1
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Wischnewski M, Hörberg MOY, Schutter DJLG. Electrophysiological correlates of (mis)judging social information. Psychophysiology 2024; 61:e14590. [PMID: 38632827 DOI: 10.1111/psyp.14590] [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: 05/27/2023] [Revised: 04/02/2024] [Accepted: 04/03/2024] [Indexed: 04/19/2024]
Abstract
Social information can be used to optimize decision-making. However, the simultaneous presentation of multiple sources of advice can lead to a distinction bias in judging the validity of the information. While the involvement of event-related potential (ERP) components in social information processing has been studied, how they are modulated by (mis)judging an advisor's information validity remains unknown. In two experiments participants performed a decision-making task with highly accurate or inaccurate cues. Each experiment consisted of an initial, learning, and test phase. During the learning phase, three advice cues were simultaneously presented and the validity of them had to be assessed. The effect of different cue constellations on ERPs was investigated. In the subsequent test phase, the willingness to follow or oppose an advice cue was tested. Results demonstrated the distinction bias with participants over or underestimating the accuracy of the most uncertain cues. The P2 amplitude was significantly increased during cue presentation when advisors were in disagreement as compared to when all were in agreement, regardless of cue validity. Further, a larger P3 amplitude during outcome presentation was found when advisors were in disagreement and increased with more informative cues. As such, the most uncertain cues were related to the smallest P3 amplitude. The findings hint at the possible role of P3 in judging and learning the predictability of social cues. This study provides novel insights into the role of P2 and P3 components during the judgment of social information validity.
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Affiliation(s)
- Miles Wischnewski
- Department of Experimental Psychology, University of Groningen, Groningen, the Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota, USA
| | - Michael O Y Hörberg
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Dennis J L G Schutter
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
- Department of Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, the Netherlands
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2
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Heerey EA, Clerke AS, Johnson NJ, Patenaude J. The subjective value of genuine smiles guides real-world social behaviour. PLoS One 2024; 19:e0304726. [PMID: 38861570 PMCID: PMC11166336 DOI: 10.1371/journal.pone.0304726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 05/16/2024] [Indexed: 06/13/2024] Open
Abstract
The mechanisms that underpin human social behaviour are poorly understood, in part because natural social behaviour is challenging to study. The task of linking the mechanisms thought to drive social behaviour to specific social behaviours in a manner that maintains ecological validity poses an even greater challenge. Here we report evidence that the subjective value people assign to genuine smiles, as measured in the laboratory, determines their responsiveness to genuine smiles encountered in a naturalistic social interaction. Specifically, participants (university undergraduates; age 17 to 36) who valued genuine smiles to a greater degree also showed stronger attention capture effects to neutral faces that were previously associated with genuine smiles and faster reciprocity of a social partner's smiles in a real social interaction. Additionally, the faster participants responded to the partner's genuine smiles the higher the partner's ratings of interaction quality were after the interaction. These data suggest that individual differences in subjective value of genuine smiles, measured in the lab, is one element that underpins responsiveness to natural genuine smiles and subsequent social outcomes.
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Affiliation(s)
- Erin A. Heerey
- Department of Psychology, The University of Western Ontario, London, ON, Canada
| | - Alexa S. Clerke
- Department of Psychology, The University of Western Ontario, London, ON, Canada
| | | | - Joshua Patenaude
- Department of Psychology, The University of Western Ontario, London, ON, Canada
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3
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Shivakumar AB, Mehak SF, Jijimon F, Gangadharan G. Extrahippocampal Contributions to Social Memory: The Role of Septal Nuclei. Biol Psychiatry 2024:S0006-3223(24)01287-3. [PMID: 38718881 DOI: 10.1016/j.biopsych.2024.04.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 03/22/2024] [Accepted: 04/22/2024] [Indexed: 06/16/2024]
Abstract
Social memory, the ability to recognize and remember individuals within a social group, is crucial for social interactions and relationships. Deficits in social memory have been linked to several neuropsychiatric and neurodegenerative disorders. The hippocampus, especially the circuit that links dorsal CA2 and ventral CA1 neurons, is considered a neural substrate for social memory formation. Recent studies have provided compelling evidence of extrahippocampal contributions to social memory. The septal nuclei, including the medial and lateral septum, make up a basal forebrain region that shares bidirectional neuronal connections with the hippocampus and has recently been identified as critical for social memory. The focus of our review is the neural circuit mechanisms that underlie social memory, with a special emphasis on the septum. We also discuss the social memory dysfunction associated with neuropsychiatric and neurodegenerative disorders.
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Affiliation(s)
- Apoorva Bettagere Shivakumar
- Department of Ageing Research, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Sonam Fathima Mehak
- Department of Ageing Research, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Feyba Jijimon
- Department of Ageing Research, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Gireesh Gangadharan
- Department of Ageing Research, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India.
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4
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Zárate-Rochín AM. Contemporary neurocognitive models of memory: A descriptive comparative analysis. Neuropsychologia 2024; 196:108846. [PMID: 38430963 DOI: 10.1016/j.neuropsychologia.2024.108846] [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: 11/03/2023] [Revised: 02/27/2024] [Accepted: 02/27/2024] [Indexed: 03/05/2024]
Abstract
The great complexity involved in the study of memory has given rise to numerous hypotheses and models associated with various phenomena at different levels of analysis. This has allowed us to delve deeper in our knowledge about memory but has also made it difficult to synthesize and integrate data from different lines of research. In this context, this work presents a descriptive comparative analysis of contemporary models that address the structure and function of multiple memory systems. The main goal is to outline a panoramic view of the key elements that constitute these models in order to visualize both the current state of research and possible future directions. The elements that stand out from different levels of analysis are distributed neural networks, hierarchical organization, predictive coding, homeostasis, and evolutionary perspective.
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Affiliation(s)
- Alba Marcela Zárate-Rochín
- Instituto de Investigaciones Cerebrales, Universidad Veracruzana, Dr. Castelazo Ayala s/n, Industrial Animas, 91190, Xalapa-Enríquez, Veracruz, Mexico.
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5
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Philippe R, Janet R, Khalvati K, Rao RPN, Lee D, Dreher JC. Neurocomputational mechanisms involved in adaptation to fluctuating intentions of others. Nat Commun 2024; 15:3189. [PMID: 38609372 PMCID: PMC11014977 DOI: 10.1038/s41467-024-47491-2] [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: 01/05/2022] [Accepted: 03/12/2024] [Indexed: 04/14/2024] Open
Abstract
Humans frequently interact with agents whose intentions can fluctuate between competition and cooperation over time. It is unclear how the brain adapts to fluctuating intentions of others when the nature of the interactions (to cooperate or compete) is not explicitly and truthfully signaled. Here, we use model-based fMRI and a task in which participants thought they were playing with another player. In fact, they played with an algorithm that alternated without signaling between cooperative and competitive strategies. We show that a neurocomputational mechanism with arbitration between competitive and cooperative experts outperforms other learning models in predicting choice behavior. At the brain level, the fMRI results show that the ventral striatum and ventromedial prefrontal cortex track the difference of reliability between these experts. When attributing competitive intentions, we find increased coupling between these regions and a network that distinguishes prediction errors related to competition and cooperation. These findings provide a neurocomputational account of how the brain arbitrates dynamically between cooperative and competitive intentions when making adaptive social decisions.
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Affiliation(s)
- Rémi Philippe
- CNRS-Institut des Sciences Cognitives Marc Jeannerod, UMR5229, Neuroeconomics, reward, and decision making laboratory, Lyon, France
- Université Claude Bernard Lyon 1, Lyon, France
| | - Rémi Janet
- CNRS-Institut des Sciences Cognitives Marc Jeannerod, UMR5229, Neuroeconomics, reward, and decision making laboratory, Lyon, France
- Université Claude Bernard Lyon 1, Lyon, France
| | - Koosha Khalvati
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - Rajesh P N Rao
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
- Center for Neurotechnology, University of Washington, Seattle, WA, USA
| | - Daeyeol Lee
- Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, MD, USA
- Kavli Discovery Neuroscience Institute, Johns Hopkins University, Baltimore, MD, USA
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University, Baltimore, MD, USA
| | - Jean-Claude Dreher
- CNRS-Institut des Sciences Cognitives Marc Jeannerod, UMR5229, Neuroeconomics, reward, and decision making laboratory, Lyon, France.
- Université Claude Bernard Lyon 1, Lyon, France.
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6
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Kopytin G, Ivanova M, Herrojo Ruiz M, Shestakova A. Evaluating the Influence of Musical and Monetary Rewards on Decision Making through Computational Modelling. Behav Sci (Basel) 2024; 14:124. [PMID: 38392477 PMCID: PMC10886002 DOI: 10.3390/bs14020124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 01/25/2024] [Accepted: 02/02/2024] [Indexed: 02/24/2024] Open
Abstract
A central question in behavioural neuroscience is how different rewards modulate learning. While the role of monetary rewards is well-studied in decision-making research, the influence of abstract rewards like music remains poorly understood. This study investigated the dissociable effects of these two reward types on decision making. Forty participants completed two decision-making tasks, each characterised by probabilistic associations between stimuli and rewards, with probabilities changing over time to reflect environmental volatility. In each task, choices were reinforced either by monetary outcomes (win/lose) or by the endings of musical melodies (consonant/dissonant). We applied the Hierarchical Gaussian Filter, a validated hierarchical Bayesian framework, to model learning under these two conditions. Bayesian statistics provided evidence for similar learning patterns across both reward types, suggesting individuals' similar adaptability. However, within the musical task, individual preferences for consonance over dissonance explained some aspects of learning. Specifically, correlation analyses indicated that participants more tolerant of dissonance behaved more stochastically in their belief-to-response mappings and were less likely to choose the response associated with the current prediction for a consonant ending, driven by higher volatility estimates. By contrast, participants averse to dissonance showed increased tonic volatility, leading to larger updates in reward tendency beliefs.
