referring to the information of price or rating provided about the products that they had previously disclosed their purchase intentions without any information. Data from 38 young female participants were analyzed to identify the underlying neural regions associated with the intention change and product information. The bilateral frontopolar cortex, bilateral dorsal anterior cingulate cortex (dACC), and left insula activated higher for the unchanged than changed intention condition. The right dACC and bilateral insula also activated more toward the price than the rating condition, whereas the medial prefrontal cortex and bilateral temporoparietal junction responded in the opposite direction. These results seem to reflect the shift to exploratory decision-making strategies and increased salience in maintaining purchase intentions despite referring to provided information and to highlight the involvement of social cognition-related regions in reference to customer ratings rather than price.
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Affiliation(s)
- Hesun Erin Kim
- Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, South Korea
| | - Joon Hee Kwon
- Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, South Korea
| | - Jae-Jin Kim
- Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, South Korea
- Department of Psychiatry, Yonsei University College of Medicine, Seoul, South Korea
- *Correspondence: Jae-Jin Kim
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13
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Langley C, Cirstea BI, Cuzzolin F, Sahakian BJ. Theory of Mind and Preference Learning at the Interface of Cognitive Science, Neuroscience, and AI: A Review. Front Artif Intell 2022; 5:778852. [PMID: 35493614 PMCID: PMC9038841 DOI: 10.3389/frai.2022.778852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 03/10/2022] [Indexed: 11/13/2022] Open
Abstract
Theory of Mind (ToM)-the ability of the human mind to attribute mental states to others-is a key component of human cognition. In order to understand other people's mental states or viewpoint and to have successful interactions with others within social and occupational environments, this form of social cognition is essential. The same capability of inferring human mental states is a prerequisite for artificial intelligence (AI) to be integrated into society, for example in healthcare and the motoring industry. Autonomous cars will need to be able to infer the mental states of human drivers and pedestrians to predict their behavior. In the literature, there has been an increasing understanding of ToM, specifically with increasing cognitive science studies in children and in individuals with Autism Spectrum Disorder. Similarly, with neuroimaging studies there is now a better understanding of the neural mechanisms that underlie ToM. In addition, new AI algorithms for inferring human mental states have been proposed with more complex applications and better generalisability. In this review, we synthesize the existing understanding of ToM in cognitive and neurosciences and the AI computational models that have been proposed. We focus on preference learning as an area of particular interest and the most recent neurocognitive and computational ToM models. We also discuss the limitations of existing models and hint at potential approaches to allow ToM models to fully express the complexity of the human mind in all its aspects, including values and preferences.
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Affiliation(s)
- Christelle Langley
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Bogdan Ionut Cirstea
- School of Engineering, Computing and Mathematics, Oxford Brookes University, Oxford, United Kingdom
| | - Fabio Cuzzolin
- School of Engineering, Computing and Mathematics, Oxford Brookes University, Oxford, United Kingdom
| | - Barbara J. Sahakian
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
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14
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Steixner-Kumar S, Rusch T, Doshi P, Spezio M, Gläscher J. Humans depart from optimal computational models of interactive decision-making during competition under partial information. Sci Rep 2022; 12:289. [PMID: 34997138 PMCID: PMC8741801 DOI: 10.1038/s41598-021-04272-x] [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: 02/26/2021] [Accepted: 12/14/2021] [Indexed: 11/10/2022] Open
Abstract
Decision making under uncertainty in multiagent settings is of increasing interest in decision science. The degree to which human agents depart from computationally optimal solutions in socially interactive settings is generally unknown. Such understanding provides insight into how social contexts affect human interaction and the underlying contributions of Theory of Mind. In this paper, we adapt the well-known ‘Tiger Problem’ from artificial-agent research to human participants in solo and interactive settings. Compared to computationally optimal solutions, participants gathered less information before outcome-related decisions when competing than cooperating with others. These departures from optimality were not haphazard but showed evidence of improved performance through learning. Costly errors emerged under conditions of competition, yielding both lower rates of rewarding actions and accuracy in predicting others. Taken together, this work provides a novel approach and insights into studying human social interaction when shared information is partial.
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Affiliation(s)
- Saurabh Steixner-Kumar
- Institute of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
| | - Tessa Rusch
- Institute of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA
| | - Prashant Doshi
- Department of Computer Science, University of Georgia, Athens, GA, USA
| | - Michael Spezio
- Institute of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany. .,Psychology, Neuroscience, and Data Science, Scripps College, Claremont, CA, USA.
| | - Jan Gläscher
- Institute of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
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15
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Rusch T, Charpentier CJ. Domain specificity versus process specificity: The "social brain" during strategic interaction. Neuron 2021; 109:3236-3238. [PMID: 34672982 DOI: 10.1016/j.neuron.2021.09.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Are social brain responses domain specific, or do domain-general but socially prevalent cognitive processes drive activity in this network? In this issue of Neuron, Konovalov et al. (2021) address this by dissociating general sociality from reactivity, one defining feature of social interactions.
