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Schultner DT, Lindström BR, Cikara M, Amodio DM. Transmission of social bias through observational learning. SCIENCE ADVANCES 2024; 10:eadk2030. [PMID: 38941465 PMCID: PMC11212708 DOI: 10.1126/sciadv.adk2030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 05/22/2024] [Indexed: 06/30/2024]
Abstract
People often rely on social learning-learning by observing others' actions and outcomes-to form preferences in advance of their own direct experiences. Although typically adaptive, we investigated whether social learning may also contribute to the formation and spread of prejudice. In six experiments (n = 1550), we demonstrate that by merely observing interactions between a prejudiced actor and social group members, observers acquired the prejudices of the actor. Moreover, observers were unaware of the actors' bias, misattributing their acquired group preferences to the behavior of group members, despite identical behavior between groups. Computational modeling revealed that this effect was due to value shaping, whereby one's preferences are shaped by another's actions toward a target, in addition to the target's reward feedback. These findings identify social learning as a potent mechanism of prejudice formation that operates implicitly and supports the transmission of intergroup bias.
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Affiliation(s)
- David T. Schultner
- Faculty of Social and Behavioral Sciences, Department of Psychology, University of Amsterdam, Amsterdam, Netherlands
| | - Björn R. Lindström
- Division of Psychology, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Mina Cikara
- Graduate School of Arts and Sciences, Department of Psychology, Harvard University, Cambridge, MA, USA
| | - David M. Amodio
- Faculty of Social and Behavioral Sciences, Department of Psychology, University of Amsterdam, Amsterdam, Netherlands
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2
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Bavard S, Stuchlý E, Konovalov A, Gluth S. Humans can infer social preferences from decision speed alone. PLoS Biol 2024; 22:e3002686. [PMID: 38900903 PMCID: PMC11189591 DOI: 10.1371/journal.pbio.3002686] [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: 12/01/2023] [Accepted: 05/21/2024] [Indexed: 06/22/2024] Open
Abstract
Humans are known to be capable of inferring hidden preferences and beliefs of their conspecifics when observing their decisions. While observational learning based on choices has been explored extensively, the question of how response times (RT) impact our learning of others' social preferences has received little attention. Yet, while observing choices alone can inform us about the direction of preference, they reveal little about the strength of this preference. In contrast, RT provides a continuous measure of strength of preference with faster responses indicating stronger preferences and slower responses signaling hesitation or uncertainty. Here, we outline a preregistered orthogonal design to investigate the involvement of both choices and RT in learning and inferring other's social preferences. Participants observed other people's behavior in a social preferences task (Dictator Game), seeing either their choices, RT, both, or no information. By coupling behavioral analyses with computational modeling, we show that RT is predictive of social preferences and that observers were able to infer those preferences even when receiving only RT information. Based on these findings, we propose a novel observational reinforcement learning model that closely matches participants' inferences in all relevant conditions. In contrast to previous literature suggesting that, from a Bayesian perspective, people should be able to learn equally well from choices and RT, we show that observers' behavior substantially deviates from this prediction. Our study elucidates a hitherto unknown sophistication in human observational learning but also identifies important limitations to this ability.
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Affiliation(s)
- Sophie Bavard
- Department of Psychology, University of Hamburg, Hamburg, Germany
| | - Erik Stuchlý
- Department of Psychology, University of Hamburg, Hamburg, Germany
| | - Arkady Konovalov
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, United Kingdom
| | - Sebastian Gluth
- Department of Psychology, University of Hamburg, Hamburg, Germany
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3
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Charpentier CJ, Wu Q, Min S, Ding W, Cockburn J, O'Doherty JP. Heterogeneity in strategy use during arbitration between experiential and observational learning. Nat Commun 2024; 15:4436. [PMID: 38789415 PMCID: PMC11126711 DOI: 10.1038/s41467-024-48548-y] [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: 04/14/2023] [Accepted: 05/06/2024] [Indexed: 05/26/2024] Open
Abstract
To navigate our complex social world, it is crucial to deploy multiple learning strategies, such as learning from directly experiencing action outcomes or from observing other people's behavior. Despite the prevalence of experiential and observational learning in humans and other social animals, it remains unclear how people favor one strategy over the other depending on the environment, and how individuals vary in their strategy use. Here, we describe an arbitration mechanism in which the prediction errors associated with each learning strategy influence their weight over behavior. We designed an online behavioral task to test our computational model, and found that while a substantial proportion of participants relied on the proposed arbitration mechanism, there was some meaningful heterogeneity in how people solved this task. Four other groups were identified: those who used a fixed mixture between the two strategies, those who relied on a single strategy and non-learners with irrelevant strategies. Furthermore, groups were found to differ on key behavioral signatures, and on transdiagnostic symptom dimensions, in particular autism traits and anxiety. Together, these results demonstrate how large heterogeneous datasets and computational methods can be leveraged to better characterize individual differences.
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Affiliation(s)
- Caroline J Charpentier
- Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA.
- Department of Psychology & Brain and Behavior Institute, University of Maryland, College Park, MD, USA.
| | - Qianying Wu
- Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA
| | - Seokyoung Min
- Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA
| | - Weilun Ding
- Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA
| | - Jeffrey Cockburn
- Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA
| | - John P O'Doherty
- Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA
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Kim J, Bong SH, Yoon D, Jeong B. Prosocial emotions predict individual differences in economic decision-making during ultimatum game with dynamic reciprocal contexts. Sci Rep 2024; 14:11397. [PMID: 38762655 PMCID: PMC11102497 DOI: 10.1038/s41598-024-62203-y] [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: 02/26/2024] [Accepted: 05/14/2024] [Indexed: 05/20/2024] Open
Abstract
Social decision-making is known to be influenced by predictive emotions or the perceived reciprocity of partners. However, the connection between emotion, decision-making, and contextual reciprocity remains less understood. Moreover, arguments suggest that emotional experiences within a social context can be better conceptualised as prosocial rather than basic emotions, necessitating the inclusion of two social dimensions: focus, the degree of an emotion's relevance to oneself or others, and dominance, the degree to which one feels in control of an emotion. For better representation, these dimensions should be considered alongside the interoceptive dimensions of valence and arousal. In an ultimatum game involving fair, moderate, and unfair offers, this online study measured the emotions of 476 participants using a multidimensional affective rating scale. Using unsupervised classification algorithms, we identified individual differences in decisions and emotional experiences. Certain individuals exhibited consistent levels of acceptance behaviours and emotions, while reciprocal individuals' acceptance behaviours and emotions followed external reward value structures. Furthermore, individuals with distinct emotional responses to partners exhibited unique economic responses to their emotions, with only the reciprocal group exhibiting sensitivity to dominance prediction errors. The study illustrates a context-specific model capable of subtyping populations engaged in social interaction and exhibiting heterogeneous mental states.
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Affiliation(s)
- Jaewon Kim
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291, Daehak-Ro, Yuseong-Gu, Daejeon, 34141, South Korea
| | - Su Hyun Bong
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291, Daehak-Ro, Yuseong-Gu, Daejeon, 34141, South Korea
| | - Dayoung Yoon
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291, Daehak-Ro, Yuseong-Gu, Daejeon, 34141, South Korea
| | - Bumseok Jeong
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291, Daehak-Ro, Yuseong-Gu, Daejeon, 34141, South Korea.
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5
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Zheltyakova M, Korotkov A, Cherednichenko D, Didur M, Kireev M. To lie or to tell the truth? The influence of processing the opponent's feedback on the forthcoming choice. Front Psychol 2024; 15:1275884. [PMID: 38784609 PMCID: PMC11112074 DOI: 10.3389/fpsyg.2024.1275884] [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: 08/11/2023] [Accepted: 04/22/2024] [Indexed: 05/25/2024] Open
Abstract
Introduction The brain mechanisms of deceptive behavior are relatively well studied, and the key brain regions involved in its processing were established. At the same time, the brain mechanisms underlying the processes of preparation for deception are less known. Methods We studied BOLD-signal changes during the presentation of the opponent's feedback to a previous deceptive or honest action during the computer game. The goal of the game was to mislead the opponent either by means of deception or by means of telling the truth. Results As a result, it was shown that several brain regions that were previously demonstrated as involved in deception execution, such as the left anterior cingulate cortex and anterior insula, also underlie processes related to deception preparation. Discussion The results obtained also allowed us to suggest that brain regions responsible for performance monitoring, intention assessment, suppression of non-selected solutions, and reward processing could be involved in shaping future action selection and preparation for deception. By shedding light on the brain mechanisms underlying deception, our study contributes to a deeper understanding of this complex cognitive process. Furthermore, it emphasizes the significance of exploring brain mechanisms governing the choice between deception and truth at various stages of decision-making.
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Affiliation(s)
| | | | | | | | - Maxim Kireev
- N.P. Bechtereva Institute of the Human Brain, Russian Academy of Science, Saint Petersburg, Russia
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Weber R, Hopp FR, Eden A, Fisher JT, Lee HE. Vicarious punishment of moral violations in naturalistic drama narratives predicts cortical synchronization. Neuroimage 2024; 292:120613. [PMID: 38631616 DOI: 10.1016/j.neuroimage.2024.120613] [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: 09/26/2023] [Revised: 04/13/2024] [Accepted: 04/15/2024] [Indexed: 04/19/2024] Open
Abstract
Punishment of moral norm violators is instrumental for human cooperation. Yet, social and affective neuroscience research has primarily focused on second- and third-party norm enforcement, neglecting the neural architecture underlying observed (vicarious) punishment of moral wrongdoers. We used naturalistic television drama as a sampling space for observing outcomes of morally-relevant behaviors to assess how individuals cognitively process dynamically evolving moral actions and their consequences. Drawing on Affective Disposition Theory, we derived hypotheses linking character morality with viewers' neural processing of characters' rewards and punishments. We used functional magnetic resonance imaging (fMRI) to examine neural responses of 28 female participants while free-viewing 15 short story summary video clips of episodes from a popular US television soap opera. Each summary included a complete narrative structure, fully crossing main character behaviors (moral/immoral) and the consequences (reward/punishment) characters faced for their actions. Narrative engagement was examined via intersubject correlation and representational similarity analysis. Highest cortical synchronization in 9 specifically selected regions previously implicated in processing moral information was observed when characters who act immorally are punished for their actions with participants' empathy as an important moderator. The results advance our understanding of the moral brain and the role of normative considerations and character outcomes in viewers' engagement with popular narratives.
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Affiliation(s)
- Rene Weber
- University of California, Santa Barbara, Department of Communication - Media Neuroscience Lab; University of California, Santa Barbara, Department of Psychological and Brain Sciences; Ewha Womans University, Department of Communication and Media.
| | - Frederic R Hopp
- University of Amsterdam, Amsterdam School of Communication Research
| | - Allison Eden
- Michigan State University, Department of Communication
| | | | - Hye-Eun Lee
- Ewha Womans University, Department of Communication and Media
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Rodriguez Buritica JM, Eppinger B, Heekeren HR, Crone EA, van Duijvenvoorde ACK. Observational reinforcement learning in children and young adults. NPJ SCIENCE OF LEARNING 2024; 9:18. [PMID: 38480747 PMCID: PMC10937639 DOI: 10.1038/s41539-024-00227-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 02/21/2024] [Indexed: 03/17/2024]
Abstract
Observational learning is essential for the acquisition of new behavior in educational practices and daily life and serves as an important mechanism for human cognitive and social-emotional development. However, we know little about its underlying neurocomputational mechanisms from a developmental perspective. In this study we used model-based fMRI to investigate differences in observational learning and individual learning between children and younger adults. Prediction errors (PE), the difference between experienced and predicted outcomes, related positively to striatal and ventral medial prefrontal cortex activation during individual learning and showed no age-related differences. PE-related activation during observational learning was more pronounced when outcomes were worse than predicted. Particularly, negative PE-coding in the dorsal medial prefrontal cortex was stronger in adults compared to children and was associated with improved observational learning in children and adults. The current findings pave the way to better understand observational learning challenges across development and educational settings.