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Affiliation(s)
- Grigory Kopytin
- Institute for Cognitive Neuroscience, HSE University, 101000 Moscow, Russia
| | - Marina Ivanova
- Institute for Cognitive Neuroscience, HSE University, 101000 Moscow, Russia
| | - Maria Herrojo Ruiz
- Department of Psychology, Goldsmiths University of London, London SE14 6NW, UK
| | - Anna Shestakova
- Institute for Cognitive Neuroscience, HSE University, 101000 Moscow, Russia
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7
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Hauke DJ, Wobmann M, Andreou C, Mackintosh AJ, de Bock R, Karvelis P, Adams RA, Sterzer P, Borgwardt S, Roth V, Diaconescu AO. Altered Perception of Environmental Volatility During Social Learning in Emerging Psychosis. COMPUTATIONAL PSYCHIATRY (CAMBRIDGE, MASS.) 2024; 8:1-22. [PMID: 38774429 PMCID: PMC11104374 DOI: 10.5334/cpsy.95] [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: 02/02/2023] [Accepted: 12/15/2023] [Indexed: 05/24/2024]
Abstract
Paranoid delusions or unfounded beliefs that others intend to deliberately cause harm are a frequent and burdensome symptom in early psychosis, but their emergence and consolidation still remains opaque. Recent theories suggest that overly precise prediction errors lead to an unstable model of the world providing a breeding ground for delusions. Here, we employ a Bayesian approach to test for such an unstable model of the world and investigate the computational mechanisms underlying emerging paranoia. We modelled behaviour of 18 first-episode psychosis patients (FEP), 19 individuals at clinical high risk for psychosis (CHR-P), and 19 healthy controls (HC) during an advice-taking task designed to probe learning about others' changing intentions. We formulated competing hypotheses comparing the standard Hierarchical Gaussian Filter (HGF), a Bayesian belief updating scheme, with a mean-reverting HGF to model an altered perception of volatility. There was a significant group-by-volatility interaction on advice-taking suggesting that CHR-P and FEP displayed reduced adaptability to environmental volatility. Model comparison favored the standard HGF in HC, but the mean-reverting HGF in CHR-P and FEP in line with perceiving increased volatility, although model attributions in CHR-P were heterogeneous. We observed correlations between perceiving increased volatility and positive symptoms generally as well as with frequency of paranoid delusions specifically. Our results suggest that FEP are characterised by a different computational mechanism - perceiving the environment as increasingly volatile - in line with Bayesian accounts of psychosis. This approach may prove useful to investigate heterogeneity in CHR-P and identify vulnerability for transition to psychosis.
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Affiliation(s)
- Daniel J. Hauke
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Michelle Wobmann
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
| | - Christina Andreou
- Department of Psychiatry and Psychotherapy, Translational Psychiatry, University of Lübeck, Lübeck, Germany
| | | | - Renate de Bock
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
| | - Povilas Karvelis
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada
| | - Rick A. Adams
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
- Max Planck Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom
| | - Philipp Sterzer
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
| | - Stefan Borgwardt
- Department of Psychiatry and Psychotherapy, Translational Psychiatry, University of Lübeck, Lübeck, Germany
| | - Volker Roth
- Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland
| | - Andreea O. Diaconescu
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
- Department of Psychology, University of Toronto, Toronto, ON, Canada
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8
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Hodson R, Mehta M, Smith R. The empirical status of predictive coding and active inference. Neurosci Biobehav Rev 2024; 157:105473. [PMID: 38030100 DOI: 10.1016/j.neubiorev.2023.105473] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 10/27/2023] [Accepted: 11/16/2023] [Indexed: 12/01/2023]
Abstract
Research on predictive processing models has focused largely on two specific algorithmic theories: Predictive Coding for perception and Active Inference for decision-making. While these interconnected theories possess broad explanatory potential, they have only recently begun to receive direct empirical evaluation. Here, we review recent studies of Predictive Coding and Active Inference with a focus on evaluating the degree to which they are empirically supported. For Predictive Coding, we find that existing empirical evidence offers modest support. However, some positive results can also be explained by alternative feedforward (e.g., feature detection-based) models. For Active Inference, most empirical studies have focused on fitting these models to behavior as a means of identifying and explaining individual or group differences. While Active Inference models tend to explain behavioral data reasonably well, there has not been a focus on testing empirical validity of active inference theory per se, which would require formal comparison to other models (e.g., non-Bayesian or model-free reinforcement learning models). This review suggests that, while promising, a number of specific research directions are still necessary to evaluate the empirical adequacy and explanatory power of these algorithms.
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Affiliation(s)
| | | | - Ryan Smith
- Laureate Institute for Brain Research, USA.
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9
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Cushman F. Computational Social Psychology. Annu Rev Psychol 2024; 75:625-652. [PMID: 37540891 DOI: 10.1146/annurev-psych-021323-040420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/06/2023]
Abstract
Social psychologists attempt to explain how we interact by appealing to basic principles of how we think. To make good on this ambition, they are increasingly relying on an interconnected set of formal tools that model inference, attribution, value-guided decision making, and multi-agent interactions. By reviewing progress in each of these areas and highlighting the connections between them, we can better appreciate the structure of social thought and behavior, while also coming to understand when, why, and how formal tools can be useful for social psychologists.
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Affiliation(s)
- Fiery Cushman
- Department of Psychology, Harvard University, Cambridge, Massachusetts, USA;
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10
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Wilkinson CS, Luján MÁ, Hales C, Costa KM, Fiore VG, Knackstedt LA, Kober H. Listening to the Data: Computational Approaches to Addiction and Learning. J Neurosci 2023; 43:7547-7553. [PMID: 37940590 PMCID: PMC10634572 DOI: 10.1523/jneurosci.1415-23.2023] [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: 07/26/2023] [Revised: 08/21/2023] [Accepted: 08/22/2023] [Indexed: 11/10/2023] Open
Abstract
Computational approaches hold great promise for identifying novel treatment targets and creating translational therapeutics for substance use disorders. From circuitries underlying decision-making to computationally derived neural markers of drug-cue reactivity, this review is a summary of the approaches to data presented at our 2023 Society for Neuroscience Mini-Symposium. Here, we highlight data- and hypothesis-driven computational approaches that recently afforded advancements in addiction and learning neuroscience. First, we discuss the value of hypothesis-driven algorithmic modeling approaches, which integrate behavioral, neural, and cognitive outputs to refine hypothesis testing. Then, we review the advantages of data-driven dimensionality reduction and machine learning methods for uncovering novel predictor variables and elucidating relationships in high-dimensional data. Overall, this review highlights recent breakthroughs in cognitive mapping, model-based analysis of behavior/risky decision-making, patterns of drug taking, relapse, and neuromarker discovery, and showcases the benefits of novel modeling techniques, across both preclinical and clinical data.
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Affiliation(s)
| | - Miguel Á Luján
- Department of Neurobiology, University of Maryland, School of Medicine, Baltimore, Maryland 21201
| | - Claire Hales
- Department of Psychology, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
| | - Kauê M Costa
- National Institute on Drug Abuse Intramural Research Program, Baltimore, Maryland 21224
| | - Vincenzo G Fiore
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, New York 10029
| | - Lori A Knackstedt
- Department of Psychology, University of Florida, Gainesville, Florida 32611
| | - Hedy Kober
- Departments of Psychiatry, Psychology, and Neuroscience, Yale University, New Haven, Connecticut 06511
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11
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Bellucci G, Park SQ. Neurocomputational mechanisms of biased impression formation in lonely individuals. Commun Biol 2023; 6:1118. [PMID: 37923876 PMCID: PMC10624906 DOI: 10.1038/s42003-023-05429-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 10/06/2023] [Indexed: 11/06/2023] Open
Abstract
Social impressions are fundamental in our daily interactions with other people but forming accurate impressions of our social partners can be biased to different extents. Loneliness has previously been suggested to induce biases that hinder the formation of accurate impressions of others for successful social bonding. Here, we demonstrated that despite counterfactual evidence, negative first impressions bias information weighting, leading to less favorable trustworthiness beliefs. Lonely individuals did not only have more negative expectations of others' social behavior, but they also manifested a stronger weighting bias. Reduced orbitofrontal cortex (OFC) activity was associated with a stronger weighting bias in lonelier individuals and mediated the relationship between loneliness and this weighting bias. Importantly, stronger coupling between OFC and temporoparietal junction compensated for such effects, promoting more positive trustworthiness beliefs especially in lonelier individuals. These findings bear potential for future basic and clinical investigations on social cognition and the development of clinical symptoms linked to loneliness.