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Affiliation(s)
- Tessa Rusch
- Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA 91125, USA
| | - Caroline J Charpentier
- Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA 91125, USA.
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16
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Sakurai T. Social processes and social environment during development. Semin Cell Dev Biol 2021; 129:40-46. [PMID: 34649805 DOI: 10.1016/j.semcdb.2021.09.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 09/20/2021] [Accepted: 09/28/2021] [Indexed: 12/24/2022]
Abstract
Social behavior involves many processes including cognitive functions. Altered social behaviors associated with many psychiatric disorders might have alterations in the processes. Poor social environment affects development and maturation of cognitive functions that are important for social cognition, possibly introducing social stress as well as vulnerability to the stress into the developing brain. Adolescence and early adulthood have higher sensitivity to social stress, which may be linked to the onset of psychiatric disorders during this time period. Understanding social behavioral processes in detail will be crucial for elucidating mechanisms of emerging the social behavior phenotypes in psychiatric disorders and for devising therapeutic and preventive interventions to introduce the resilience for the onset of psychiatric disorders through modulation of social circuitries.
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Affiliation(s)
- Takeshi Sakurai
- Medical Innovation Center Kyoto University Graduate School of Medicine, 53 ShogoinKawahara-cho, Sakyo-ku, Kyoto 606-8507, Japan; Department of Pathology, Columbia University Vagelos College of Physicians and Surgeons, New York, USA.
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17
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Tusche A, Bas LM. Neurocomputational models of altruistic decision-making and social motives: Advances, pitfalls, and future directions. WILEY INTERDISCIPLINARY REVIEWS. COGNITIVE SCIENCE 2021; 12:e1571. [PMID: 34340256 PMCID: PMC9286344 DOI: 10.1002/wcs.1571] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Revised: 06/23/2021] [Accepted: 07/01/2021] [Indexed: 01/09/2023]
Abstract
This article discusses insights from computational models and social neuroscience into motivations, precursors, and mechanisms of altruistic decision-making and other-regard. We introduce theoretical and methodological tools for researchers who wish to adopt a multilevel, computational approach to study behaviors that promote others' welfare. Using examples from recent studies, we outline multiple mental and neural processes relevant to altruism. To this end, we integrate evidence from neuroimaging, psychology, economics, and formalized mathematical models. We introduce basic mechanisms-pertinent to a broad range of value-based decisions-and social emotions and cognitions commonly recruited when our decisions involve other people. Regarding the latter, we discuss how decomposing distinct facets of social processes can advance altruistic models and the development of novel, targeted interventions. We propose that an accelerated synthesis of computational approaches and social neuroscience represents a critical step towards a more comprehensive understanding of altruistic decision-making. We discuss the utility of this approach to study lifespan differences in social preference in late adulthood, a crucial future direction in aging global populations. Finally, we review potential pitfalls and recommendations for researchers interested in applying a computational approach to their research. This article is categorized under: Economics > Interactive Decision-Making Psychology > Emotion and Motivation Neuroscience > Cognition Economics > Individual Decision-Making.
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Affiliation(s)
- Anita Tusche
- Department of Psychology, Queen's University, Ontario, Kingston, Canada.,Department of Economics, Queen's University, Ontario, Kingston, Canada.,Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, California, USA
| | - Lisa M Bas
- Department of Psychology, Queen's University, Ontario, Kingston, Canada
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18
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Nguyen TN, Gonzalez C. Theory of Mind From Observation in Cognitive Models and Humans. Top Cogn Sci 2021; 14:665-686. [PMID: 34165919 DOI: 10.1111/tops.12553] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 05/31/2021] [Accepted: 06/01/2021] [Indexed: 12/01/2022]
Abstract
A major challenge for research in artificial intelligence is to develop systems that can infer the goals, beliefs, and intentions of others (i.e., systems that have theory of mind, ToM). In this research, we propose a cognitive ToM framework that uses a well-known theory of decisions from experience to construct a computational representation of ToM. Instance-based learning theory (IBLT) is used to construct a cognitive model that generates ToM from the observation of other agents' behavior. The IBL model of the observer distinguishes itself from previous models of ToM that make unreasonable assumptions about human cognition, are hand-crafted for particular settings, complex, or unable to explain a cognitive development of ToM compared to human's ToM. The IBL model learns from the observation of goal-directed agents' behavior in a gridworld navigation task, and it infers and predicts the behaviors of the agents in new gridworlds across different degrees of decision complexity in similar ways to the way human observers do. We provide evidence for the alignment of the IBL observer's predictions under various levels of decision complexity. We also advance the demonstration of the IBL predictions using a classic test of false beliefs (the Sally-Anne test), which is commonly used to test ToM in humans. We discuss our results and the potential of the IBL observer model to improve human-machine interactions.