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Affiliation(s)
- Julia M Rodriguez Buritica
- Department of Psychology, University of Greifswald, Greifswald, Germany.
- Berlin School of Mind and Brain & Department of Psychology, Humboldt University of Berlin, Berlin, Germany.
- Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany.
| | - Ben Eppinger
- Department of Psychology, University of Greifswald, Greifswald, Germany
- Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany
- Department of Psychology, Concordia University, Montreal, Canada
- Department of Psychology, Technische Universität Dresden, Dresden, Germany
| | - Hauke R Heekeren
- Department of Psychology, University of Greifswald, Greifswald, Germany
- Executive University Board, Universität Hamburg, Hamburg, Germany
| | - Eveline A Crone
- Department of Psychology, Education and Child Studies, Erasmus University Rotterdam, Rotterdam, Netherlands
- Institute of Psychology, Leiden University, Leiden, The Netherlands
- Leiden Institute for Brain and Cognition, Leiden, The Netherlands
| | - Anna C K van Duijvenvoorde
- Institute of Psychology, Leiden University, Leiden, The Netherlands.
- Leiden Institute for Brain and Cognition, Leiden, The Netherlands.
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Wu Y, Merrick M, Gweon H. Expecting the Unexpected: Infants Use Others' Surprise to Revise Their Own Expectations. Open Mind (Camb) 2024; 8:67-83. [PMID: 38435704 PMCID: PMC10898783 DOI: 10.1162/opmi_a_00117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 12/19/2023] [Indexed: 03/05/2024] Open
Abstract
Human infants show systematic responses to events that violate their expectations. Can they also revise these expectations based on others' expressions of surprise? Here we ask whether infants (N = 156, mean = 15.2 months, range: 12.0-18.0 months) can use an experimenter's expression of surprise to revise their own expectations about statistically probable vs. improbable events. An experimenter sampled a ball from a box of red and white balls and briefly displayed either a surprised or an unsurprised expression at the outcome before revealing it to the infant. Following an unsurprised expression, the results were consistent with prior work; infants looked longer at a statistically improbable outcome than a probable outcome. Following a surprised expression, however, this standard pattern disappeared or was even reversed. These results suggest that even before infants can observe the unexpected events themselves, they can use others' surprise to expect the unexpected. Starting early in life, human learners can leverage social information that signals others' prediction error to update their own predictions.
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Affiliation(s)
- Yang Wu
- Department of Psychology, University of Toronto Scarborough, Toronto, ON, Canada
| | - Megan Merrick
- Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN, USA
| | - Hyowon Gweon
- Department of Psychology, Stanford University, Stanford, CA, USA
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Zhou Y, Han S, Kang P, Tobler PN, Hein G. The social transmission of empathy relies on observational reinforcement learning. Proc Natl Acad Sci U S A 2024; 121:e2313073121. [PMID: 38381794 PMCID: PMC10907261 DOI: 10.1073/pnas.2313073121] [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: 08/09/2023] [Accepted: 01/12/2024] [Indexed: 02/23/2024] Open
Abstract
Theories of moral development propose that empathy is transmitted across individuals. However, the mechanisms through which empathy is socially transmitted remain unclear. Here, we combine computational learning models and functional MRI to investigate whether, and if so, how empathic and non-empathic responses observed in others affect the empathy of female observers. The results of three independent studies showed that watching empathic or non-empathic responses generates a learning signal that respectively increases or decreases empathy ratings of the observer. A fourth study revealed that the learning-related transmission of empathy is stronger when observing human rather than computer demonstrators. Finally, we show that the social transmission of empathy alters empathy-related responses in the anterior insula, i.e., the same region that correlated with empathy baseline ratings, as well as its functional connectivity with the temporoparietal junction. Together, our findings provide a computational and neural mechanism for the social transmission of empathy that accounts for changes in individual empathic responses in empathic and non-empathic social environments.
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Affiliation(s)
- Yuqing Zhou
- Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
- Translational Social Neuroscience Unit, Department of Psychiatry, Psychosomatics, and Psychotherapy, University of Würzburg, Würzburg 97080, Germany
| | - Shihui Han
- School of Psychological and Cognitive Sciences, Beijing Key Laboratory of Behavior and Mental Health, PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Pyungwon Kang
- Department of Economics and Laboratory for Social and Neural Systems Research, University of Zurich and Neuroscience Center Zurich, University of Zurich and Swiss Federal Institute of Technology Zurich, Zurich CH-8006, Switzerland
| | - Philippe N. Tobler
- Department of Economics and Laboratory for Social and Neural Systems Research, University of Zurich and Neuroscience Center Zurich, University of Zurich and Swiss Federal Institute of Technology Zurich, Zurich CH-8006, Switzerland
| | - Grit Hein
- Translational Social Neuroscience Unit, Department of Psychiatry, Psychosomatics, and Psychotherapy, University of Würzburg, Würzburg 97080, Germany
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10
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Mayo O, Shamay-Tsoory S. Dynamic mutual predictions during social learning: A computational and interbrain model. Neurosci Biobehav Rev 2024; 157:105513. [PMID: 38135267 DOI: 10.1016/j.neubiorev.2023.105513] [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: 08/02/2023] [Revised: 10/27/2023] [Accepted: 12/18/2023] [Indexed: 12/24/2023]
Abstract
During social interactions, we constantly learn about the thoughts, feelings, and personality traits of our interaction partners. Learning in social interactions is critical for bond formation and acquiring knowledge. Importantly, this type of learning is typically bi-directional, as both partners learn about each other simultaneously. Here we review the literature on social learning and propose a new computational and neural model characterizing mutual predictions that take place within and between interactions. According to our model, each partner in the interaction attempts to minimize the prediction error of the self and the interaction partner. In most cases, these inferential models become similar over time, thus enabling mutual understanding to develop. At the neural level, this type of social learning may be supported by interbrain plasticity, defined as a change in interbrain coupling over time in neural networks associated with social learning, among them the mentalizing network, the observation-execution system, and the hippocampus. The mutual prediction model constitutes a promising means of providing empirically verifiable accounts of how relationships develop over time.
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Affiliation(s)
- Oded Mayo
- The Department of Psychology, University of Haifa, Haifa, Israel.
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Pereg M, Hertz U, Ben-Artzi I, Shahar N. Disentangling the contribution of individual and social learning processes in human advice-taking behavior. NPJ SCIENCE OF LEARNING 2024; 9:4. [PMID: 38245562 PMCID: PMC10799906 DOI: 10.1038/s41539-024-00214-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Accepted: 01/03/2024] [Indexed: 01/22/2024]
Abstract
The study of social learning examines how individuals learn from others by means of observation, imitation, or compliance with advice. However, it still remains largely unknown whether social learning processes have a distinct contribution to behavior, independent from non-social trial-and-error learning that often occurs simultaneously. 153 participants completed a reinforcement learning task, where they were asked to make choices to gain rewards. Advice from an artificial teacher was presented in 60% of the trials, allowing us to compare choice behavior with and without advice. Results showed a strong and reliable tendency to follow advice (test-retest reliability ~0.73). Computational modeling suggested a unique contribution of three distinct learning strategies: (a) individual learning (i.e., learning the value of actions, independent of advice), (b) informed advice-taking (i.e., learning the value of following advice), and (c) non-informed advice-taking (i.e., a constant bias to follow advice regardless of outcome history). Comparing artificial and empirical data provided specific behavioral regression signatures to both informed and non-informed advice taking processes. We discuss the theoretical implications of integrating internal and external information during the learning process.
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Affiliation(s)
- Maayan Pereg
- School of Psychological Sciences, Tel Aviv University, Tel Aviv, Israel.
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
- Minducate Center for the Science of Learning, Sagol School of Neuroscience, Tel Aviv, Israel.
- Department of Psychology, Achva Academic College, Arugot, Israel.
| | - Uri Hertz
- Department of Cognitive Sciences, University of Haifa, Haifa, Israel
- Institute of Information Processing and Decision Making, University of Haifa, Haifa, Israel
| | - Ido Ben-Artzi
- School of Psychological Sciences, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- Minducate Center for the Science of Learning, Sagol School of Neuroscience, Tel Aviv, Israel
| | - Nitzan Shahar
- School of Psychological Sciences, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
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12
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Zhang J, Wang Y, Leong C, Mao Y, Yuan Z. Bridging Stories and Science: An fNIRS-based hyperscanning investigation into child learning in STEM. Neuroimage 2024; 285:120486. [PMID: 38070436 DOI: 10.1016/j.neuroimage.2023.120486] [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: 08/21/2023] [Revised: 10/08/2023] [Accepted: 12/04/2023] [Indexed: 01/13/2024] Open
Abstract
Early STEM education is crucial for later learning. This novel study utilised fNIRS to examine how STEM teaching methods (i.e., traditional, storytelling, storyboarding) affect neural activity synchronisation between teachers and students. Our results showed that left and right inferior frontal gyrus (IFG) for storytelling teaching versus traditional teaching, superior temporal gyrus for storyboard teaching versus traditional teaching, and left angular gyrus for storyboard and storytelling teaching were significant different in brain synchronisation. In the storytelling teaching condition, left supramarginal gyrus brain synchrony was found to improve STEM learning outcomes. In the storyboard teaching condition, IFG brain synchrony correlated positively with STEM learning improvement. The findings confirmed that story-based teaching and storyboarding can improve STEM learning efficacy at the neural level and unscored the significant role of neural synchronization as a predictor of learning outcomes.
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Affiliation(s)
- Juan Zhang
- Faculty of Education, University of Macau, Macau SAR, China; Centre for Cognitive and Brain Sciences, University of Macau, Macau SAR, China.
| | - Yihui Wang
- Faculty of Education, University of Macau, Macau SAR, China; Centre for Cognitive and Brain Sciences, University of Macau, Macau SAR, China.
| | - Chantat Leong
- Faculty of Health Sciences, University of Macau, Macau SAR, China; Centre for Cognitive and Brain Sciences, University of Macau, Macau SAR, China
| | - Yidi Mao
- Faculty of Education, University of Macau, Macau SAR, China; Centre for Cognitive and Brain Sciences, University of Macau, Macau SAR, China
| | - Zhen Yuan
- Faculty of Health Sciences, University of Macau, Macau SAR, China; Centre for Cognitive and Brain Sciences, University of Macau, Macau SAR, China.