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Affiliation(s)
- Gabriele Bellucci
- Department of Psychology, Royal Holloway, University of London, Egham, TW20 0EX, UK.
- Department of Psychology I, University of Lübeck, Lübeck, Germany.
| | - Soyoung Q Park
- Department of Psychology I, University of Lübeck, Lübeck, Germany
- Department of Decision Neuroscience and Nutrition, German Institute of Human Nutrition (DIfE), Potsdam-Rehbruecke, Nuthetal, Germany
- Charité-Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Neuroscience Research Center, 10117, Berlin, Germany
- Deutsches Zentrum für Diabetes, 85764, Neuherberg, Germany
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12
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Qiao L, Zhang L, Chen A. Control dilemma: Evidence of the stability-flexibility trade-off. Int J Psychophysiol 2023; 191:29-41. [PMID: 37499985 DOI: 10.1016/j.ijpsycho.2023.07.002] [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: 03/06/2023] [Revised: 07/21/2023] [Accepted: 07/24/2023] [Indexed: 07/29/2023]
Abstract
Cognitive control can be applied flexibly when task goals or environments change (i.e., cognitive flexibility), or stably to pursue a goal in the face of distraction (i.e., cognitive stability). Whether these seemingly contradictory characteristics have an inverse relationship has been controversial, as some studies have suggested a trade-off mechanism between cognitive flexibility and cognitive stability, while others have not found such reciprocal associations. This study investigated the possible antagonistic correlation between cognitive flexibility and stability using a novel version of the flexibility-stability paradigm and the classic cued task switching paradigm. In Experiment 1, we showed that cognitive flexibility was inversely correlated with cognitive stability, as increased distractor proportions were associated with decreased cognitive flexibility and greater cognitive stability. Moreover, cognitive flexibility and stability were regulated by a single control system instead of two independent control mechanisms, as the model selection results indicated that the reciprocally regulated model with one integration parameter outperformed all other models, and the model parameter was inversely linked to cognitive flexibility and stability. We found similar results using the classic cued task switching paradigm in Experiment 2. Therefore, a trade-off between cognitive flexibility and stability was observed from the paradigms used in this study.
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Affiliation(s)
- Lei Qiao
- School of Psychology, Shenzhen University, Shenzhen, China
| | - Lijie Zhang
- School of Education Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China.
| | - Antao Chen
- Department of Psychology, Shanghai University of Sport, Shanghai, China
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13
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Charlton CE, Karvelis P, McIntyre RS, Diaconescu AO. Suicide prevention and ketamine: insights from computational modeling. Front Psychiatry 2023; 14:1214018. [PMID: 37457775 PMCID: PMC10342546 DOI: 10.3389/fpsyt.2023.1214018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 06/12/2023] [Indexed: 07/18/2023] Open
Abstract
Suicide is a pressing public health issue, with over 700,000 individuals dying each year. Ketamine has emerged as a promising treatment for suicidal thoughts and behaviors (STBs), yet the complex mechanisms underlying ketamine's anti-suicidal effect are not fully understood. Computational psychiatry provides a promising framework for exploring the dynamic interactions underlying suicidality and ketamine's therapeutic action, offering insight into potential biomarkers, treatment targets, and the underlying mechanisms of both. This paper provides an overview of current computational theories of suicidality and ketamine's mechanism of action, and discusses various computational modeling approaches that attempt to explain ketamine's anti-suicidal effect. More specifically, the therapeutic potential of ketamine is explored in the context of the mismatch negativity and the predictive coding framework, by considering neurocircuits involved in learning and decision-making, and investigating altered connectivity strengths and receptor densities targeted by ketamine. Theory-driven computational models offer a promising approach to integrate existing knowledge of suicidality and ketamine, and for the extraction of model-derived mechanistic parameters that can be used to identify patient subgroups and personalized treatment approaches. Future computational studies on ketamine's mechanism of action should optimize task design and modeling approaches to ensure parameter reliability, and external factors such as set and setting, as well as psychedelic-assisted therapy should be evaluated for their additional therapeutic value.
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Affiliation(s)
- Colleen E. Charlton
- Krembil Center for Neuroinformatics, Center for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Povilas Karvelis
- Krembil Center for Neuroinformatics, Center for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Roger S. McIntyre
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada
| | - Andreea O. Diaconescu
- Krembil Center for Neuroinformatics, Center for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
- Department of Psychology, University of Toronto, Toronto, ON, Canada
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14
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Buades-Rotger M, Smeijers D, Gallardo-Pujol D, Krämer UM, Brazil IA. Aggressive and psychopathic traits are linked to the acquisition of stable but imprecise hostile expectations. Transl Psychiatry 2023; 13:197. [PMID: 37296151 DOI: 10.1038/s41398-023-02497-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 05/12/2023] [Accepted: 05/30/2023] [Indexed: 06/12/2023] Open
Abstract
Individuals with hostile expectations (HEX) anticipate harm from seemingly neutral or ambiguous stimuli. However, it is unclear how HEX are acquired, and whether specific components of HEX learning can predict antisocial thought, conduct, and personality. In an online sample of healthy young individuals (n = 256, 69% women), we administered a virtual shooting task and applied computational modelling of behaviour to investigate HEX learning and its constellation of correlates. HEX acquisition was best explained by a hierarchical reinforcement learning mechanism. Crucially, we found that individuals with relatively higher self-reported aggressiveness and psychopathy developed stronger and less accurate hostile beliefs as well as larger prediction errors. Moreover, aggressive and psychopathic traits were associated with more temporally stable hostility representations. Our study thus shows that aggressiveness and psychopathy are linked with the acquisition of robust yet imprecise hostile beliefs through reinforcement learning.
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Affiliation(s)
- Macià Buades-Rotger
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands.
- Department of Neurology, University of Lübeck, Lübeck, Germany.
- Department of Clinical Psychology and Psychobiology, University of Barcelona, Barcelona, Spain.
| | - Danique Smeijers
- Division Diagnostics, Research, and Education, Forensic Psychiatric Center Pompestichting, Nijmegen, The Netherlands
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
| | - David Gallardo-Pujol
- Department of Clinical Psychology and Psychobiology, University of Barcelona, Barcelona, Spain
- Institute of Neurosciences, Barcelona, Spain
| | - Ulrike M Krämer
- Department of Neurology, University of Lübeck, Lübeck, Germany
- Department of Psychology, University of Lübeck, Lübeck, Germany
- Center of Brain, Behavior and Metabolism (CBBM), University of Lübeck, Lübeck, Germany
| | - Inti A Brazil
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands.
- Division Diagnostics, Research, and Education, Forensic Psychiatric Center Pompestichting, Nijmegen, The Netherlands.
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15
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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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16
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Sobczak A, Yousuf M, Bunzeck N. Anticipating social feedback involves basal forebrain and mesolimbic functional connectivity. Neuroimage 2023; 274:120131. [PMID: 37094625 DOI: 10.1016/j.neuroimage.2023.120131] [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: 02/23/2023] [Revised: 04/18/2023] [Accepted: 04/20/2023] [Indexed: 04/26/2023] Open
Abstract
The mesolimbic system and basal forebrain (BF) are implicated in processing rewards and punishment, but their interplay and functional properties of subregions with respect to future social outcomes remain unclear. Therefore, this study investigated regional responses and interregional functional connectivity of the lateral (l), medial (m), and ventral (v) Substantia Nigra (SN), Nucleus Accumbens (NAcc), Nucleus basalis of Meynert (NBM), and Medial Septum/Diagonal Band (MS/DB) during reward and punishment anticipation in a social incentive delay task with neutral, positive, and negative feedback using high-resolution fMRI (1.5mm3). Neuroimaging data (n=36 healthy humans) of the anticipation phase was analyzed using mass-univariate, functional connectivity, and multivariate-pattern analysis. As expected, participants responded faster when anticipating positive and negative compared to neutral social feedback. At the neural level, anticipating social information engaged valence-related and valence-unrelated functional connectivity patterns involving the BF and mesolimbic areas. Precisely, valence-related connectivity between the lSN and NBM was associated with anticipating neutral social feedback, while connectivity between the vSN and NBM was associated with anticipating positive social feedback. A more complex pattern was observed for anticipating negative social feedback, including connectivity between the lSN and MS/DB, lSN and NAcc, as well as mSN and NAcc. To conclude, behavioral responses are modulated by the possibility to obtain positive and avoid negative social feedback. The neural processing of feedback anticipation relies on functional connectivity patterns between the BF and mesolimbic areas associated with the emotional valence of the social information. As such, our findings give novel insights into the underlying neural processes of social information processing.
