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Bonnaire J, Dumas G, Cassell J. Bringing together multimodal and multilevel approaches to study the emergence of social bonds between children and improve social AI. FRONTIERS IN NEUROERGONOMICS 2024; 5:1290256. [PMID: 38827377 PMCID: PMC11140154 DOI: 10.3389/fnrgo.2024.1290256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 04/29/2024] [Indexed: 06/04/2024]
Abstract
This protocol paper outlines an innovative multimodal and multilevel approach to studying the emergence and evolution of how children build social bonds with their peers, and its potential application to improving social artificial intelligence (AI). We detail a unique hyperscanning experimental framework utilizing functional near-infrared spectroscopy (fNIRS) to observe inter-brain synchrony in child dyads during collaborative tasks and social interactions. Our proposed longitudinal study spans middle childhood, aiming to capture the dynamic development of social connections and cognitive engagement in naturalistic settings. To do so we bring together four kinds of data: the multimodal conversational behaviors that dyads of children engage in, evidence of their state of interpersonal rapport, collaborative performance on educational tasks, and inter-brain synchrony. Preliminary pilot data provide foundational support for our approach, indicating promising directions for identifying neural patterns associated with productive social interactions. The planned research will explore the neural correlates of social bond formation, informing the creation of a virtual peer learning partner in the field of Social Neuroergonomics. This protocol promises significant contributions to understanding the neural basis of social connectivity in children, while also offering a blueprint for designing empathetic and effective social AI tools, particularly for educational contexts.
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Affiliation(s)
| | - Guillaume Dumas
- Research Center of the CHU Sainte-Justine, Department of Psychiatry, University of Montréal, Montreal, QC, Canada
- Mila–Quebec Artificial Intelligence Institute, Montreal, QC, Canada
| | - Justine Cassell
- Inria Paris Centre, Paris, France
- School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, United States
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McMahon E, Isik L. Seeing social interactions. Trends Cogn Sci 2023; 27:1165-1179. [PMID: 37805385 PMCID: PMC10841760 DOI: 10.1016/j.tics.2023.09.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 09/01/2023] [Accepted: 09/05/2023] [Indexed: 10/09/2023]
Abstract
Seeing the interactions between other people is a critical part of our everyday visual experience, but recognizing the social interactions of others is often considered outside the scope of vision and grouped with higher-level social cognition like theory of mind. Recent work, however, has revealed that recognition of social interactions is efficient and automatic, is well modeled by bottom-up computational algorithms, and occurs in visually-selective regions of the brain. We review recent evidence from these three methodologies (behavioral, computational, and neural) that converge to suggest the core of social interaction perception is visual. We propose a computational framework for how this process is carried out in the brain and offer directions for future interdisciplinary investigations of social perception.
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Affiliation(s)
- Emalie McMahon
- Department of Cognitive Science, Johns Hopkins University, Baltimore, MD, USA
| | - Leyla Isik
- Department of Cognitive Science, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
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Coucke N, Heinrich MK, Cleeremans A, Dorigo M. Learning from humans to build social cognition among robots. Front Robot AI 2023; 10:1030416. [PMID: 36814449 PMCID: PMC9939630 DOI: 10.3389/frobt.2023.1030416] [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: 08/28/2022] [Accepted: 01/23/2023] [Indexed: 02/09/2023] Open
Abstract
Self-organized groups of robots have generally coordinated their behaviors using quite simple social interactions. Although simple interactions are sufficient for some group behaviors, future research needs to investigate more elaborate forms of coordination, such as social cognition, to progress towards real deployments. In this perspective, we define social cognition among robots as the combination of social inference, social learning, social influence, and knowledge transfer, and propose that these abilities can be established in robots by building underlying mechanisms based on behaviors observed in humans. We review key social processes observed in humans that could inspire valuable capabilities in robots and propose that relevant insights from human social cognition can be obtained by studying human-controlled avatars in virtual environments that have the correct balance of embodiment and constraints. Such environments need to allow participants to engage in embodied social behaviors, for instance through situatedness and bodily involvement, but, at the same time, need to artificially constrain humans to the operational conditions of robots, for instance in terms of perception and communication. We illustrate our proposed experimental method with example setups in a multi-user virtual environment.