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13
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Germar M, Duderstadt VH, Mojzisch A. Social norms shape visual appearance: Taking a closer look at the link between social norm learning and perceptual decision-making. Cognition 2023; 241:105611. [PMID: 37678084 DOI: 10.1016/j.cognition.2023.105611] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 08/27/2023] [Accepted: 08/29/2023] [Indexed: 09/09/2023]
Abstract
One of the most fundamental questions in social psychology is whether norms can change individuals' minds by shaping the visual appearance of stimuli. This question was first raised by Muzafer Sherif (1935). Drawing on the extended social reinforcement account (Germar and Mojzisch, 2019), we aimed to provide a rigorous test of the hypothesis that norm learning leads to a persistent perceptual bias and, hence, to a change in the visual appearance of stimuli. From a methodological perspective, we used both a diffusion model approach and the method of adjustment, a well-established technique from psychophysics and vision research. The results of Experiments 1-3 show that norm effects on perceptual decision-making are robustly replicable, and are due to genuine social influence, that is, they cannot be explained by non-social priming, contingency learning effects (Experiments 1 and 2) or anchoring effects (Experiment 3). Most importantly, by using a psychophysical approach, Experiment 4 shows, for the first time, that social norm learning alters individuals' point of subjective equality and, hence, the visual appearance of stimuli.
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Affiliation(s)
- Markus Germar
- Institute of Psychology, University of Hildesheim, Universitätsplatz 1, 31141 Hildesheim, Germany.
| | - Vinzenz H Duderstadt
- Georg-Elias-Müller Institute for Psychology, University of Göttingen, 37073 Göttingen, Germany
| | - Andreas Mojzisch
- Institute of Psychology, University of Hildesheim, Universitätsplatz 1, 31141 Hildesheim, Germany
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14
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Tan SHJ, Wong JN, Teo WP. Is neuroimaging ready for the classroom? A systematic review of hyperscanning studies in learning. Neuroimage 2023; 281:120367. [PMID: 37689175 DOI: 10.1016/j.neuroimage.2023.120367] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 08/17/2023] [Accepted: 09/06/2023] [Indexed: 09/11/2023] Open
Abstract
Whether education research can be informed by findings from neuroscience studies has been hotly debated since Bruer's (1997) famous claim that neuroscience and education are "a bridge too far". However, this claim came before recent advancements in portable electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) technologies, and second-person neuroscience techniques that brought about significant headway in understanding instructor-learner interactions in the classroom. To explore whether neuroscience and education are still two very separate fields, we systematically review 15 hyperscanning studies that were conducted in real-world classrooms or that implemented a teaching-learning task to investigate instructor-learner dynamics. Findings from this investigation illustrate that inter-brain synchrony between instructor and learner is an additional and valuable dimension to understand the complex web of instructor- and learner-related variables that influence learning. Importantly, these findings demonstrate the possibility of conducting real-world classroom studies with portable neuroimaging techniques and highlight the potential of such studies in providing translatable real-world implications. Once thought of as incompatible, a successful coupling between neuroscience and education is now within sight.
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Affiliation(s)
- S H Jessica Tan
- Science of Learning in Education Centre, Office of Education Research, National Institute of Education, Nanyang Technological University, Singapore.
| | - Jin Nen Wong
- Science of Learning in Education Centre, Office of Education Research, National Institute of Education, Nanyang Technological University, Singapore
| | - Wei-Peng Teo
- Science of Learning in Education Centre, Office of Education Research, National Institute of Education, Nanyang Technological University, Singapore; Physical Education and Sport Science Academic Group, National Institute of Education, Nanyang Technological University, Singapore
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15
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Messimeris D, Levy R, Le Bouc R. Economic and social values in the brain: evidence from lesions to the human ventromedial prefrontal cortex. Front Neurol 2023; 14:1198262. [PMID: 37900604 PMCID: PMC10602746 DOI: 10.3389/fneur.2023.1198262] [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: 03/31/2023] [Accepted: 09/27/2023] [Indexed: 10/31/2023] Open
Abstract
Making good economic and social decisions is essential for individual and social welfare. Decades of research have provided compelling evidence that damage to the ventromedial prefrontal cortex (vmPFC) is associated with dramatic personality changes and impairments in economic and social decision-making. However, whether the vmPFC subserves a unified mechanism in the social and non-social domains remains unclear. When choosing between economic options, the vmPFC is thought to guide decision by encoding value signals that reflect the motivational relevance of the options on a common scale. A recent framework, the "extended common neural currency" hypothesis, suggests that the vmPFC may also assign values to social factors and principles, thereby guiding social decision-making. Although neural value signals have been observed in the vmPFC in both social and non-social studies, it is yet to be determined whether they have a causal influence on behavior or merely correlate with decision-making. In this review, we assess whether lesion studies of patients with vmPFC damage offer evidence for such a causal role of the vmPFC in shaping economic and social behavior.
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Affiliation(s)
- Despina Messimeris
- FrontLab, Paris Brain Institute (ICM), Sorbonne University, INSERM UMRS 1127, CNRS UMR 7225, Pitié-Salpêtrière Hospital, Paris, France
- Department of Neurology, Pitié-Salpêtrière Hospital, Sorbonne University, Assistance Publique-Hôpitaux de Paris (AP-HP), Paris, France
| | - Richard Levy
- FrontLab, Paris Brain Institute (ICM), Sorbonne University, INSERM UMRS 1127, CNRS UMR 7225, Pitié-Salpêtrière Hospital, Paris, France
- Department of Neurology, Pitié-Salpêtrière Hospital, Sorbonne University, Assistance Publique-Hôpitaux de Paris (AP-HP), Paris, France
| | - Raphaël Le Bouc
- Department of Neurology, Pitié-Salpêtrière Hospital, Sorbonne University, Assistance Publique-Hôpitaux de Paris (AP-HP), Paris, France
- Motivation, Brain and Behavior Laboratory (MBB), Paris Brain Institute (ICM), Sorbonne University, INSERM UMRS 1127, CNRS UMR 7225, Pitié-Salpêtrière Hospital, Paris, France
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16
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Pan Y, Vinding MC, Zhang L, Lundqvist D, Olsson A. A Brain-To-Brain Mechanism for Social Transmission of Threat Learning. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2304037. [PMID: 37544901 PMCID: PMC10558655 DOI: 10.1002/advs.202304037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Indexed: 08/08/2023]
Abstract
Survival and adaptation in environments require swift and efficacious learning about what is dangerous. Across species, much of such threat learning is acquired socially, e.g., through the observation of others' ("demonstrators'") defensive behaviors. However, the specific neural mechanisms responsible for the integration of information shared between demonstrators and observers remain largely unknown. This dearth of knowledge is addressed by performing magnetoencephalography (MEG) neuroimaging in demonstrator-observer dyads. A set of stimuli are first shown to a demonstrator whose defensive responses are filmed and later presented to an observer, while neuronal activity is recorded sequentially from both individuals who never interacted directly. These results show that brain-to-brain coupling (BtBC) in the fronto-limbic circuit (including insula, ventromedial, and dorsolateral prefrontal cortex) within demonstrator-observer dyads predict subsequent expressions of learning in the observer. Importantly, the predictive power of BtBC magnifies when a threat is imminent to the demonstrator. Furthermore, BtBC depends on how observers perceive their social status relative to the demonstrator, likely driven by shared attention and emotion, as bolstered by dyadic pupillary coupling. Taken together, this study describes a brain-to-brain mechanism for social threat learning, involving BtBC, which reflects social relationships and predicts adaptive, learned behaviors.
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Affiliation(s)
- Yafeng Pan
- Department of Psychology and Behavioral SciencesZhejiang UniversityHangzhou310058China
- Department of Clinical NeuroscienceKarolinska InstitutetStockholm17165Sweden
| | - Mikkel C. Vinding
- Department of Clinical NeuroscienceKarolinska InstitutetStockholm17165Sweden
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and ResearchCopenhagen University Hospital ‐ Amager and HvidovreCopenhagen2650Denmark
| | - Lei Zhang
- Centre for Human Brain HealthSchool of PsychologyUniversity of BirminghamBirminghamB15 2TTUK
- Institute for Mental HealthSchool of PsychologyUniversity of BirminghamBirminghamB15 2TTUK
- SocialCognitive and Affective Neuroscience UnitDepartment of CognitionEmotionand Methods in PsychologyFaculty of PsychologyUniversity of ViennaVienna1010Austria
| | - Daniel Lundqvist
- Department of Clinical NeuroscienceKarolinska InstitutetStockholm17165Sweden
| | - Andreas Olsson
- Department of Clinical NeuroscienceKarolinska InstitutetStockholm17165Sweden
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17
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Ni Y, Sun J, Li J. The shadowing effect of initial expectation on learning asymmetry. PLoS Comput Biol 2023; 19:e1010751. [PMID: 37486955 PMCID: PMC10399892 DOI: 10.1371/journal.pcbi.1010751] [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: 11/21/2022] [Revised: 08/03/2023] [Accepted: 07/04/2023] [Indexed: 07/26/2023] Open
Abstract
Evidence for positivity and optimism bias abounds in high-level belief updates. However, no consensus has been reached regarding whether learning asymmetries exist in more elementary forms of updates such as reinforcement learning (RL). In RL, the learning asymmetry concerns the sensitivity difference in incorporating positive and negative prediction errors (PE) into value estimation, namely the asymmetry of learning rates associated with positive and negative PEs. Although RL has been established as a canonical framework in characterizing interactions between agent and environment, the direction of learning asymmetry remains controversial. Here, we propose that part of the controversy stems from the fact that people may have different value expectations before entering the learning environment. Such a default value expectation influences how PEs are calculated and consequently biases subjects' choices. We test this hypothesis in two learning experiments with stable or varying reinforcement probabilities, across monetary gains, losses, and gain-loss mixed environments. Our results consistently support the model incorporating both asymmetric learning rates and the initial value expectation, highlighting the role of initial expectation in value updating and choice preference. Further simulation and model parameter recovery analyses confirm the unique contribution of initial value expectation in accessing learning rate asymmetry.
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Affiliation(s)
- Yinmei Ni
- School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China
- PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Jingwei Sun
- School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China
- Lenovo Research, Lenovo Group, Beijing, China
| | - Jian Li
- School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China
- PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
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18
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Navidi P, Saeedpour S, Ershadmanesh S, Hossein MM, Bahrami B. Prosocial learning: Model-based or model-free? PLoS One 2023; 18:e0287563. [PMID: 37352225 PMCID: PMC10289351 DOI: 10.1371/journal.pone.0287563] [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: 12/14/2022] [Accepted: 06/07/2023] [Indexed: 06/25/2023] Open
Abstract
Prosocial learning involves the acquisition of knowledge and skills necessary for making decisions that benefit others. We asked if, in the context of value-based decision-making, there is any difference between learning strategies for oneself vs. for others. We implemented a 2-step reinforcement learning paradigm in which participants learned, in separate blocks, to make decisions for themselves or for a present other confederate who evaluated their performance. We replicated the canonical features of the model-based and model-free reinforcement learning in our results. The behaviour of the majority of participants was best explained by a mixture of the model-based and model-free control, while most participants relied more heavily on MB control, and this strategy enhanced their learning success. Regarding our key self-other hypothesis, we did not find any significant difference between the behavioural performances nor in the model-based parameters of learning when comparing self and other conditions.