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Affiliation(s)
- Alexandra Sobczak
- Department of Psychology, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany.
| | - Mushfa Yousuf
- Department of Psychology, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | - Nico Bunzeck
- Department of Psychology, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany; Center of Brain, Behavior and Metabolism, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
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17
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Hein TP, Gong Z, Ivanova M, Fedele T, Nikulin V, Herrojo Ruiz M. Anterior cingulate and medial prefrontal cortex oscillations underlie learning alterations in trait anxiety in humans. Commun Biol 2023; 6:271. [PMID: 36922553 PMCID: PMC10017780 DOI: 10.1038/s42003-023-04628-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 02/27/2023] [Indexed: 03/18/2023] Open
Abstract
Anxiety has been linked to altered belief formation and uncertainty estimation, impacting learning. Identifying the neural processes underlying these changes is important for understanding brain pathology. Here, we show that oscillatory activity in the medial prefrontal, anterior cingulate and orbitofrontal cortex (mPFC, ACC, OFC) explains anxiety-related learning alterations. In a magnetoencephalography experiment, two groups of human participants pre-screened with high and low trait anxiety (HTA, LTA: 39) performed a probabilistic reward-based learning task. HTA undermined learning through an overestimation of volatility, leading to faster belief updating, more stochastic decisions and pronounced lose-shift tendencies. On a neural level, we observed increased gamma activity in the ACC, dmPFC, and OFC during encoding of precision-weighted prediction errors in HTA, accompanied by suppressed ACC alpha/beta activity. Our findings support the association between altered learning and belief updating in anxiety and changes in gamma and alpha/beta activity in the ACC, dmPFC, and OFC.
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Affiliation(s)
- Thomas P Hein
- Goldsmiths, University of London, Psychology Department, Whitehead Building New Cross, London, SE14 6NW, UK
| | - Zheng Gong
- Centre for Cognition and Decision making, Institute for Cognitive Neuroscience, HSE University, Moscow, Russian Federation
| | - Marina Ivanova
- Centre for Cognition and Decision making, Institute for Cognitive Neuroscience, HSE University, Moscow, Russian Federation
| | - Tommaso Fedele
- Centre for Cognition and Decision making, Institute for Cognitive Neuroscience, HSE University, Moscow, Russian Federation
| | - Vadim Nikulin
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Maria Herrojo Ruiz
- Goldsmiths, University of London, Psychology Department, Whitehead Building New Cross, London, SE14 6NW, UK.
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18
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Qiao L, Zhang L, Chen A. Brain connectivity modulation by Bayesian surprise in relation to control demand drives cognitive flexibility via control engagement. Cereb Cortex 2023; 33:1985-2000. [PMID: 35553644 DOI: 10.1093/cercor/bhac187] [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: 01/19/2022] [Revised: 04/20/2022] [Accepted: 04/27/2022] [Indexed: 11/13/2022] Open
Abstract
Human control is characterized by its flexibility and adaptability in response to the conditional probability in the environment. Previous studies have revealed that efficient conflict control could be attained by predicting and adapting to the changing control demand. However, it is unclear whether cognitive flexibility could also be gained by predicting and adapting to the changing control demand. The present study aimed to explore this issue by combining the model-based analyses of behavioral and neuroimaging data with a probabilistic cued task switching paradigm. We demonstrated that the Bayesian surprise (i.e. unsigned precision-weighted prediction error [PE]) negatively modulated the connections among stimulus processing brain regions and control regions/networks. The effect of Bayesian surprise modulation on these connections guided control engagement as reflected by the control PE effect on behavior, which in turn facilitated cognitive flexibility. These results bridge a gap in the literature by illustrating the neural and behavioral effect of control demand prediction (or PE) on cognitive flexibility and offer novel insights into the source of switch cost and the mechanism of cognitive flexibility.
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Affiliation(s)
- Lei Qiao
- School of Psychology, Shenzhen University, Shenzhen, China
| | - Lijie Zhang
- School of Psychology, Shenzhen University, Shenzhen, China
| | - Antao Chen
- Department Psychology, Shanghai Univ Sport, Shanghai 200438, Peoples R China
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19
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Trudel N, Lockwood PL, Rushworth MFS, Wittmann MK. Neural activity tracking identity and confidence in social information. eLife 2023; 12:71315. [PMID: 36763582 PMCID: PMC9917428 DOI: 10.7554/elife.71315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 12/15/2022] [Indexed: 02/11/2023] Open
Abstract
Humans learn about the environment either directly by interacting with it or indirectly by seeking information about it from social sources such as conspecifics. The degree of confidence in the information obtained through either route should determine the impact that it has on adapting and changing behaviour. We examined whether and how behavioural and neural computations differ during non-social learning as opposed to learning from social sources. Trial-wise confidence judgements about non-social and social information sources offered a window into this learning process. Despite matching exactly the statistical features of social and non-social conditions, confidence judgements were more accurate and less changeable when they were made about social as opposed to non-social information sources. In addition to subjective reports of confidence, differences were also apparent in the Bayesian estimates of participants' subjective beliefs. Univariate activity in dorsomedial prefrontal cortex and posterior temporoparietal junction more closely tracked confidence about social as opposed to non-social information sources. In addition, the multivariate patterns of activity in the same areas encoded identities of social information sources compared to non-social information sources.
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Affiliation(s)
- Nadescha Trudel
- Wellcome Centre of Integrative Neuroimaging (WIN), Department of Experimental Psychology, University of OxfordOxfordUnited Kingdom
- Wellcome Centre for Human Neuroimaging, University College LondonLondonUnited Kingdom
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College LondonLondonUnited Kingdom
| | - Patricia L Lockwood
- Centre for Human Brain Health, School of Psychology, University of BirminghamBirminghamUnited Kingdom
- Institute for Mental Health, School of Psychology, University of BirminghamBirminghamUnited Kingdom
- Centre for Developmental Science, School of Psychology, University of BirminghamBirminghamUnited Kingdom
| | - Matthew FS Rushworth
- Wellcome Centre of Integrative Neuroimaging (WIN), Department of Experimental Psychology, University of OxfordOxfordUnited Kingdom
- Wellcome Centre of Integrative Neuroimaging (WIN), Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of OxfordOxfordUnited Kingdom
| | - Marco K Wittmann
- Wellcome Centre of Integrative Neuroimaging (WIN), Department of Experimental Psychology, University of OxfordOxfordUnited Kingdom
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College LondonLondonUnited Kingdom
- Department of Experimental Psychology, University College LondonLondonUnited Kingdom
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20
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Fromm S, Katthagen T, Deserno L, Heinz A, Kaminski J, Schlagenhauf F. Belief Updating in Subclinical and Clinical Delusions. SCHIZOPHRENIA BULLETIN OPEN 2023; 4:sgac074. [PMID: 39145350 PMCID: PMC11207849 DOI: 10.1093/schizbullopen/sgac074] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/16/2024]
Abstract
Background and Hypothesis Current frameworks propose that delusions result from aberrant belief updating due to altered prediction error (PE) signaling and misestimation of environmental volatility. We aimed to investigate whether behavioral and neural signatures of belief updating are specifically related to the presence of delusions or generally associated with manifest schizophrenia. Methods Our cross-sectional design includes human participants (n[female/male] = 66[25/41]), stratified into four groups: healthy participants with minimal (n = 22) or strong delusional-like ideation (n = 18), and participants with diagnosed schizophrenia with minimal (n = 13) or strong delusions (n = 13), resulting in a 2 × 2 design, which allows to test for the effects of delusion and diagnosis. Participants performed a reversal learning task with stable and volatile task contingencies during fMRI scanning. We formalized learning with a hierarchical Gaussian filter model and conducted model-based fMRI analysis regarding beliefs of outcome uncertainty and volatility, precision-weighted PEs of the outcome- and the volatility-belief. Results Patients with schizophrenia as compared to healthy controls showed lower accuracy and heightened choice switching, while delusional ideation did not affect these measures. Participants with delusions showed increased precision-weighted PE-related neural activation in fronto-striatal regions. People with diagnosed schizophrenia overestimated environmental volatility and showed an attenuated neural representation of volatility in the anterior insula, medial frontal and angular gyrus. Conclusions Delusional beliefs are associated with altered striatal PE-signals. Juxtaposing, the potentially unsettling belief that the environment is constantly changing and weaker neural encoding of this subjective volatility seems to be associated with manifest schizophrenia, but not with the presence of delusional ideation.
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Affiliation(s)
- Sophie Fromm
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health CCM, Department of Psychiatry and Neuroscience | CCM, NeuroCure Clinical Research Center, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Einstein Center for Neurosciences Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
- Department of Psychology, Humboldt-Universität zu Berlin, Germany
| | - Teresa Katthagen
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health CCM, Department of Psychiatry and Neuroscience | CCM, NeuroCure Clinical Research Center, Berlin, Germany
| | - Lorenz Deserno
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Würzburg, Würzburg, Germany
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Department of Psychiatry and Psychotherapy, Technische Universität, Dresden, Germany
| | - Andreas Heinz
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health CCM, Department of Psychiatry and Neuroscience | CCM, NeuroCure Clinical Research Center, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Einstein Center for Neurosciences Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Jakob Kaminski
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health CCM, Department of Psychiatry and Neuroscience | CCM, NeuroCure Clinical Research Center, Berlin, Germany
| | - Florian Schlagenhauf
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health CCM, Department of Psychiatry and Neuroscience | CCM, NeuroCure Clinical Research Center, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Einstein Center for Neurosciences Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
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21
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Müller-Pinzler L, Czekalla N, Mayer AV, Schröder A, Stolz DS, Paulus FM, Krach S. Neurocomputational mechanisms of affected beliefs. Commun Biol 2022; 5:1241. [PMCID: PMC9663730 DOI: 10.1038/s42003-022-04165-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 10/25/2022] [Indexed: 11/16/2022] Open
Abstract
AbstractThe feedback people receive on their behavior shapes the process of belief formation and self-efficacy in mastering a particular task. However, the neural and computational mechanisms of how the subjective value of self-efficacy beliefs, and the corresponding affect, influence the learning process remain unclear. We investigated these mechanisms during self-efficacy belief formation using fMRI, pupillometry, and computational modeling, and by analyzing individual differences in affective experience. Biases in the formation of self-efficacy beliefs were associated with affect, pupil dilation, and neural activity within the anterior insula, amygdala, ventral tegmental area/ substantia nigra, and mPFC. Specifically, neural and pupil responses mapped the valence of the prediction errors in correspondence with individuals’ experienced affective states and learning biases during self-efficacy belief formation. Together with the functional connectivity dynamics of the anterior insula within this network, our results provide evidence for neural and computational mechanisms of how we arrive at affected beliefs.