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Affiliation(s)
- Thuy Ngoc Nguyen
- Dynamic Decision Making Laboratory, Social and Decision Sciences Department, Carnegie Mellon University
| | - Cleotilde Gonzalez
- Dynamic Decision Making Laboratory, Social and Decision Sciences Department, Carnegie Mellon University
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19
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Aguirre-Loaiza H, Duarte-Pulgarín CA, Grajales LD, Gärtner M, García DY, Marín ÁG. Empatía y Teoría de la Mente: comparación entre deportistas y no deportistas. PENSAMIENTO PSICOLÓGICO 2020. [DOI: 10.11144/javerianacali.ppsi18-2.etmc] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022] Open
Abstract
Objetivo. Comparar la dimensión de cognición social de la Teoría de la Mente (ToM, por sus siglas en inglés) y la empatía entre deportistas y no deportistas. Método. Se ejecutó un diseño transversal con muestreo intencional, en el que 46 deportistas (Medad = 18.2, DE = 4.5) y 48 no deportistas (Medad = 20.2, DE = 3.5) completaron la Tarea de Empatía por el Dolor y el Test de las Miradas. Resultados. No se hallaron diferencias en la ToM, t(92) = 1.21, p = 0.228, d = 0.25. El Anova factorial mixto 3x2 indicó que el comportamiento de empatía es homogéneo por las condiciones (neutral, accidental e intencional) y grupos (deportistas vs no deportistas), F(2, 92) = 0.127, p = 0.881, ηp2 = 0.001. Sin embargo, la comparación de medias mostró diferencias favorables para deportistas en la condición de estímulos neutrales (p < 0.05). Conclusión. No hay variabilidad de la ToM, ni en las condiciones de accidentalidad e intencionalidad en el aspecto empático; mientras que en estímulos neutrales, el promedio difiere favorablemente para los deportistas.
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20
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Hebart MN, Schuck NW. Current topics in Computational Cognitive Neuroscience. Neuropsychologia 2020; 147:107621. [PMID: 32898518 DOI: 10.1016/j.neuropsychologia.2020.107621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Martin N Hebart
- Vision and Computational Cognition Group, Max Planck Institute for Human Cognitive and Brain Sciences, 04103, Leipzig, Germany.
| | - Nicolas W Schuck
- Max Planck Research Group NeuroCode, Max Planck Institute for Human Development, 14195, Berlin, Germany; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, 14195, Berlin, Germany.
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21
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Zhang L, Lengersdorff L, Mikus N, Gläscher J, Lamm C. Using reinforcement learning models in social neuroscience: frameworks, pitfalls and suggestions of best practices. Soc Cogn Affect Neurosci 2020; 15:695-707. [PMID: 32608484 PMCID: PMC7393303 DOI: 10.1093/scan/nsaa089] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 06/03/2020] [Accepted: 06/15/2020] [Indexed: 12/29/2022] Open
Abstract
The recent years have witnessed a dramatic increase in the use of reinforcement learning (RL) models in social, cognitive and affective neuroscience. This approach, in combination with neuroimaging techniques such as functional magnetic resonance imaging, enables quantitative investigations into latent mechanistic processes. However, increased use of relatively complex computational approaches has led to potential misconceptions and imprecise interpretations. Here, we present a comprehensive framework for the examination of (social) decision-making with the simple Rescorla-Wagner RL model. We discuss common pitfalls in its application and provide practical suggestions. First, with simulation, we unpack the functional role of the learning rate and pinpoint what could easily go wrong when interpreting differences in the learning rate. Then, we discuss the inevitable collinearity between outcome and prediction error in RL models and provide suggestions of how to justify whether the observed neural activation is related to the prediction error rather than outcome valence. Finally, we suggest posterior predictive check is a crucial step after model comparison, and we articulate employing hierarchical modeling for parameter estimation. We aim to provide simple and scalable explanations and practical guidelines for employing RL models to assist both beginners and advanced users in better implementing and interpreting their model-based analyses.
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Affiliation(s)
- Lei Zhang
- Neuropsychopharmacology and Biopsychology Unit, Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna 1010, Austria
- Social, Cognitive and Affective Neuroscience Unit, Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna 1010, Austria
| | - Lukas Lengersdorff
- Neuropsychopharmacology and Biopsychology Unit, Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna 1010, Austria
- Social, Cognitive and Affective Neuroscience Unit, Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna 1010, Austria
| | - Nace Mikus
- Neuropsychopharmacology and Biopsychology Unit, Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna 1010, Austria
| | - Jan Gläscher
- Institute of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg 20246, Germany
| | - Claus Lamm
- Neuropsychopharmacology and Biopsychology Unit, Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna 1010, Austria
- Social, Cognitive and Affective Neuroscience Unit, Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna 1010, Austria
- Vienna Cognitive Science Hub, University of Vienna, Vienna 1010, Austria
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