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Affiliation(s)
- Parisa Navidi
- Department of Cognitive Psychology, Institute for Cognitive Science Studies, Tehran, Iran
| | - Sepehr Saeedpour
- Department of Electrical and Computer Engineering, University of Tehran, Tehran, Iran
| | - Sara Ershadmanesh
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences, Tehran, Iran
- Department of Computational Neuroscience, MPI for Biological Cybernetics, Tuebingen, Germany
| | | | - Bahador Bahrami
- Crowd Cognition Group, Department of General Psychology and Education, Ludwig Maximilians University, Munich, Germany
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19
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Rosenblau G, Frolichs K, Korn CW. A neuro-computational social learning framework to facilitate transdiagnostic classification and treatment across psychiatric disorders. Neurosci Biobehav Rev 2023; 149:105181. [PMID: 37062494 PMCID: PMC10236440 DOI: 10.1016/j.neubiorev.2023.105181] [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: 08/31/2022] [Revised: 03/14/2023] [Accepted: 04/13/2023] [Indexed: 04/18/2023]
Abstract
Social deficits are among the core and most striking psychiatric symptoms, present in most psychiatric disorders. Here, we introduce a novel social learning framework, which consists of neuro-computational models that combine reinforcement learning with various types of social knowledge structures. We outline how this social learning framework can help specify and quantify social psychopathology across disorders and provide an overview of the brain regions that may be involved in this type of social learning. We highlight how this framework can specify commonalities and differences in the social psychopathology of individuals with autism spectrum disorder (ASD), personality disorders (PD), and major depressive disorder (MDD) and improve treatments on an individual basis. We conjecture that individuals with psychiatric disorders rely on rigid social knowledge representations when learning about others, albeit the nature of their rigidity and the behavioral consequences can greatly differ. While non-clinical cohorts tend to efficiently adapt social knowledge representations to relevant environmental constraints, psychiatric cohorts may rigidly stick to their preconceived notions or overly coarse knowledge representations during learning.
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Affiliation(s)
- Gabriela Rosenblau
- Department of Psychological and Brain Sciences, George Washington University, Washington DC, USA; Autism and Neurodevelopmental Disorders Institute, George Washington University, Washington DC, USA.
| | - Koen Frolichs
- Section Social Neuroscience, Department of General Psychiatry, University of Heidelberg, Heidelberg, Germany; Institute for Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Christoph W Korn
- Section Social Neuroscience, Department of General Psychiatry, University of Heidelberg, Heidelberg, Germany; Institute for Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
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20
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Vélez N, Chen AM, Burke T, Cushman FA, Gershman SJ. Teachers recruit mentalizing regions to represent learners' beliefs. Proc Natl Acad Sci U S A 2023; 120:e2215015120. [PMID: 37216526 PMCID: PMC10235937 DOI: 10.1073/pnas.2215015120] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Accepted: 04/20/2023] [Indexed: 05/24/2023] Open
Abstract
Teaching enables humans to impart vast stores of culturally specific knowledge and skills. However, little is known about the neural computations that guide teachers' decisions about what information to communicate. Participants (N = 28) played the role of teachers while being scanned using fMRI; their task was to select examples that would teach learners how to answer abstract multiple-choice questions. Participants' examples were best described by a model that selects evidence that maximizes the learner's belief in the correct answer. Consistent with this idea, participants' predictions about how well learners would do closely tracked the performance of an independent sample of learners (N = 140) who were tested on the examples they had provided. In addition, regions that play specialized roles in processing social information, namely the bilateral temporoparietal junction and middle and dorsal medial prefrontal cortex, tracked learners' posterior belief in the correct answer. Our results shed light on the computational and neural architectures that support our extraordinary abilities as teachers.
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Affiliation(s)
- Natalia Vélez
- Department of Psychology, Harvard University, Cambridge, MA20138
| | - Alicia M. Chen
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA02139
| | - Taylor Burke
- Department of Psychology, Harvard University, Cambridge, MA20138
| | - Fiery A. Cushman
- Department of Psychology, Harvard University, Cambridge, MA20138
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21
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Zhang Y, Bi K, Li J, Wang Y, Fang F. Dyadic visual perceptual learning on orientation discrimination. Curr Biol 2023:S0960-9822(23)00552-3. [PMID: 37224810 DOI: 10.1016/j.cub.2023.04.070] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 02/24/2023] [Accepted: 04/26/2023] [Indexed: 05/26/2023]
Abstract
The belief that learning can be modulated by social context is mainly supported by high-level value-based learning studies. However, whether social context can even modulate low-level learning such as visual perceptual learning (VPL) is still unknown. Unlike traditional VPL studies in which participants were trained singly, here, we developed a novel dyadic VPL paradigm in which paired participants were trained with the same orientation discrimination task and could monitor each other's performance. We found that the social context (i.e., dyadic training) led to a greater behavioral performance improvement and a faster learning rate compared with the single training. Interestingly, the facilitating effects could be modulated by the performance difference between paired participants. Functional magnetic resonance imaging (fMRI) results showed that, compared with the single training, social cognition areas including bilateral parietal cortex and dorsolateral prefrontal cortex displayed a different activity pattern and enhanced functional connectivities to early visual cortex (EVC) during the dyadic training. Furthermore, the dyadic training resulted in more refined orientation representation in primary visual cortex (V1), which was closely associated with the greater behavioral performance improvement. Taken together, we demonstrate that the social context, learning with a partner, can remarkably augment the plasticity of low-level visual information process by means of reshaping the neural activities in EVC and social cognition areas, as well as their functional interplays.
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Affiliation(s)
- Yifei Zhang
- School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China; Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing 100871, China; Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
| | - Keyan Bi
- School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China; Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing 100871, China; Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
| | - Jian Li
- School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Yizhou Wang
- Center on Frontiers of Computing Studies, School of Computer Science, Peking University, Beijing 100871, China
| | - Fang Fang
- School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China; Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing 100871, China; Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China.
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22
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van Schie C, Cook JL, Elzinga B, Ly V. A boost in self-esteem after positive social evaluation predicts social and non-social learning. ROYAL SOCIETY OPEN SCIENCE 2023; 10:230027. [PMID: 37234503 PMCID: PMC10206450 DOI: 10.1098/rsos.230027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 04/23/2023] [Indexed: 05/28/2023]
Abstract
Fluctuations in self-esteem resulting from social acceptance and rejection could guide social behaviour by putting us in a state that is more or less open to social experiences. However, it remains unclear whether social acceptance and rejection may shape learning from social information depending on individual differences in self-esteem changes. Here we used a social feedback paradigm to manipulate social acceptance and rejection in a between-subjects design. Subsequently, we administered a behavioural task that enables the assessment of how well individuals learn on the basis of own experiences versus social information. Participants receiving positive (N = 43) versus negative (N = 44) social evaluation demonstrated an increase in subjective self-esteem. Importantly, the effect of the social evaluation on social learning was moderated by self-esteem changes. Specifically, an increase in self-esteem, as induced by positive evaluation, was associated with increased learning from social, but decreased learning from individual information. A decrease in self-esteem in response to negative evaluation was associated with decreased learning from individual information. These data suggest that increases in self-esteem in response to positive evaluation can induce a shift in the inclination to use social versus non-social information and may open one up to constructive learning from others.
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Affiliation(s)
- Charlotte van Schie
- Department of Clinical Psychology, Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
- Illawarra Health and Medical Research Institute and the School of Psychology, University of Wollongong, Wollongong, Australia
| | | | - Bernet Elzinga
- Department of Clinical Psychology, Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Verena Ly
- Department of Clinical Psychology, Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
- Institute Office, Institute of Psychology, Leiden University, Leiden, The Netherlands
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23
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Fornari L, Ioumpa K, Nostro AD, Evans NJ, De Angelis L, Speer SPH, Paracampo R, Gallo S, Spezio M, Keysers C, Gazzola V. Neuro-computational mechanisms and individual biases in action-outcome learning under moral conflict. Nat Commun 2023; 14:1218. [PMID: 36878911 PMCID: PMC9988878 DOI: 10.1038/s41467-023-36807-3] [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: 11/25/2021] [Accepted: 02/17/2023] [Indexed: 03/08/2023] Open
Abstract
Learning to predict action outcomes in morally conflicting situations is essential for social decision-making but poorly understood. Here we tested which forms of Reinforcement Learning Theory capture how participants learn to choose between self-money and other-shocks, and how they adapt to changes in contingencies. We find choices were better described by a reinforcement learning model based on the current value of separately expected outcomes than by one based on the combined historical values of past outcomes. Participants track expected values of self-money and other-shocks separately, with the substantial individual difference in preference reflected in a valuation parameter balancing their relative weight. This valuation parameter also predicted choices in an independent costly helping task. The expectations of self-money and other-shocks were biased toward the favored outcome but fMRI revealed this bias to be reflected in the ventromedial prefrontal cortex while the pain-observation network represented pain prediction errors independently of individual preferences.
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Affiliation(s)
- Laura Fornari
- Netherlands Institute for Neuroscience, KNAW, Meibergdreef 47, 1105BA, Amsterdam, The Netherlands
| | - Kalliopi Ioumpa
- Netherlands Institute for Neuroscience, KNAW, Meibergdreef 47, 1105BA, Amsterdam, The Netherlands
| | - Alessandra D Nostro
- Netherlands Institute for Neuroscience, KNAW, Meibergdreef 47, 1105BA, Amsterdam, The Netherlands
| | - Nathan J Evans
- School of Psychology, University of Queensland, Brisbane, QLD, Australia
| | - Lorenzo De Angelis
- Netherlands Institute for Neuroscience, KNAW, Meibergdreef 47, 1105BA, Amsterdam, The Netherlands
| | - Sebastian P H Speer
- Netherlands Institute for Neuroscience, KNAW, Meibergdreef 47, 1105BA, Amsterdam, The Netherlands
| | - Riccardo Paracampo
- Netherlands Institute for Neuroscience, KNAW, Meibergdreef 47, 1105BA, Amsterdam, The Netherlands
| | - Selene Gallo
- Netherlands Institute for Neuroscience, KNAW, Meibergdreef 47, 1105BA, Amsterdam, The Netherlands
| | - Michael Spezio
- Psychology, Neuroscience, & Data Science, Scripps College, 1030 Columbia Ave, CA 91711, Claremont, CA, USA
| | - Christian Keysers
- Netherlands Institute for Neuroscience, KNAW, Meibergdreef 47, 1105BA, Amsterdam, The Netherlands.,Department of Psychology, University of Amsterdam, Nieuwe Achtergracht 129-B, 1018 WT, Amsterdam, The Netherlands
| | - Valeria Gazzola
- Netherlands Institute for Neuroscience, KNAW, Meibergdreef 47, 1105BA, Amsterdam, The Netherlands. .,Department of Psychology, University of Amsterdam, Nieuwe Achtergracht 129-B, 1018 WT, Amsterdam, The Netherlands.
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24
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Jiang Y, Mi Q, Zhu L. Neurocomputational mechanism of real-time distributed learning on social networks. Nat Neurosci 2023; 26:506-516. [PMID: 36797365 DOI: 10.1038/s41593-023-01258-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 01/17/2023] [Indexed: 02/18/2023]
Abstract
Social networks shape our decisions by constraining what information we learn and from whom. Yet, the mechanisms by which network structures affect individual learning and decision-making remain unclear. Here, by combining a real-time distributed learning task with functional magnetic resonance imaging, computational modeling and social network analysis, we studied how humans learn from observing others' decisions on seven-node networks with varying topological structures. We show that learning on social networks can be approximated by a well-established error-driven process for observational learning, supported by an action prediction error encoded in the lateral prefrontal cortex. Importantly, learning is flexibly weighted toward well-connected neighbors, according to activity in the dorsal anterior cingulate cortex, but only insofar as social observations contain secondhand, potentially intertwining, information. These data suggest a neurocomputational mechanism of network-based filtering on the sources of information, which may give rise to biased learning and the spread of misinformation in an interconnected society.
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Affiliation(s)
- Yaomin Jiang
- School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China.,IDG/McGovern Institute for Brain Research, Peking University, Beijing, China.,Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Qingtian Mi
- School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China.,IDG/McGovern Institute for Brain Research, Peking University, Beijing, China.,Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Lusha Zhu
- School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China. .,IDG/McGovern Institute for Brain Research, Peking University, Beijing, China. .,Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China.