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22
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Pisauro MA, Fouragnan EF, Arabadzhiyska DH, Apps MAJ, Philiastides MG. Neural implementation of computational mechanisms underlying the continuous trade-off between cooperation and competition. Nat Commun 2022; 13:6873. [PMID: 36369180 PMCID: PMC9652314 DOI: 10.1038/s41467-022-34509-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 10/27/2022] [Indexed: 11/13/2022] Open
Abstract
Social interactions evolve continuously. Sometimes we cooperate, sometimes we compete, while at other times we strategically position ourselves somewhere in between to account for the ever-changing social contexts around us. Research on social interactions often focuses on a binary dichotomy between competition and cooperation, ignoring people's evolving shifts along a continuum. Here, we develop an economic game - the Space Dilemma - where two players change their degree of cooperativeness over time in cooperative and competitive contexts. Using computational modelling we show how social contexts bias choices and characterise how inferences about others' intentions modulate cooperativeness. Consistent with the modelling predictions, brain regions previously linked to social cognition, including the temporo-parietal junction, dorso-medial prefrontal cortex and the anterior cingulate gyrus, encode social prediction errors and context-dependent signals, correlating with shifts along a cooperation-competition continuum. These results provide a comprehensive account of the computational and neural mechanisms underlying the continuous trade-off between cooperation and competition.
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Affiliation(s)
- M A Pisauro
- Department of Experimental Psychology, University of Oxford, Oxford, UK.
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK.
- School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK.
| | - E F Fouragnan
- Department of Experimental Psychology, University of Oxford, Oxford, UK
- School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
- Brain Research Imaging Center and School of Psychology, Faculty of Health, University of Plymouth, Plymouth, UK
| | - D H Arabadzhiyska
- School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
| | - M A J Apps
- Department of Experimental Psychology, University of Oxford, Oxford, UK
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK
| | - M G Philiastides
- School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
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23
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Incorporating social knowledge structures into computational models. Nat Commun 2022; 13:6205. [PMID: 36266284 PMCID: PMC9584930 DOI: 10.1038/s41467-022-33418-2] [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: 05/06/2021] [Accepted: 09/16/2022] [Indexed: 12/24/2022] Open
Abstract
To navigate social interactions successfully, humans need to continuously learn about the personality traits of other people (e.g., how helpful or aggressive is the other person?). However, formal models that capture the complexities of social learning processes are currently lacking. In this study, we specify and test potential strategies that humans can employ for learning about others. Standard Rescorla-Wagner (RW) learning models only capture parts of the learning process because they neglect inherent knowledge structures and omit previously acquired knowledge. We therefore formalize two social knowledge structures and implement them in hybrid RW models to test their usefulness across multiple social learning tasks. We name these concepts granularity (knowledge structures about personality traits that can be utilized at different levels of detail during learning) and reference points (previous knowledge formalized into representations of average people within a social group). In five behavioural experiments, results from model comparisons and statistical analyses indicate that participants efficiently combine the concepts of granularity and reference points-with the specific combinations in models depending on the people and traits that participants learned about. Overall, our experiments demonstrate that variants of RW algorithms, which incorporate social knowledge structures, describe crucial aspects of the dynamics at play when people interact with each other.
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24
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Bègue I, Brakowski J, Seifritz E, Dagher A, Tobler PN, Kirschner M, Kaiser S. Cerebellar and cortico-striatal-midbrain contributions to reward-cognition processes and apathy within the psychosis continuum. Schizophr Res 2022; 246:85-94. [PMID: 35728420 DOI: 10.1016/j.schres.2022.06.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 04/30/2022] [Accepted: 06/11/2022] [Indexed: 11/29/2022]
Abstract
Negative symptoms in the psychosis continuum are linked to impairments in reward processing and cognitive function. Processes at the interface of reward processing and cognition and their relation to negative symptoms remain little studied, despite evidence suggestive of integration in mechanisms and neural circuitry. Here, we investigated brain activation during reward-dependent modulation of working memory (WM) and their relationship to negative symptoms in subclinical and early stages of the psychosis continuum. We included 27 persons with high schizotypal personality traits and 23 patients with first episode psychosis as well as 27 healthy controls. Participants underwent functional magnetic resonance imaging while performing an established 2-back WM task with two reward levels (5 CHF vs. no reward), which allowed us to assess common reward-cognition regions through whole-brain conjunction analyses and to investigate relations with clinical scores of negative symptoms. As expected for behavior, reward facilitated performance while cognitive load diminished it. At the neural level, the conjunction of high reward and high cognitive load contrasts across the psychosis continuum showed increased hemodynamic activity in the thalamus and the cerebellar vermis. During high cognitive load, more severe apathy but not diminished expression in the psychosis continuum was associated with reduced activity in right lateral orbitofrontal cortex, midbrain, posterior vermal cerebellum, caudate and lateral parietal cortex. Our results suggest that hypoactivity in the cerebellar vermis and the cortical-striatal-midbrain-circuitry in the psychosis continuum relates to apathy possibly via impaired flexible cognitive resource allocation for effective goal pursuit.
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Affiliation(s)
- Indrit Bègue
- Division of Adult Psychiatry, Department of Psychiatry, Geneva University Hospitals, Switzerland.
| | - Janis Brakowski
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Switzerland
| | - Erich Seifritz
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Switzerland
| | - Alain Dagher
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Philippe N Tobler
- Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich, Switzerland
| | - Matthias Kirschner
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Switzerland
| | - Stefan Kaiser
- Division of Adult Psychiatry, Department of Psychiatry, Geneva University Hospitals, Switzerland
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25
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Le Petit M, Eustache F, Perrier J, de La Sayette V, Desgranges B, Laisney M. Functional connectivity of the medial prefrontal cortex related to mindreading abilities. Cereb Cortex Commun 2022; 3:tgac032. [PMID: 36090668 PMCID: PMC9454031 DOI: 10.1093/texcom/tgac032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 07/06/2022] [Accepted: 07/11/2022] [Indexed: 11/13/2022] Open
Abstract
The medial prefrontal cortex is a key region of mindreading belonging to the mentalizing system, a set of brain areas underlying mental state inference based on reasoning on social concepts. The aim of this study was to characterize the functional connectivity between regions involved in mindreading and to highlight the processes it underpins, focusing on the dorsal and ventral parts of the medial prefrontal cortex. We analyzed resting-state functional magnetic resonance imaging of 56 healthy volunteers, to study the relationship between mindreading abilities and functional connectivity of the medial prefrontal cortex. Cognitive mindreading performances were correlated with connectivity between the medial prefrontal cortex and frontal regions involved in the regulation of the salience of one’s own mental contents, with a distinction between the dorsal part connected to regions subtending inhibition processes and the ventral part to emotional regions. Affective mindreading performances were negatively correlated with negative connectivity of the ventro- and dorsomedial prefrontal cortex with sensorimotor regions belonging to the mirror neuron system subtending the simulation of mental states. These findings suggested a role of the medial prefrontal cortex to decrease the salience of one’s own mental content and in the antisynchronous interaction between the mentalizing and mirror neurons systems.
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Affiliation(s)
- Marine Le Petit
- Normandie Univ, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, CHU de Caen, Centre Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine , 14000 Caen, France
| | - Francis Eustache
- Normandie Univ, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, CHU de Caen, Centre Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine , 14000 Caen, France
| | - Joy Perrier
- Normandie Univ, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, CHU de Caen, Centre Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine , 14000 Caen, France
| | - Vincent de La Sayette
- Normandie Univ, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, CHU de Caen, Centre Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine , 14000 Caen, France
| | - Béatrice Desgranges
- Normandie Univ, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, CHU de Caen, Centre Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine , 14000 Caen, France
| | - Mickaël Laisney
- Normandie Univ, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, CHU de Caen, Centre Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine , 14000 Caen, France
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26
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Rethinking delusions: A selective review of delusion research through a computational lens. Schizophr Res 2022; 245:23-41. [PMID: 33676820 PMCID: PMC8413395 DOI: 10.1016/j.schres.2021.01.023] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Revised: 01/27/2021] [Accepted: 01/29/2021] [Indexed: 02/06/2023]
Abstract
Delusions are rigid beliefs held with high certainty despite contradictory evidence. Notwithstanding decades of research, we still have a limited understanding of the computational and neurobiological alterations giving rise to delusions. In this review, we highlight a selection of recent work in computational psychiatry aimed at developing quantitative models of inference and its alterations, with the goal of providing an explanatory account for the form of delusional beliefs in psychosis. First, we assess and evaluate the experimental paradigms most often used to study inferential alterations in delusions. Based on our review of the literature and theoretical considerations, we contend that classic draws-to-decision paradigms are not well-suited to isolate inferential processes, further arguing that the commonly cited 'jumping-to-conclusion' bias may reflect neither delusion-specific nor inferential alterations. Second, we discuss several enhancements to standard paradigms that show promise in more effectively isolating inferential processes and delusion-related alterations therein. We further draw on our recent work to build an argument for a specific failure mode for delusions consisting of prior overweighting in high-level causal inferences about partially observable hidden states. Finally, we assess plausible neurobiological implementations for this candidate failure mode of delusional beliefs and outline promising future directions in this area.