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Affiliation(s)
- Nicolas Coucke
- IRIDIA, Université Libre de Bruxelles, Brussels, Belgium,Consciousness, Cognition and Computation Group, Center for Research in Cognition and Neurosciences, Université Libre de Bruxelles, Brussels, Belgium,*Correspondence: Nicolas Coucke, ; Mary Katherine Heinrich,
| | - Mary Katherine Heinrich
- IRIDIA, Université Libre de Bruxelles, Brussels, Belgium,*Correspondence: Nicolas Coucke, ; Mary Katherine Heinrich,
| | - Axel Cleeremans
- Consciousness, Cognition and Computation Group, Center for Research in Cognition and Neurosciences, Université Libre de Bruxelles, Brussels, Belgium
| | - Marco Dorigo
- IRIDIA, Université Libre de Bruxelles, Brussels, Belgium
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Kastel N, Hesp C, Ridderinkhof KR, Friston KJ. Small steps for mankind: Modeling the emergence of cumulative culture from joint active inference communication. Front Neurorobot 2023; 16:944986. [PMID: 36699948 PMCID: PMC9868743 DOI: 10.3389/fnbot.2022.944986] [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: 05/16/2022] [Accepted: 11/30/2022] [Indexed: 01/11/2023] Open
Abstract
Although the increase in the use of dynamical modeling in the literature on cultural evolution makes current models more mathematically sophisticated, these models have yet to be tested or validated. This paper provides a testable deep active inference formulation of social behavior and accompanying simulations of cumulative culture in two steps: First, we cast cultural transmission as a bi-directional process of communication that induces a generalized synchrony (operationalized as a particular convergence) between the belief states of interlocutors. Second, we cast social or cultural exchange as a process of active inference by equipping agents with the choice of who to engage in communication with. This induces trade-offs between confirmation of current beliefs and exploration of the social environment. We find that cumulative culture emerges from belief updating (i.e., active inference and learning) in the form of a joint minimization of uncertainty. The emergent cultural equilibria are characterized by a segregation into groups, whose belief systems are actively sustained by selective, uncertainty minimizing, dyadic exchanges. The nature of these equilibria depends sensitively on the precision afforded by various probabilistic mappings in each individual's generative model of their encultured niche.
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Affiliation(s)
- Natalie Kastel
- Amsterdam Brain and Cognition Centre, University of Amsterdam, Amsterdam, Netherlands,Institute for Advanced Study, University of Amsterdam, Amsterdam, Netherlands,Precision Psychiatry and Social Physiology Laboratory, Department of Psychiatry, CHU Sainte-Justine Research Center, Université de Montréal, Montreal, QC, Canada,*Correspondence: Natalie Kastel
| | - Casper Hesp
- Amsterdam Brain and Cognition Centre, University of Amsterdam, Amsterdam, Netherlands,Institute for Advanced Study, University of Amsterdam, Amsterdam, Netherlands,Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom,Department of Developmental Psychology, University of Amsterdam, Amsterdam, Netherlands
| | - K. Richard Ridderinkhof
- Amsterdam Brain and Cognition Centre, University of Amsterdam, Amsterdam, Netherlands,Department of Developmental Psychology, University of Amsterdam, Amsterdam, Netherlands
| | - Karl J. Friston
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
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Multilevel development of cognitive abilities in an artificial neural network. Proc Natl Acad Sci U S A 2022; 119:e2201304119. [PMID: 36122214 PMCID: PMC9522351 DOI: 10.1073/pnas.2201304119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Several neuronal mechanisms have been proposed to account for the formation of cognitive abilities through postnatal interactions with the physical and sociocultural environment. Here, we introduce a three-level computational model of information processing and acquisition of cognitive abilities. We propose minimal architectural requirements to build these levels, and how the parameters affect their performance and relationships. The first sensorimotor level handles local nonconscious processing, here during a visual classification task. The second level or cognitive level globally integrates the information from multiple local processors via long-ranged connections and synthesizes it in a global, but still nonconscious, manner. The third and cognitively highest level handles the information globally and consciously. It is based on the global neuronal workspace (GNW) theory and is referred to as the conscious level. We use the trace and delay conditioning tasks to, respectively, challenge the second and third levels. Results first highlight the necessity of epigenesis through the selection and stabilization of synapses at both local and global scales to allow the network to solve the first two tasks. At the global scale, dopamine appears necessary to properly provide credit assignment despite the temporal delay between perception and reward. At the third level, the presence of interneurons becomes necessary to maintain a self-sustained representation within the GNW in the absence of sensory input. Finally, while balanced spontaneous intrinsic activity facilitates epigenesis at both local and global scales, the balanced excitatory/inhibitory ratio increases performance. We discuss the plausibility of the model in both neurodevelopmental and artificial intelligence terms.
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