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25
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Chen X, Liu J, Luo YJ, Feng C. Brain Systems Underlying Fundamental Motivations of Human Social Conformity. Neurosci Bull 2023; 39:328-342. [PMID: 36287291 PMCID: PMC9905476 DOI: 10.1007/s12264-022-00960-4] [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: 01/26/2022] [Accepted: 07/11/2022] [Indexed: 01/10/2023] Open
Abstract
From birth to adulthood, we often align our behaviors, attitudes, and opinions with a majority, a phenomenon known as social conformity. A seminal framework has proposed that conformity behaviors are mainly driven by three fundamental motives: a desire to gain more information to be accurate, to obtain social approval from others, and to maintain a favorable self-concept. Despite extensive interest in neuroimaging investigation of social conformity, the relationship between brain systems and these fundamental motivations has yet to be established. Here, we reviewed brain imaging findings of social conformity with a componential framework, aiming to reveal the neuropsychological substrates underlying different conformity motivations. First, information-seeking engages the evaluation of social information, information integration, and modification of task-related activity, corresponding to brain networks implicated in reward, cognitive control, and tasks at hand. Second, social acceptance involves the anticipation of social acceptance or rejection and mental state attribution, mediated by networks of reward, punishment, and mentalizing. Third, self-enhancement entails the excessive representation of positive self-related information and suppression of negative self-related information, ingroup favoritism and/or outgroup derogation, and elaborated mentalizing processes to the ingroup, supported by brain systems of reward, punishment, and mentalizing. Therefore, recent brain imaging studies have provided important insights into the fundamental motivations of social conformity in terms of component processes and brain mechanisms.
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Affiliation(s)
- Xinling Chen
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, Guangzhou, 510631, China
- School of Psychology, South China Normal University, Guangzhou, 510631, China
- Center for Studies of Psychological Application, South China Normal University, Guangzhou, 510631, China
- Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, 510631, China
| | - Jiaxi Liu
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, Guangzhou, 510631, China
- School of Psychology, South China Normal University, Guangzhou, 510631, China
- Center for Studies of Psychological Application, South China Normal University, Guangzhou, 510631, China
- Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, 510631, China
| | - Yue-Jia Luo
- Department of Applied Psychology, University of Health and Rehabilitation Sciences, Qingdao, 266113, China.
- The State Key Lab of Cognitive and Learning, Faculty of Psychology, Beijing Normal University, Beijing, 100875, China.
- The Research Center of Brain Science and Visual Cognition, Kunming University of Science and Technology, Kunming, 650506, China.
- College of Teacher Education, Qilu Normal University, Jinan, 250200, China.
| | - Chunliang Feng
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, Guangzhou, 510631, China.
- School of Psychology, South China Normal University, Guangzhou, 510631, China.
- Center for Studies of Psychological Application, South China Normal University, Guangzhou, 510631, China.
- Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, 510631, China.
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26
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Lalani B, Gray S, Mitra-Ganguli T. Systems Thinking in an era of climate change: Does cognitive neuroscience hold the key to improving environmental decision making? A perspective on Climate-Smart Agriculture. Front Integr Neurosci 2023; 17:1145744. [PMID: 37181865 PMCID: PMC10174047 DOI: 10.3389/fnint.2023.1145744] [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: 01/16/2023] [Accepted: 03/02/2023] [Indexed: 05/16/2023] Open
Abstract
Systems Thinking (ST) can be defined as a mental construct that recognises patterns and connections in a particular complex system to make the "best decision" possible. In the field of sustainable agriculture and climate change, higher degrees of ST are assumed to be associated with more successful adaptation strategies under changing conditions, and "better" environmental decision making in a number of environmental and cultural settings. Future climate change scenarios highlight the negative effects on agricultural productivity worldwide, particularly in low-income countries (LICs) situated in the Global South. Alongside this, current measures of ST are limited by their reliance on recall, and are prone to possible measurement errors. Using Climate-Smart Agriculture (CSA), as an example case study, in this article we explore: (i) ST from a social science perspective; (ii) cognitive neuroscience tools that could be used to explore ST abilities in the context of LICs; (iii) an exploration of the possible correlates of systems thinking: observational learning, prospective thinking/memory and the theory of planned behaviour and (iv) a proposed theory of change highlighting the integration of social science frameworks and a cognitive neuroscience perspective. We find, recent advancements in the field of cognitive neuroscience such as Near-Infrared Spectroscopy (NIRS) provide exciting potential to explore previously hidden forms of cognition, especially in a low-income country/field setting; improving our understanding of environmental decision-making and the ability to more accurately test more complex hypotheses where access to laboratory studies is severely limited. We highlight that ST may correlate with other key aspects involved in environmental decision-making and posit motivating farmers via specific brain networks would: (a) enhance understanding of CSA practices (e.g., via the frontoparietal network extending from the dorsolateral prefrontal cortex (DLPFC) to the parietal cortex (PC) a control hub involved in ST and observational learning) such as tailoring training towards developing improved ST abilities among farmers and involving observational learning more explicitly and (b) motivate farmers to use such practices [e.g., via the network between the DLPFC and nucleus accumbens (NAc)] which mediates reward processing and motivation by focussing on a reward/emotion to engage farmers. Finally, our proposed interdisciplinary theory of change can be used as a starting point to encourage discussion and guide future research in this space.
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Affiliation(s)
- Baqir Lalani
- Natural Resources Institute, University of Greenwich, Chatham Maritime, United Kingdom
- *Correspondence: Baqir Lalani
| | - Steven Gray
- Department of Community Sustainability, Michigan State University, East Lansing, MI, United States
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Zhao H, Zhang T, Cheng T, Chen C, Zhai Y, Liang X, Cheng N, Long Y, Li Y, Wang Z, Lu C. Neurocomputational mechanisms of young children's observational learning of delayed gratification. Cereb Cortex 2022; 33:6063-6076. [PMID: 36562999 DOI: 10.1093/cercor/bhac484] [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: 09/26/2022] [Revised: 11/13/2022] [Accepted: 11/17/2022] [Indexed: 12/24/2022] Open
Abstract
The ability to delay gratification is crucial for a successful and healthy life. An effective way for young children to learn this ability is to observe the action of adult models. However, the underlying neurocomputational mechanism remains unknown. Here, we tested the hypotheses that children employed either the simple imitation strategy or the goal-inference strategy when learning from adult models in a high-uncertainty context. Results of computational modeling indicated that children used the goal-inference strategy regardless of whether the adult model was their mother or a stranger. At the neural level, results showed that successful learning of delayed gratification was associated with enhanced interpersonal neural synchronization (INS) between children and the adult models in the dorsal lateral prefrontal cortex but was not associated with children's own single-brain activity. Moreover, the discounting of future reward's value obtained from computational modeling of the goal-inference strategy was positively correlated with the strength of INS. These findings from our exploratory study suggest that, even for 3-year-olds, the goal-inference strategy is used to learn delayed gratification from adult models, and the learning strategy is associated with neural interaction between the brains of children and adult models.
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Affiliation(s)
- Hui Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, P.R. China
| | - Tengfei Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, P.R. China
| | - Tong Cheng
- Research Center for Child Development, School of Psychology, Capital Normal University, Beijing 100048, P.R. China
| | - Chuansheng Chen
- Department of Psychological Science, University of California, Irvine, CA 92697, United States
| | - Yu Zhai
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, P.R. China
| | - Xi Liang
- Research Center for Child Development, School of Psychology, Capital Normal University, Beijing 100048, P.R. China
| | - Nanhua Cheng
- Research Center for Child Development, School of Psychology, Capital Normal University, Beijing 100048, P.R. China
| | - Yuhang Long
- Institute of Developmental Psychology, Faculty of Psychology, Beijing Normal University, Beijing 100875, China
| | - Ying Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, P.R. China
| | - Zhengyan Wang
- Research Center for Child Development, School of Psychology, Capital Normal University, Beijing 100048, P.R. China
| | - Chunming Lu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, P.R. China
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Liu J, Li W, Zou Q, Liu S, Wang M, Zheng J. Research on the influencing factors of the citizen adoption process of China's national integrated online government service platform. ASLIB J INFORM MANAG 2022. [DOI: 10.1108/ajim-04-2022-0201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Abstract
PurposeThe Chinese government hopes to achieve the goal of benefiting citizens by building a National Integrated Online Government Service Platform (NIOGSP). However, citizens' low adoption of the platform makes it difficult for the government to achieve its goal. Research on the influencing factors of citizen adoption of NIOGSP can help the government fully understand the concerns and needs of its citizens and take targeted measures to increase citizen adoption.Design/methodology/approachFirst, this research builds a model of the citizen adoption process, including attention, retention and motivation, based on an observational learning model. Next, research variables are determined based on social cognitive theory, literature review and real-world needs. Finally, based on the questionnaire survey and structural equation model, the influencing factors of each stage of the citizen adoption process model are studied and the relationship between the three stages of the model is verified.FindingsResults show that perceived usefulness (PU) and self-efficacy (SE) positively affect attention. SE positively affects retention, while perceived privacy (PP) negatively affects retention. PU, social influence, PP and anxiety positively affect motivation.Originality/valueThe conclusion of this study can provide reference for governments in various countries to establish and improve online one-stop government. In addition, this study verifies the citizen adoption process model and finds that there is no obvious causal relationship between attention and retention, but both have positive effects on motivation.
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Adaptive learning strategies in purely observational learning. CURRENT PSYCHOLOGY 2022. [DOI: 10.1007/s12144-022-03904-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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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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31
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Zhou Y, Lindström B, Soutschek A, Kang P, Tobler PN, Hein G. Learning from Ingroup Experiences Changes Intergroup Impressions. J Neurosci 2022; 42:6931-6945. [PMID: 35906067 PMCID: PMC9464015 DOI: 10.1523/jneurosci.0027-22.2022] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 06/29/2022] [Accepted: 07/11/2022] [Indexed: 11/21/2022] Open
Abstract
Humans form impressions toward individuals of their own social groups (ingroup members) and of different social groups (outgroup members). Outgroup-focused theories predict that intergroup impressions are mainly shaped by experiences with outgroup individuals, while ingroup-focused theories predict that ingroup experiences play a dominant role. Here we test predictions from these two psychological theories by estimating how intergroup impressions are dynamically shaped when people learn from both ingroup and outgroup experiences. While undergoing fMRI, male participants had identical experiences with different ingroup or outgroup members and rated their social closeness and impressions toward the ingroup and the outgroup. Behavioral results showed an initial ingroup bias in impression ratings which was significantly reduced over the course of learning, with larger effects in individuals with stronger ingroup identification. Computational learning models revealed that these changes in intergroup impressions were predicted by the weight given to ingroup prediction errors. Neurally, the individual weight for ingroup prediction errors was related to the coupling between the left inferior parietal lobule and the left anterior insula, which, in turn, predicted learning-related changes in intergroup impressions. Our findings provide computational and neural evidence for ingroup-focused theories, highlighting the importance of ingroup experiences in shaping social impressions in intergroup settings.SIGNIFICANCE STATEMENT Living in multicultural societies, humans interact with individuals of their own social groups (ingroup members) and of different social groups (outgroup members). However, little is known about how people learn from the mixture of ingroup and outgroup interactions, the most natural experiences in current societies. Here, participants had identical, intermixed experiences with different ingroup and outgroup individuals and rated their closeness and impressions toward the ingroup and the outgroup. Combining computational models and fMRI, we find that the weight given to ingroup experiences (ingroup prediction errors) is the main source of intergroup impression change, captured by changes in connectivity between the parietal lobe and insula. These findings highlight the importance of ingroup experiences in shaping intergroup impressions in complex social environments.