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27
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Witkowski PP, Park SA, Boorman ED. Neural mechanisms of credit assignment for inferred relationships in a structured world. Neuron 2022; 110:2680-2690.e9. [PMID: 35714610 DOI: 10.1016/j.neuron.2022.05.021] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 04/12/2022] [Accepted: 05/18/2022] [Indexed: 10/18/2022]
Abstract
Animals abstract compact representations of a task's structure, which supports accelerated learning and flexible behavior. Whether and how such abstracted representations may be used to assign credit for inferred, but unobserved, relationships in structured environments are unknown. We develop a hierarchical reversal-learning task and Bayesian learning model to assess the computational and neural mechanisms underlying how humans infer specific choice-outcome associations via structured knowledge. We find that the medial prefrontal cortex (mPFC) efficiently represents hierarchically related choice-outcome associations governed by the same latent cause, using a generalized code to assign credit for both experienced and inferred outcomes. Furthermore, the mPFC and lateral orbitofrontal cortex track the current "position" within a latent association space that generalizes over stimuli. Collectively, these findings demonstrate the importance of both tracking the current position in an abstracted task space and efficient, generalizable representations in the prefrontal cortex for supporting flexible learning and inference in structured environments.
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Affiliation(s)
- Phillip P Witkowski
- Center for Mind and Brain, University of California, Davis, Davis, CA 95618; Department of Psychology, University of California, Davis, Davis, CA 95618.
| | - Seongmin A Park
- Center for Mind and Brain, University of California, Davis, Davis, CA 95618
| | - Erie D Boorman
- Center for Mind and Brain, University of California, Davis, Davis, CA 95618; Department of Psychology, University of California, Davis, Davis, CA 95618.
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28
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Abstract
Conflicting evidence about how the brain processes social and individual learning stems from which type of information is presented as the primary source of knowledge during experiments.
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Affiliation(s)
- Orit Nafcha
- School of Psychological Sciences, University of Haifa, Haifa, Israel
| | - Uri Hertz
- Department of Cognitive Sciences, University of Haifa, Haifa, Israel
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29
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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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30
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Basolateral and central amygdala orchestrate how we learn whom to trust. Commun Biol 2021; 4:1329. [PMID: 34824373 PMCID: PMC8617284 DOI: 10.1038/s42003-021-02815-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Accepted: 10/20/2021] [Indexed: 11/08/2022] Open
Abstract
Cooperation and mutual trust are essential in our society, yet not everybody is trustworthy. In this fMRI study, 62 healthy volunteers performed a repeated trust game, placing trust in a trustworthy or an untrustworthy player. We found that the central amygdala was active during trust behavior planning while the basolateral amygdala was active during outcome evaluation. When planning the trust behavior, central and basolateral amygdala activation was stronger for the untrustworthy player compared to the trustworthy player but only in participants who actually learned to differentiate the trustworthiness of the players. Independent of learning success, nucleus accumbens encoded whether trust was reciprocated. This suggests that learning whom to trust is not related to reward processing in the nucleus accumbens, but rather to engagement of the amygdala. Our study overcomes major empirical gaps between animal models and human neuroimaging and shows how different subnuclei of the amygdala and connected areas orchestrate learning to form different subjective trustworthiness beliefs about others and guide trust choice behavior.
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31
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Menon R, Süß T, Oliveira VEDM, Neumann ID, Bludau A. Neurobiology of the lateral septum: regulation of social behavior. Trends Neurosci 2021; 45:27-40. [PMID: 34810019 DOI: 10.1016/j.tins.2021.10.010] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 10/12/2021] [Accepted: 10/22/2021] [Indexed: 12/21/2022]
Abstract
Social interactions are essential for mammalian life and are regulated by evolutionary conserved neuronal mechanisms. An individual's internal state, experiences, and the nature of the social stimulus are critical for determining apt responses to social situations. The lateral septum (LS) - a structure of the basal forebrain - integrates abundant cortical and subcortical inputs, and projects to multiple downstream regions to generate appropriate behavioral responses. Although incoming cognitive information is indispensable for contextualizing a social stimulus, neuromodulatory information related to the internal state of the organism significantly influences the behavioral outcome as well. This review article provides an overview of the neuroanatomical properties of the LS, and examines its neurochemical (neuropeptidergic and hormonal) signaling, which provide the neuromodulatory information essential for fine-tuning social behavior across the lifespan.
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Affiliation(s)
- Rohit Menon
- Department of Behavioral and Molecular Neurobiology, University of Regensburg, Regensburg, Germany
| | - Theresa Süß
- Department of Behavioral and Molecular Neurobiology, University of Regensburg, Regensburg, Germany
| | - Vinícius Elias de Moura Oliveira
- Department of Behavioral and Molecular Neurobiology, University of Regensburg, Regensburg, Germany; Laboratory of Neuroendocrinology, GIGA Neurosciences, University of Liege, Liege, Belgium
| | - Inga D Neumann
- Department of Behavioral and Molecular Neurobiology, University of Regensburg, Regensburg, Germany
| | - Anna Bludau
- Department of Behavioral and Molecular Neurobiology, University of Regensburg, Regensburg, Germany.
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32
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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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33
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Vandewouw MM, Safar K, Mossad SI, Lu J, Lerch JP, Anagnostou E, Taylor MJ. Do shapes have feelings? Social attribution in children with autism spectrum disorder and attention-deficit/hyperactivity disorder. Transl Psychiatry 2021; 11:493. [PMID: 34564704 PMCID: PMC8464598 DOI: 10.1038/s41398-021-01625-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 08/16/2021] [Accepted: 08/26/2021] [Indexed: 12/14/2022] Open
Abstract
Theory of mind (ToM) deficits are common in children with neurodevelopmental disorders (NDDs), such as autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), which contribute to their social and cognitive difficulties. The social attribution task (SAT) involves geometrical shapes moving in patterns that depict social interactions and is known to recruit brain regions from the classic ToM network. To better understand ToM in ASD and ADHD children, we examined the neural correlates using the SAT and functional magnetic resonance imaging (fMRI) in a cohort of 200 children: ASD (N = 76), ADHD (N = 74) and typically developing (TD; N = 50) (4-19 years). In the scanner, participants were presented with SAT videos corresponding to social help, social threat, and random conditions. Contrasting social vs. random, the ASD compared with TD children showed atypical activation in ToM brain areas-the middle temporal and anterior cingulate gyri. In the social help vs. social threat condition, atypical activation of the bilateral middle cingulate and right supramarginal and superior temporal gyri was shared across the NDD children, with between-diagnosis differences only being observed in the right fusiform. Data-driven subgrouping identified two distinct subgroups spanning all groups that differed in both their clinical characteristics and brain-behaviour relations with ToM ability.
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Affiliation(s)
- Marlee M Vandewouw
- Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, ON, Canada.
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, ON, Canada.
- Autism Research Center, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada.
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada.
| | - Kristina Safar
- Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, ON, Canada
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, ON, Canada
| | - Sarah I Mossad
- Department of Psychology, Hospital for Sick Children, Toronto, ON, Canada
| | - Julie Lu
- Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, ON, Canada
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, ON, Canada
| | - Jason P Lerch
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, ON, Canada
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Evdokia Anagnostou
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, ON, Canada
- Autism Research Center, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Margot J Taylor
- Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, ON, Canada
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, ON, Canada
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
- Department of Psychology, University of Toronto, Toronto, ON, Canada
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34
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Lockwood PL, Klein-Flügge MC. Computational modelling of social cognition and behaviour-a reinforcement learning primer. Soc Cogn Affect Neurosci 2021; 16:761-771. [PMID: 32232358 PMCID: PMC8343561 DOI: 10.1093/scan/nsaa040] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Revised: 02/07/2020] [Accepted: 03/18/2020] [Indexed: 02/06/2023] Open
Abstract
Social neuroscience aims to describe the neural systems that underpin social cognition and behaviour. Over the past decade, researchers have begun to combine computational models with neuroimaging to link social computations to the brain. Inspired by approaches from reinforcement learning theory, which describes how decisions are driven by the unexpectedness of outcomes, accounts of the neural basis of prosocial learning, observational learning, mentalizing and impression formation have been developed. Here we provide an introduction for researchers who wish to use these models in their studies. We consider both theoretical and practical issues related to their implementation, with a focus on specific examples from the field.