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Affiliation(s)
- Yuqing Zhou
- Translational Social Neuroscience Unit, Department of Psychiatry, Psychosomatics, and Psychotherapy, University of Würzburg, Würzburg 97080, Germany
| | - Björn Lindström
- Department of Experimental and Applied Psychology, Vrije Universiteit Amsterdam, Amsterdam, 1081 HV, The Netherlands
| | - Alexander Soutschek
- Department of Psychology, Ludwig Maximilian University, Munich 80802, Germany
| | - Pyungwon Kang
- Department of Economics and Laboratory for Social and Neural Systems Research, University of Zurich and Neuroscience Center Zurich, University of Zurich and Swiss Federal Institute of Technology Zurich, Zurich, CH-8006, Switzerland
| | - Philippe N Tobler
- Department of Economics and Laboratory for Social and Neural Systems Research, University of Zurich and Neuroscience Center Zurich, University of Zurich and Swiss Federal Institute of Technology Zurich, Zurich, CH-8006, Switzerland
| | - Grit Hein
- Translational Social Neuroscience Unit, Department of Psychiatry, Psychosomatics, and Psychotherapy, University of Würzburg, Würzburg 97080, Germany
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32
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Nöbel S, Monier M, Fargeot L, Lespagnol G, Danchin E, Isabel G. Female fruit flies copy the acceptance, but not the rejection, of a mate. Behav Ecol 2022. [DOI: 10.1093/beheco/arac071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Acceptance and avoidance can be socially transmitted, especially in the case of mate choice. When a Drosophila melanogaster female observes a conspecific female (called demonstrator female) choosing to mate with one of two males, the former female (called observer female) can memorize and copy the latter female’s choice. Traditionally in mate-copying experiments, demonstrations provide two types of information to observer females, namely, the acceptance (positive) of one male and the rejection of the other male (negative). To disentangle the respective roles of positive and negative information in Drosophila mate copying, we performed experiments in which demonstrations provided only one type of information at a time. We found that positive information alone is sufficient to trigger mate copying. Observer females preferred males of phenotype A after watching a female mating with a male of phenotype A in the absence of any other male. Contrastingly, negative information alone (provided by a demonstrator female actively rejecting a male of phenotype B) did not affect future observer females’ mate choice. These results suggest that the informative part of demonstrations in Drosophila mate-copying experiments lies mainly, if not exclusively, in the positive information provided by the copulation with a given male. We discuss the reasons for such a result and suggest that Drosophila females learn to prefer the successful males, implying that the underlying learning mechanisms may be shared with those of appetitive memory in non-social associative learning.
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Affiliation(s)
- Sabine Nöbel
- Université Toulouse 1 Capitole and Institute for Advanced Study in Toulouse (IAST) , Toulouse , France
- Laboratoire Évolution & Diversité Biologique (EDB), UMR5174, CNRS, IRD, Université Toulouse III Paul Sabatier , 118 route de Narbonne, F-31062 Toulouse Cedex 9 , France
| | - Magdalena Monier
- Laboratoire Évolution & Diversité Biologique (EDB), UMR5174, CNRS, IRD, Université Toulouse III Paul Sabatier , 118 route de Narbonne, F-31062 Toulouse Cedex 9 , France
| | - Laura Fargeot
- Centre de Recherches sur la Cognition Animale (CRCA) , Centre de Biologie Intégrative (CBI), CNRS UMR 5169, Toulouse , France
| | - Guillaume Lespagnol
- Laboratoire Évolution & Diversité Biologique (EDB), UMR5174, CNRS, IRD, Université Toulouse III Paul Sabatier , 118 route de Narbonne, F-31062 Toulouse Cedex 9 , France
| | - Etienne Danchin
- Laboratoire Évolution & Diversité Biologique (EDB), UMR5174, CNRS, IRD, Université Toulouse III Paul Sabatier , 118 route de Narbonne, F-31062 Toulouse Cedex 9 , France
| | - Guillaume Isabel
- Centre de Recherches sur la Cognition Animale (CRCA) , Centre de Biologie Intégrative (CBI), CNRS UMR 5169, Toulouse , France
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Jiang Y, Wu H, Mi Q, Zhu L. Neurocomputations of strategic behavior: From iterated to novel interactions. WIRES COGNITIVE SCIENCE 2022; 13:e1598. [PMID: 35441465 PMCID: PMC9542218 DOI: 10.1002/wcs.1598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 03/27/2022] [Accepted: 03/29/2022] [Indexed: 11/15/2022]
Abstract
Strategic interactions, where an individual's payoff depends on the decisions of multiple intelligent agents, are ubiquitous among social animals. They span a variety of important social behaviors such as competition, cooperation, coordination, and communication, and often involve complex, intertwining cognitive operations ranging from basic reward processing to higher‐order mentalization. Here, we review the progress and challenges in probing the neural and cognitive mechanisms of strategic behavior of interacting individuals, drawing an analogy to recent developments in studies of reward‐seeking behavior, in particular, how research focuses in the field of strategic behavior have been expanded from adaptive behavior based on trial‐and‐error to flexible decisions based on limited prior experience. We highlight two important research questions in the field of strategic behavior: (i) How does the brain exploit past experience for learning to behave strategically? and (ii) How does the brain decide what to do in novel strategic situations in the absence of direct experience? For the former, we discuss the utility of learning models that have effectively connected various types of neural data with strategic learning behavior and helped elucidate the interplay among multiple learning processes. For the latter, we review the recent evidence and propose a neural generative mechanism by which the brain makes novel strategic choices through simulating others' goal‐directed actions according to rational or bounded‐rational principles obtained through indirect social knowledge. This article is categorized under:Economics > Interactive Decision‐Making Psychology > Reasoning and Decision Making Neuroscience > Cognition
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Affiliation(s)
- Yaomin Jiang
- School of Psychological and Cognitive Sciences, Beijing Key Laboratory of Behavior and Mental Health, IDG/McGovern Institute for Brain Research, Peking‐Tsinghua Center for Life Sciences Peking University Beijing China
| | - Hai‐Tao Wu
- School of Psychological and Cognitive Sciences, Beijing Key Laboratory of Behavior and Mental Health, IDG/McGovern Institute for Brain Research, Peking‐Tsinghua Center for Life Sciences Peking University Beijing China
| | - Qingtian Mi
- School of Psychological and Cognitive Sciences, Beijing Key Laboratory of Behavior and Mental Health, IDG/McGovern Institute for Brain Research, Peking‐Tsinghua Center for Life Sciences Peking University Beijing China
| | - Lusha Zhu
- School of Psychological and Cognitive Sciences, Beijing Key Laboratory of Behavior and Mental Health, IDG/McGovern Institute for Brain Research, Peking‐Tsinghua Center for Life Sciences Peking University Beijing China
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Pan Y, Dikker S, Zhu Y, Yang C, Hu Y, Goldstein P. Instructor-learner body coupling reflects instruction and learning. NPJ SCIENCE OF LEARNING 2022; 7:15. [PMID: 35764662 PMCID: PMC9240028 DOI: 10.1038/s41539-022-00131-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 05/24/2022] [Indexed: 06/01/2023]
Abstract
It is widely accepted that nonverbal communication is crucial for learning, but the exact functions of interpersonal coordination between instructors and learners remain unclear. Specifically, it is unknown what role instructional approaches play in the coupling of physical motion between instructors and learners, and crucially, how such instruction-mediated Body-to-Body Coupling (BtBC) might affect learning. We used a video-based, computer-vision Motion Energy Analysis (MEA) to quantify BtBC between learners and instructors who used two different instructional approaches to teach psychological concepts. BtBC was significantly greater when the instructor employed a scaffolding approach than when an explanation approach was used. The importance of the instructional approach was further underscored by the fact that an increase in motion in the instructor was associated with boosted BtBC, but only during scaffolding; no such relationship between the instructor movements and BtBC was found during explanation interactions. Finally, leveraging machine learning approaches (i.e., support vector and logistic regression models), we demonstrated that both learning outcome and instructional approaches could be decoded based on BtBC. Collectively, these results show that the real-time interaction of teaching and learning bodies is important for learning and that the instructional approach matters, with possible implications for both in-person and online learning.
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Affiliation(s)
- Yafeng Pan
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China
- Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Institute of Brain and Education Innovation, School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Suzanne Dikker
- NYU-Max Planck Center for Language, Music and Emotion, New York City, NY, USA
- Department of Clinical Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Yi Zhu
- Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Institute of Brain and Education Innovation, School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Cuirong Yang
- Department of Psychology, Suzhou University of Science and Technology, Suzhou, China
| | - Yi Hu
- Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Institute of Brain and Education Innovation, School of Psychology and Cognitive Science, East China Normal University, Shanghai, China.
- Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai, China.
| | - Pavel Goldstein
- Integrative Pain (iPain) Laboratory, School of Public Health, University of Haifa, Haifa, Israel.
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35
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Martinez-Saito M, Gorina E. Learning under social versus nonsocial uncertainty: A meta-analytic approach. Hum Brain Mapp 2022; 43:4185-4206. [PMID: 35620870 PMCID: PMC9374892 DOI: 10.1002/hbm.25948] [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] [Received: 10/12/2021] [Revised: 04/08/2022] [Accepted: 05/04/2022] [Indexed: 01/10/2023] Open
Abstract
Much of the uncertainty that clouds our understanding of the world springs from the covert values and intentions held by other people. Thus, it is plausible that specialized mechanisms that compute learning signals under uncertainty of exclusively social origin operate in the brain. To test this hypothesis, we scoured academic databases for neuroimaging studies involving learning under uncertainty, and performed a meta‐analysis of brain activation maps that compared learning in the face of social versus nonsocial uncertainty. Although most of the brain activations associated with learning error signals were shared between social and nonsocial conditions, we found some evidence for functional segregation of error signals of exclusively social origin during learning in limited regions of ventrolateral prefrontal cortex and insula. This suggests that most behavioral adaptations to navigate social environments are reused from frontal and subcortical areas processing generic value representation and learning, but that a specialized circuitry might have evolved in prefrontal regions to deal with social context representation and strategic action.
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Affiliation(s)
| | - Elena Gorina
- Department of Cognitive and Brain Sciences, Hebrew University of Jerusalem, Jerusalem, Israel
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36
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Patt VM, Palombo DJ, Esterman M, Verfaellie M. Hippocampal Contribution to Probabilistic Feedback Learning: Modeling Observation- and Reinforcement-based Processes. J Cogn Neurosci 2022; 34:1429-1446. [PMID: 35604353 DOI: 10.1162/jocn_a_01873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Simple probabilistic reinforcement learning is recognized as a striatum-based learning system, but in recent years, has also been associated with hippocampal involvement. This study examined whether such involvement may be attributed to observation-based learning (OL) processes, running in parallel to striatum-based reinforcement learning. A computational model of OL, mirroring classic models of reinforcement-based learning (RL), was constructed and applied to the neuroimaging data set of Palombo, Hayes, Reid, and Verfaellie (2019). Hippocampal contributions to value-based learning: Converging evidence from fMRI and amnesia. Cognitive, Affective & Behavioral Neuroscience, 19(3), 523-536. Results suggested that OL processes may indeed take place concomitantly to reinforcement learning and involve activation of the hippocampus and central orbitofrontal cortex. However, rather than independent mechanisms running in parallel, the brain correlates of the OL and RL prediction errors indicated collaboration between systems, with direct implication of the hippocampus in computations of the discrepancy between the expected and actual reinforcing values of actions. These findings are consistent with previous accounts of a role for the hippocampus in encoding the strength of observed stimulus-outcome associations, with updating of such associations through striatal reinforcement-based computations. In addition, enhanced negative RL prediction error signaling was found in the anterior insula with greater use of OL over RL processes. This result may suggest an additional mode of collaboration between the OL and RL systems, implicating the error monitoring network.
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Affiliation(s)
- Virginie M Patt
- VA Boston Healthcare System, MA.,Boston University School of Medicine, MA
| | | | - Michael Esterman
- VA Boston Healthcare System, MA.,Boston University School of Medicine, MA
| | - Mieke Verfaellie
- VA Boston Healthcare System, MA.,Boston University School of Medicine, MA
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Rybicki AJ, Sowden SL, Schuster B, Cook JL. Dopaminergic challenge dissociates learning from primary versus secondary sources of information. eLife 2022; 11:74893. [PMID: 35289748 PMCID: PMC9023054 DOI: 10.7554/elife.74893] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 03/14/2022] [Indexed: 11/13/2022] Open
Abstract
Some theories of human cultural evolution posit that humans have social-specific learning mechanisms that are adaptive specialisations moulded by natural selection to cope with the pressures of group living. However, the existence of neurochemical pathways that are specialised for learning from social information and individual experience is widely debated. Cognitive neuroscientific studies present mixed evidence for social-specific learning mechanisms: some studies find dissociable neural correlates for social and individual learning, whereas others find the same brain areas and, dopamine-mediated, computations involved in both. Here, we demonstrate that, like individual learning, social learning is modulated by the dopamine D2 receptor antagonist haloperidol when social information is the primary learning source, but not when it comprises a secondary, additional element. Two groups (total N = 43) completed a decision-making task which required primary learning, from own experience, and secondary learning from an additional source. For one group, the primary source was social, and secondary was individual; for the other group this was reversed. Haloperidol affected primary learning irrespective of social/individual nature, with no effect on learning from the secondary source. Thus, we illustrate that dopaminergic mechanisms underpinning learning can be dissociated along a primary-secondary but not a social-individual axis. These results resolve conflict in the literature and support an expanding field showing that, rather than being specialised for particular inputs, neurochemical pathways in the human brain can process both social and non-social cues and arbitrate between the two depending upon which cue is primarily relevant for the task at hand.
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38
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Shamay-Tsoory SG, Hertz U. Adaptive Empathy: A Model for Learning Empathic Responses in Response to Feedback. PERSPECTIVES ON PSYCHOLOGICAL SCIENCE 2022; 17:1008-1023. [PMID: 35050819 DOI: 10.1177/17456916211031926] [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] [Indexed: 01/10/2023]
Abstract
Empathy is usually deployed in social interactions. Nevertheless, common measures and examinations of empathy study this construct in isolation from the person in distress. In this article we seek to extend the field of examination to include both empathizer and target to determine whether and how empathic responses are affected by feedback and learned through interaction. Building on computational approaches in feedback-based adaptations (e.g., no feedback, model-free and model-based learning), we propose a framework for understanding how empathic responses are learned on the basis of feedback. In this framework, adaptive empathy, defined as the ability to adapt one's empathic responses, is a central aspect of empathic skills and can provide a new dimension to the evaluation and investigation of empathy. By extending existing neural models of empathy, we suggest that adaptive empathy may be mediated by interactions between the neural circuits associated with valuation, shared distress, observation-execution, and mentalizing. Finally, we propose that adaptive empathy should be considered a prominent facet of empathic capabilities with the potential to explain empathic behavior in health and psychopathology.
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Affiliation(s)
- Simone G Shamay-Tsoory
- Department of Psychology, University of Haifa.,Integrated Brain and Behavior Research Center (IBBRC), University of Haifa
| | - Uri Hertz
- Integrated Brain and Behavior Research Center (IBBRC), University of Haifa.,Department of Cognitive Sciences, University of Haifa
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39
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Lee H, Chung D. Characterization of the Core Determinants of Social Influence From a Computational and Cognitive Perspective. Front Psychiatry 2022; 13:846535. [PMID: 35509882 PMCID: PMC9059935 DOI: 10.3389/fpsyt.2022.846535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 03/22/2022] [Indexed: 01/10/2023] Open
Abstract
Most human decisions are made among social others, and in what social context the choices are made is known to influence individuals' decisions. Social influence has been noted as an important factor that may nudge individuals to take more risks (e.g., initiation of substance use), but ironically also help individuals to take safer actions (e.g., successful abstinence). Such bi-directional impacts of social influence hint at the complexity of social information processing. Here, we first review the recent computational approaches that shed light on neural and behavioral mechanisms underlying social influence following basic computations involved in decision-making: valuation, action selection, and learning. We next review the studies on social influence from various fields including neuroeconomics, developmental psychology, social psychology, and cognitive neuroscience, and highlight three dimensions of determinants-who are the recipients, how the social contexts are presented, and to what domains and processes of decisions the influence is applied-that modulate the extent to which individuals are influenced by others. Throughout the review, we also introduce the brain regions that were suggested as neural instantiations of social influence from a large body of functional neuroimaging studies. Finally, we outline the remaining questions to be addressed in the translational application of computational and cognitive theories of social influence to psychopathology and health.
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Affiliation(s)
- Hyeji Lee
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, South Korea.,Department of Psychology, The University of Edinburgh, Edinburgh, United Kingdom
| | - Dongil Chung
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, South Korea
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Bogdan PC, Moore M, Kuznietsov I, Frank JD, Federmeier KD, Dolcos S, Dolcos F. Direct feedback and social conformity promote behavioral change via mechanisms indexed by centroparietal positivity: Electrophysiological evidence from a role-swapping ultimatum game. Psychophysiology 2021; 59:e13985. [PMID: 34931318 DOI: 10.1111/psyp.13985] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 11/16/2021] [Accepted: 11/23/2021] [Indexed: 10/19/2022]
Abstract
Our behavior is shaped by multiple factors, including direct feedback (seeing the outcomes of our past actions) and social observation (in part, via a drive to conform to other peoples' behaviors). However, it remains unclear how these two processes are linked in the context of behavioral change. This is important to investigate, as behavioral change is associated with distinct neural correlates that reflect specific aspects of processing, such as information integration and rule updating. To clarify whether these processes characterize both direct learning and conformity, we elicited the two within the same task, using a role-swapping version of the Ultimatum Game-a fairness paradigm where subjects decide how to share a pot of money with other players-while electroencephalography (EEG) data were recorded. Behavioral results showed that subjects decided how to divide the pot based on both direct feedback (seeing whether their past proposals were accepted or rejected) and social observation (copying the splits that others just proposed). Converging EEG evidence revealed that increased centroparietal positivity (P2, P3b, and late positivity) indexed behavioral changes motivated by direct feedback and those motivated by drives to conform. However, exploratory analyses also suggest that these two motivating factors may also be dissociable, and that frontal midline theta oscillations may predict behavioral changes linked to direct feedback but not conformity. Overall, this study provides novel electrophysiological evidence regarding the different forms of behavioral change. These findings are also relevant for understanding the mechanisms of social information processing that underlie successful cooperation.
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Affiliation(s)
- Paul C Bogdan
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Department of Psychology, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA
| | - Matthew Moore
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Illia Kuznietsov
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Department of Human and Animal Physiology, Lesya Ukrainka Volyn National University, Lutsk, Ukraine
| | - Justin D Frank
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Kara D Federmeier
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Department of Psychology, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA.,Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Sanda Dolcos
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Department of Psychology, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA
| | - Florin Dolcos
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Department of Psychology, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA.,Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
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Rothwell J, Antal A, Burke D, Carlsen A, Georgiev D, Jahanshahi M, Sternad D, Valls-Solé J, Ziemann U. Central nervous system physiology. Clin Neurophysiol 2021; 132:3043-3083. [PMID: 34717225 PMCID: PMC8863401 DOI: 10.1016/j.clinph.2021.09.013] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 09/13/2021] [Accepted: 09/20/2021] [Indexed: 12/15/2022]
Abstract
This is the second chapter of the series on the use of clinical neurophysiology for the study of movement disorders. It focusses on methods that can be used to probe neural circuits in brain and spinal cord. These include use of spinal and supraspinal reflexes to probe the integrity of transmission in specific pathways; transcranial methods of brain stimulation such as transcranial magnetic stimulation and transcranial direct current stimulation, which activate or modulate (respectively) the activity of populations of central neurones; EEG methods, both in conjunction with brain stimulation or with behavioural measures that record the activity of populations of central neurones; and pure behavioural measures that allow us to build conceptual models of motor control. The methods are discussed mainly in relation to work on healthy individuals. Later chapters will focus specifically on changes caused by pathology.
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Affiliation(s)
- John Rothwell
- Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, London, UK,Corresponding author at: Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK, (J. Rothwell)
| | - Andrea Antal
- Department of Neurology, University Medical Center Göttingen, Germany
| | - David Burke
- Department of Neurology, Royal Prince Alfred Hospital, University of Sydney, Sydney 2050, Australia
| | - Antony Carlsen
- School of Human Kinetics, University of Ottawa, Ottawa, Canada
| | - Dejan Georgiev
- Department of Neurology, University Medical Centre Ljubljana, Slovenia
| | - Marjan Jahanshahi
- Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, London, UK
| | - Dagmar Sternad
- Departments of Biology, Electrical & Computer Engineering, and Physics, Northeastern University, Boston, MA 02115, USA
| | - Josep Valls-Solé
- Institut d’Investigació Biomèdica August Pi I Sunyer, Villarroel, 170, Barcelona, Spain
| | - Ulf Ziemann
- Department of Neurology and Stroke, and Hertie Institute for Clinical Brain Research, Eberhard Karls University, Tübingen, Germany
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FeldmanHall O, Nassar MR. The computational challenge of social learning. Trends Cogn Sci 2021; 25:1045-1057. [PMID: 34583876 PMCID: PMC8585698 DOI: 10.1016/j.tics.2021.09.002] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 08/31/2021] [Accepted: 09/01/2021] [Indexed: 10/20/2022]
Abstract
The complex reward structure of the social world and the uncertainty endemic to social contexts poses a challenge for modeling. For example, during social interactions, the actions of one person influence the internal states of another. These social dependencies make it difficult to formalize social learning problems in a mathematically tractable way. While it is tempting to dispense with these complexities, they are a defining feature of social life. Because the structure of social interactions challenges the simplifying assumptions often made in models, they make an ideal testbed for computational models of cognition. By adopting a framework that embeds existing social knowledge into the model, we can go beyond explaining behaviors in laboratory tasks to explaining those observed in the wild.