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Affiliation(s)
- Patricia L Lockwood
- Department of Experimental Psychology, University of Oxford, Oxford OX1 3PH, United Kingdom
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford OX1 3PH, United Kingdom
| | - Miriam C Klein-Flügge
- Department of Experimental Psychology, University of Oxford, Oxford OX1 3PH, United Kingdom
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford OX1 3PH, United Kingdom
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35
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Liu M, Dong W, Qin S, Verguts T, Chen Q. Electrophysiological Signatures of Hierarchical Learning. Cereb Cortex 2021; 32:626-639. [PMID: 34339505 DOI: 10.1093/cercor/bhab245] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 06/26/2021] [Accepted: 06/27/2021] [Indexed: 11/13/2022] Open
Abstract
Human perception and learning is thought to rely on a hierarchical generative model that is continuously updated via precision-weighted prediction errors (pwPEs). However, the neural basis of such cognitive process and how it unfolds during decision-making remain poorly understood. To investigate this question, we combined a hierarchical Bayesian model (i.e., Hierarchical Gaussian Filter [HGF]) with electroencephalography (EEG), while participants performed a probabilistic reversal learning task in alternatingly stable and volatile environments. Behaviorally, the HGF fitted significantly better than two control, nonhierarchical, models. Neurally, low-level and high-level pwPEs were independently encoded by the P300 component. Low-level pwPEs were reflected in the theta (4-8 Hz) frequency band, but high-level pwPEs were not. Furthermore, the expressions of high-level pwPEs were stronger for participants with better HGF fit. These results indicate that the brain employs hierarchical learning and encodes both low- and high-level learning signals separately and adaptively.
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Affiliation(s)
- Meng Liu
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, 510631 Guangzhou, China.,School of Psychology, South China Normal University, 510631 Guangzhou, China.,Center for Studies of Psychological Application, South China Normal University, 510631 Guangzhou, China.,Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Wenshan Dong
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, 510631 Guangzhou, China.,School of Psychology, South China Normal University, 510631 Guangzhou, China.,Center for Studies of Psychological Application, South China Normal University, 510631 Guangzhou, China.,Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Shaozheng Qin
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, 100875 Beijing, China
| | - Tom Verguts
- Department of Experimental Psychology, Ghent University, B-9000 Ghent, Belgium
| | - Qi Chen
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, 510631 Guangzhou, China.,School of Psychology, South China Normal University, 510631 Guangzhou, China.,Center for Studies of Psychological Application, South China Normal University, 510631 Guangzhou, China.,Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
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36
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Fox S. Psychomotor Predictive Processing. ENTROPY (BASEL, SWITZERLAND) 2021; 23:806. [PMID: 34202804 PMCID: PMC8303599 DOI: 10.3390/e23070806] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 06/22/2021] [Accepted: 06/23/2021] [Indexed: 02/06/2023]
Abstract
Psychomotor experience can be based on what people predict they will experience, rather than on sensory inputs. It has been argued that disconnects between human experience and sensory inputs can be addressed better through further development of predictive processing theory. In this paper, the scope of predictive processing theory is extended through three developments. First, by going beyond previous studies that have encompassed embodied cognition but have not addressed some fundamental aspects of psychomotor functioning. Second, by proposing a scientific basis for explaining predictive processing that spans objective neuroscience and subjective experience. Third, by providing an explanation of predictive processing that can be incorporated into the planning and operation of systems involving robots and other new technologies. This is necessary because such systems are becoming increasingly common and move us farther away from the hunter-gatherer lifestyles within which our psychomotor functioning evolved. For example, beliefs that workplace robots are threatening can generate anxiety, while wearing hardware, such as augmented reality headsets and exoskeletons, can impede the natural functioning of psychomotor systems. The primary contribution of the paper is the introduction of a new formulation of hierarchical predictive processing that is focused on psychomotor functioning.
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Affiliation(s)
- Stephen Fox
- VTT Technical Research Centre of Finland, FI-02150 Espoo, Finland
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37
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Reiter AMF, Diaconescu AO, Eppinger B, Li SC. Human aging alters social inference about others' changing intentions. Neurobiol Aging 2021; 103:98-108. [PMID: 33845400 DOI: 10.1016/j.neurobiolaging.2021.01.034] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 01/08/2021] [Accepted: 01/09/2021] [Indexed: 01/28/2023]
Abstract
Decoding others' intentions accurately in order to adapt one's own behavior is pivotal throughout life. In this study, we asked how younger and older adults deal with uncertainty in dynamic social environments. We used an advice-taking paradigm together with Bayesian modeling to characterize effects of aging on learning about others' time-varying intentions. We observed age differences when comparing learning on two levels of social uncertainty: the fidelity of the adviser and the volatility of intentions. Older adults expected the adviser to change his/her intentions more frequently (i.e., a higher volatility of the adviser). They also showed higher confidence (i.e., precision) in their volatility beliefs and were less willing to change their beliefs about volatility over the course of the experiment. This led them to update their predictions about the fidelity of the adviser more quickly. Potentially indicative of stereotype effects, we observed that older advisers were perceived as more volatile, but also more faithful than younger advisers. This offers new insights into adult age differences in response to social uncertainty.
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Affiliation(s)
- Andrea M F Reiter
- Lifespan Developmental Neuroscience, Faculty of Psychology, Technische Universität Dresden, Germany; Department of Neurology, Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK; Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University of Würzburg, Würzburg, Germany.
| | - Andreea O Diaconescu
- Translational Neuromodeling Unit, University of Zurich & ETH Zurich, Switzerland; Department of Psychiatry, University of Basel, Switzerland; Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), University of Toronto, Canada
| | - Ben Eppinger
- Lifespan Developmental Neuroscience, Faculty of Psychology, Technische Universität Dresden, Germany; Department of Psychology, Concordia University, Canada; PERFORM Centre, Concordia University, Canada
| | - Shu-Chen Li
- Lifespan Developmental Neuroscience, Faculty of Psychology, Technische Universität Dresden, Germany; CeTI - Centre for Tactile Internet With Human-in-the-Loop, Technische Universität Dresden, Germany
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38
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Abstract
A large body of work has linked dopaminergic signaling to learning and reward processing. It stresses the role of dopamine in reward prediction error signaling, a key neural signal that allows us to learn from past experiences, and that facilitates optimal choice behavior. Latterly, it has become clear that dopamine does not merely code prediction error size but also signals the difference between the expected value of rewards, and the value of rewards actually received, which is obtained through the integration of reward attributes such as the type, amount, probability and delay. More recent work has posited a role of dopamine in learning beyond rewards. These theories suggest that dopamine codes absolute or unsigned prediction errors, playing a key role in how the brain models associative regularities within its environment, while incorporating critical information about the reliability of those regularities. Work is emerging supporting this perspective and, it has inspired theoretical models of how certain forms of mental pathology may emerge in relation to dopamine function. Such pathology is frequently related to disturbed inferences leading to altered internal models of the environment. Thus, it is critical to understand the role of dopamine in error-related learning and inference.
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Affiliation(s)
- Kelly M. J. Diederen
- Department of Psychosis Studies,
Institute of Psychiatry, Psychology and Neuroscience, King’s College London,
London, UK
| | - Paul C. Fletcher
- Department of Psychiatry,
University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough
NHS Foundation Trust, Cambridge, UK
- Wellcome Trust MRC Institute of
Metabolic Science, University of Cambridge, Cambridge Biomedical Campus,
Cambridge, UK
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39
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Smith R, Badcock P, Friston KJ. Recent advances in the application of predictive coding and active inference models within clinical neuroscience. Psychiatry Clin Neurosci 2021; 75:3-13. [PMID: 32860285 DOI: 10.1111/pcn.13138] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 08/01/2020] [Accepted: 08/25/2020] [Indexed: 12/15/2022]
Abstract
Research in clinical neuroscience is founded on the idea that a better understanding of brain (dys)function will improve our ability to diagnose and treat neurological and psychiatric disorders. In recent years, neuroscience has converged on the notion that the brain is a 'prediction machine,' in that it actively predicts the sensory input that it will receive if one or another course of action is chosen. These predictions are used to select actions that will (most often, and in the long run) maintain the body within the narrow range of physiological states consistent with survival. This insight has given rise to an area of clinical computational neuroscience research that focuses on characterizing neural circuit architectures that can accomplish these predictive functions, and on how the associated processes may break down or become aberrant within clinical conditions. Here, we provide a brief review of examples of recent work on the application of predictive processing models of brain function to study clinical (psychiatric) disorders, with the aim of highlighting current directions and their potential clinical utility. We offer examples of recent conceptual models, formal mathematical models, and applications of such models in empirical research in clinical populations, with a focus on making this material accessible to clinicians without expertise in computational neuroscience. In doing so, we aim to highlight the potential insights and opportunities that understanding the brain as a prediction machine may offer to clinical research and practice.