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Affiliation(s)
- Oriel FeldmanHall
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, RI 02912, USA; Carney Institute for Brain Sciences, Brown University, Providence, RI 02912, USA.
| | - Matthew R Nassar
- Carney Institute for Brain Sciences, Brown University, Providence, RI 02912, USA; Department of Neuroscience, Brown University, Providence, RI 02912, USA
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Timing of social feedback shapes observational learning in strategic interaction. Sci Rep 2021; 11:21972. [PMID: 34754038 PMCID: PMC8578421 DOI: 10.1038/s41598-021-01466-1] [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] [Received: 06/26/2021] [Accepted: 10/26/2021] [Indexed: 12/02/2022] Open
Abstract
Many types of social interaction require the ability to anticipate others' behavior, which is commonly referred to as strategic sophistication. In this context, observational learning can represent a decisive tool for behavioral adaptation. However, little is known on whether and when individuals learn from observation in interactive settings. In the current study, 321 participants played one-shot interactive games and, at a given time along the experiment, they could observe the choices of an overtly efficient player. This social feedback could be provided before or after the participant's choice in each game. Results reveal that players with a sufficient level of strategic skills increased their level of sophistication only when the social feedback was provided after their choices, whereas they relied on blind imitation when they received feedback before their decision. Conversely, less sophisticated players did not increase their level of sophistication, regardless of the type of social feedback. Our findings disclose the interplay between endogenous and exogenous factors modulating observational learning in strategic interaction.
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Hurault JC, Tedesco A, Brunel L. « I know what you mean »: Investigating the sense of agency in learning an abstract mathematical knowledge using a constructivist method in virtual reality. ANNEE PSYCHOLOGIQUE 2021. [DOI: 10.3917/anpsy1.214.0443] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
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Konovalov A, Hill C, Daunizeau J, Ruff CC. Dissecting functional contributions of the social brain to strategic behavior. Neuron 2021; 109:3323-3337.e5. [PMID: 34407389 DOI: 10.1016/j.neuron.2021.07.025] [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: 02/11/2021] [Revised: 06/21/2021] [Accepted: 07/27/2021] [Indexed: 10/20/2022]
Abstract
Social interactions routinely lead to neural activity in a "social brain network" comprising, among other regions, the temporoparietal junction (TPJ) and the dorsomedial prefrontal cortex (dmPFC). But what is the function of these areas? Are they specialized for behavior in social contexts or do they implement computations required for dealing with any reactive process, even non-living entities? Here, we use fMRI and a game paradigm separating the need for these two aspects of cognition. We find that most social-brain areas respond to both social and non-social reactivity rather than just to human opponents. However, the TPJ shows a dissociation from the dmPFC: its activity and connectivity primarily reflect context-dependent outcome processing and reactivity detection, while dmPFC engagement is linked to implementation of a behavioral strategy. Our results characterize an overarching computational property of the social brain but also suggest specialized roles for subregions of this network.
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Affiliation(s)
- Arkady Konovalov
- Zurich Center for Neuroeconomics (ZNE), Department of Economics, University of Zurich, Zurich 8006, Switzerland.
| | - Christopher Hill
- Zurich Center for Neuroeconomics (ZNE), Department of Economics, University of Zurich, Zurich 8006, Switzerland
| | - Jean Daunizeau
- Université Pierre et Marie Curie, Paris, France; Institut du Cerveau et de la Moelle épinière, Paris, France; INSERM UMR S975, Paris, France
| | - Christian C Ruff
- Zurich Center for Neuroeconomics (ZNE), Department of Economics, University of Zurich, Zurich 8006, Switzerland.
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Langdon A, Botvinick M, Nakahara H, Tanaka K, Matsumoto M, Kanai R. Meta-learning, social cognition and consciousness in brains and machines. Neural Netw 2021; 145:80-89. [PMID: 34735893 DOI: 10.1016/j.neunet.2021.10.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 09/20/2021] [Accepted: 10/01/2021] [Indexed: 12/11/2022]
Abstract
The intersection between neuroscience and artificial intelligence (AI) research has created synergistic effects in both fields. While neuroscientific discoveries have inspired the development of AI architectures, new ideas and algorithms from AI research have produced new ways to study brain mechanisms. A well-known example is the case of reinforcement learning (RL), which has stimulated neuroscience research on how animals learn to adjust their behavior to maximize reward. In this review article, we cover recent collaborative work between the two fields in the context of meta-learning and its extension to social cognition and consciousness. Meta-learning refers to the ability to learn how to learn, such as learning to adjust hyperparameters of existing learning algorithms and how to use existing models and knowledge to efficiently solve new tasks. This meta-learning capability is important for making existing AI systems more adaptive and flexible to efficiently solve new tasks. Since this is one of the areas where there is a gap between human performance and current AI systems, successful collaboration should produce new ideas and progress. Starting from the role of RL algorithms in driving neuroscience, we discuss recent developments in deep RL applied to modeling prefrontal cortex functions. Even from a broader perspective, we discuss the similarities and differences between social cognition and meta-learning, and finally conclude with speculations on the potential links between intelligence as endowed by model-based RL and consciousness. For future work we highlight data efficiency, autonomy and intrinsic motivation as key research areas for advancing both fields.
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Affiliation(s)
- Angela Langdon
- Princeton Neuroscience Institute, Princeton University, USA
| | - Matthew Botvinick
- DeepMind, London, UK; Gatsby Computational Neuroscience Unit, University College London, London, UK
| | | | - Keiji Tanaka
- RIKEN Center for Brain Science, Wako, Saitama, Japan
| | - Masayuki Matsumoto
- Division of Biomedical Science, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan; Graduate School of Comprehensive Human Sciences, University of Tsukuba, Ibaraki, Japan; Transborder Medical Research Center, University of Tsukuba, Ibaraki, Japan
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47
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Pan Y, Novembre G, Olsson A. The Interpersonal Neuroscience of Social Learning. PERSPECTIVES ON PSYCHOLOGICAL SCIENCE 2021; 17:680-695. [PMID: 34637374 DOI: 10.1177/17456916211008429] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The study of the brain mechanisms underpinning social behavior is currently undergoing a paradigm shift, moving its focus from single individuals to the real-time interaction among groups of individuals. Although this development opens unprecedented opportunities to study how interpersonal brain activity shapes behaviors through learning, there have been few direct connections to the rich field of learning science. Our article examines how the rapidly developing field of interpersonal neuroscience is (and could be) contributing to our understanding of social learning. To this end, we first review recent research extracting indices of brain-to-brain coupling (BtBC) in the context of social behaviors and, in particular, social learning. We then discuss how studying communicative behaviors during learning can aid the interpretation of BtBC and how studying BtBC can inform our understanding of such behaviors. We then discuss how BtBC and communicative behaviors collectively can predict learning outcomes, and we suggest several causative and mechanistic models. Finally, we highlight key methodological and interpretational challenges as well as exciting opportunities for integrating research in interpersonal neuroscience with social learning, and we propose a multiperson framework for understanding how interpersonal transmission of information between individual brains shapes social learning.
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Affiliation(s)
- Yafeng Pan
- Department of Clinical Neuroscience, Karolinska Institutet
| | - Giacomo Novembre
- Neuroscience of Perception and Action Lab, Italian Institute of Technology
| | - Andreas Olsson
- Department of Clinical Neuroscience, Karolinska Institutet
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Increased Ventromedial Prefrontal Cortex Activity in Adolescence Benefits Prosocial Reinforcement Learning. Dev Cogn Neurosci 2021; 52:101018. [PMID: 34678671 PMCID: PMC8529395 DOI: 10.1016/j.dcn.2021.101018] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 09/01/2021] [Accepted: 09/30/2021] [Indexed: 02/03/2023] Open
Abstract
Learning which of our behaviors benefit others contributes to forming social relationships. An important period for the development of (pro)social behavior is adolescence, which is characterized by transitions in social connections. It is, however, unknown how learning to benefit others develops across adolescence and what the underlying cognitive and neural mechanisms are. In this functional neuroimaging study, we assessed learning for self and others (i.e., prosocial learning) and the concurring neural tracking of prediction errors across adolescence (ages 9-21, N = 74). Participants performed a two-choice probabilistic reinforcement learning task in which outcomes resulted in monetary consequences for themselves, an unknown other, or no one. Participants from all ages were able to learn for themselves and others, but learning for others showed a more protracted developmental trajectory. Prediction errors for self were observed in the ventral striatum and showed no age-related differences. However, prediction error coding for others showed an age-related increase in the ventromedial prefrontal cortex. These results reveal insights into the computational mechanisms of learning for others across adolescence, and highlight that learning for self and others show different age-related patterns.
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Ossmy O, Han D, Kaplan BE, Xu M, Bianco C, Mukamel R, Adolph KE. Children do not distinguish efficient from inefficient actions during observation. Sci Rep 2021; 11:18106. [PMID: 34518566 PMCID: PMC8438080 DOI: 10.1038/s41598-021-97354-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 07/13/2021] [Indexed: 11/08/2022] Open
Abstract
Observation is a powerful way to learn efficient actions from others. However, the role of observers' motor skill in assessing efficiency of others is unknown. Preschoolers are notoriously poor at performing multi-step actions like grasping the handle of a tool. Preschoolers (N = 22) and adults (N = 22) watched video-recorded actors perform efficient and inefficient tool use. Eye tracking showed that preschoolers and adults looked equally long at the videos, but adults looked longer than children at how actors grasped the tool. Deep learning analyses of participants' eye gaze distinguished efficient from inefficient grasps for adults, but not for children. Moreover, only adults showed differential action-related pupil dilation and neural activity (suppressed oscillation power in the mu frequency) while observing efficient vs. inefficient grasps. Thus, children observe multi-step actions without "seeing" whether the initial step is efficient. Findings suggest that observer's own motor efficiency determines whether they can perceive action efficiency in others.
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Affiliation(s)
- Ori Ossmy
- Department of Psychology, Center for Neural Science, New York University, 6 Washington Place, Room 403, New York, NY, 10003, USA.
| | - Danyang Han
- Department of Psychology, Center for Neural Science, New York University, 6 Washington Place, Room 403, New York, NY, 10003, USA
| | - Brianna E Kaplan
- Department of Psychology, Center for Neural Science, New York University, 6 Washington Place, Room 403, New York, NY, 10003, USA
| | - Melody Xu
- Department of Psychology, Center for Neural Science, New York University, 6 Washington Place, Room 403, New York, NY, 10003, USA
| | - Catherine Bianco
- Department of Psychology, Center for Neural Science, New York University, 6 Washington Place, Room 403, New York, NY, 10003, USA
| | - Roy Mukamel
- School of Psychological Sciences, Sagol School of Neuroscience, Tel-Aviv University, Tel Aviv, Israel
| | - Karen E Adolph
- Department of Psychology, Center for Neural Science, New York University, 6 Washington Place, Room 403, New York, NY, 10003, USA
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Yamada M, Sakurai Y. Medial prefrontal cortex stimulation disrupts observational learning in Barnes maze in rats. Cogn Neurodyn 2021; 16:497-505. [PMID: 35401858 PMCID: PMC8934901 DOI: 10.1007/s11571-021-09715-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Revised: 08/19/2021] [Accepted: 09/02/2021] [Indexed: 10/20/2022] Open
Abstract
Observational learning, which improves one's own behavior by observing the adaptive behavior of others, has been experimentally demonstrated in primates and rodents in several behavioral studies, including our previous study. However, its neural mechanisms remain unclear. We electrically stimulated the brain regions of rats and disturbed their neural activities during observation periods in the observational learning task using Barnes maze. According to comparison of escaping latencies of the observer and model rats, the observer rats with stimulation of the medial prefrontal cortex (mPFC) showed no observational learning, whereas both of the observer rats with stimulation of the dorsal hippocampus and with no stimulation (control) showed observational learning. These results suggest that mPFC stimulation disrupts observational learning and confirms that the mPFC is an important brain region for it in rats.
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