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Affiliation(s)
- Ryan Smith
- Laureate Institute for Brain Research, Oklahoma, USA
| | - Paul Badcock
- Centre for Youth Mental Health, The University of Melbourne, Victoria, Australia.,Orygen, Victoria, Australia.,Melbourne School of Psychological Sciences, The University of Melbourne, Victoria, Australia
| | - Karl J Friston
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, UK
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40
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Iglesias S, Kasper L, Harrison SJ, Manka R, Mathys C, Stephan KE. Cholinergic and dopaminergic effects on prediction error and uncertainty responses during sensory associative learning. Neuroimage 2020; 226:117590. [PMID: 33285332 DOI: 10.1016/j.neuroimage.2020.117590] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2020] [Revised: 10/20/2020] [Accepted: 11/19/2020] [Indexed: 01/11/2023] Open
Abstract
Navigating the physical world requires learning probabilistic associations between sensory events and their change in time (volatility). Bayesian accounts of this learning process rest on hierarchical prediction errors (PEs) that are weighted by estimates of uncertainty (or its inverse, precision). In a previous fMRI study we found that low-level precision-weighted PEs about visual outcomes (that update beliefs about associations) activated the putative dopaminergic midbrain; by contrast, precision-weighted PEs about cue-outcome associations (that update beliefs about volatility) activated the cholinergic basal forebrain. These findings suggested selective dopaminergic and cholinergic influences on precision-weighted PEs at different hierarchical levels. Here, we tested this hypothesis, repeating our fMRI study under pharmacological manipulations in healthy participants. Specifically, we performed two pharmacological fMRI studies with a between-subject double-blind placebo-controlled design: study 1 used antagonists of dopaminergic (amisulpride) and muscarinic (biperiden) receptors, study 2 used enhancing drugs of dopaminergic (levodopa) and cholinergic (galantamine) modulation. Pooled across all pharmacological conditions of study 1 and study 2, respectively, we found that low-level precision-weighted PEs activated the midbrain and high-level precision-weighted PEs the basal forebrain as in our previous study. However, we found pharmacological effects on brain activity associated with these computational quantities only when splitting the precision-weighted PEs into their PE and precision components: in a brainstem region putatively containing cholinergic (pedunculopontine and laterodorsal tegmental) nuclei, biperiden (compared to placebo) enhanced low-level PE responses and attenuated high-level PE activity, while amisulpride reduced high-level PE responses. Additionally, in the putative dopaminergic midbrain, galantamine compared to placebo enhanced low-level PE responses (in a body-weight dependent manner) and amisulpride enhanced high-level precision activity. Task behaviour was not affected by any of the drugs. These results do not support our hypothesis of a clear-cut dichotomy between different hierarchical inference levels and neurotransmitter systems, but suggest a more complex interaction between these neuromodulatory systems and hierarchical Bayesian quantities. However, our present results may have been affected by confounds inherent to pharmacological fMRI. We discuss these confounds and outline improved experimental tests for the future.
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Affiliation(s)
- Sandra Iglesias
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & Swiss Federal Institute of Technology (ETH Zurich), Wilfriedstr. 6, 8032 Zurich, Switzerland.
| | - Lars Kasper
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & Swiss Federal Institute of Technology (ETH Zurich), Wilfriedstr. 6, 8032 Zurich, Switzerland; Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Switzerland
| | - Samuel J Harrison
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & Swiss Federal Institute of Technology (ETH Zurich), Wilfriedstr. 6, 8032 Zurich, Switzerland
| | - Robert Manka
- Department of Cardiology, University Hospital Zurich, Switzerland
| | - Christoph Mathys
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & Swiss Federal Institute of Technology (ETH Zurich), Wilfriedstr. 6, 8032 Zurich, Switzerland; Interacting Minds Centre, Aarhus University, Aarhus, Denmark
| | - Klaas E Stephan
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & Swiss Federal Institute of Technology (ETH Zurich), Wilfriedstr. 6, 8032 Zurich, Switzerland; Max Planck Institute for Metabolism Research, Cologne, Germany
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Smith R, Kuplicki R, Feinstein J, Forthman KL, Stewart JL, Paulus MP, Khalsa SS. A Bayesian computational model reveals a failure to adapt interoceptive precision estimates across depression, anxiety, eating, and substance use disorders. PLoS Comput Biol 2020; 16:e1008484. [PMID: 33315893 PMCID: PMC7769623 DOI: 10.1371/journal.pcbi.1008484] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 12/28/2020] [Accepted: 10/31/2020] [Indexed: 12/16/2022] Open
Abstract
Recent neurocomputational theories have hypothesized that abnormalities in prior beliefs and/or the precision-weighting of afferent interoceptive signals may facilitate the transdiagnostic emergence of psychopathology. Specifically, it has been suggested that, in certain psychiatric disorders, interoceptive processing mechanisms either over-weight prior beliefs or under-weight signals from the viscera (or both), leading to a failure to accurately update beliefs about the body. However, this has not been directly tested empirically. To evaluate the potential roles of prior beliefs and interoceptive precision in this context, we fit a Bayesian computational model to behavior in a transdiagnostic patient sample during an interoceptive awareness (heartbeat tapping) task. Modelling revealed that, during an interoceptive perturbation condition (inspiratory breath-holding during heartbeat tapping), healthy individuals (N = 52) assigned greater precision to ascending cardiac signals than individuals with symptoms of anxiety (N = 15), depression (N = 69), co-morbid depression/anxiety (N = 153), substance use disorders (N = 131), and eating disorders (N = 14)-who failed to increase their precision estimates from resting levels. In contrast, we did not find strong evidence for differences in prior beliefs. These results provide the first empirical computational modeling evidence of a selective dysfunction in adaptive interoceptive processing in psychiatric conditions, and lay the groundwork for future studies examining how reduced interoceptive precision influences visceral regulation and interoceptively-guided decision-making.
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Affiliation(s)
- Ryan Smith
- Laureate Institute for Brain Research, Tulsa, Oklahoma, United States of America
| | - Rayus Kuplicki
- Laureate Institute for Brain Research, Tulsa, Oklahoma, United States of America
| | - Justin Feinstein
- Laureate Institute for Brain Research, Tulsa, Oklahoma, United States of America
- Oxley College of Health Sciences, The University of Tulsa, Tulsa, Oklahoma, United States of America
| | | | - Jennifer L. Stewart
- Laureate Institute for Brain Research, Tulsa, Oklahoma, United States of America
- Oxley College of Health Sciences, The University of Tulsa, Tulsa, Oklahoma, United States of America
| | - Martin P. Paulus
- Laureate Institute for Brain Research, Tulsa, Oklahoma, United States of America
- Oxley College of Health Sciences, The University of Tulsa, Tulsa, Oklahoma, United States of America
| | | | - Sahib S. Khalsa
- Laureate Institute for Brain Research, Tulsa, Oklahoma, United States of America
- Oxley College of Health Sciences, The University of Tulsa, Tulsa, Oklahoma, United States of America
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Huetter FK, Moehlendick B, Knop D, Siffert W. Lack of association of common polymorphisms linked to empathic behavior with self-reported trait empathy in healthy volunteers. Horm Behav 2020; 126:104841. [PMID: 32828797 DOI: 10.1016/j.yhbeh.2020.104841] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 08/07/2020] [Accepted: 08/14/2020] [Indexed: 01/28/2023]
Abstract
BACKGROUND In a previously specified sample of 421 healthy subjects, we found associations of a common oxytocin receptor (OXTR) polymorphism with self-reported trait empathy. In this study, we used this sample to explore polymorphisms in other genes which have been frequently linked to empathic behavior for associations with self-reported trait empathy: CD38 (CD38), involved in oxytocin secretion, the serotonin transporter (SLC6A4), the Catechol-O-Methyltransferase (COMT) and the corticotropin releasing hormone receptor 1 (CRHR1). METHODS We genotyped our sample for the following common polymorphisms: rs3796863 in the CD38 gene, 5-HTTLPR in the SLC6A4 gene, rs4680 in the COMT gene and rs242924 in the CRHR1 gene. Dispositional empathy was tested using Davis' Interpersonal Reactivity Index (IRI). We used a Bonferroni corrected alpha level of p = 0.002 to adjust for multiple comparisons. RESULTS None of the genotypes were associated with any of the IRI scales for the complete sample (n = 421) or for the sub-groups of male (n = 213) and female (n = 190) participants. Our sample of 421 participants achieved 95% power to detect effects greater than r = ±0.18. For smaller effects, however, false negatives could not be rejected with equal confidence as false positives. CONCLUSIONS We conclude that an association between the four polymorphisms with trait empathy measured by the IRI may not be present. We propose that the associations that have been found in other studies can be largely explained by differences in empathy-related constructs and measurements.
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Affiliation(s)
- Franz Korbinian Huetter
- Institut für Pharmakogenetik, Universitätsklinikum Essen, Universität Duisburg Essen, D-45122 Essen, Germany.
| | - Birte Moehlendick
- Institut für Pharmakogenetik, Universitätsklinikum Essen, Universität Duisburg Essen, D-45122 Essen, Germany
| | - Dietmar Knop
- Institut für Transfusionsmedizin, Universitätsklinikum Essen, Universität Duisburg Essen, D-45122 Essen, Germany
| | - Winfried Siffert
- Institut für Pharmakogenetik, Universitätsklinikum Essen, Universität Duisburg Essen, D-45122 Essen, Germany
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Hein TP, de Fockert J, Ruiz MH. State anxiety biases estimates of uncertainty and impairs reward learning in volatile environments. Neuroimage 2020; 224:117424. [PMID: 33035670 DOI: 10.1016/j.neuroimage.2020.117424] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 08/27/2020] [Accepted: 09/29/2020] [Indexed: 01/01/2023] Open
Abstract
Clinical and subclinical (trait) anxiety impairs decision making and interferes with learning. Less understood are the effects of temporary anxious states on learning and decision making in healthy populations, and whether these can serve as a model for clinical anxiety. Here we test whether anxious states in healthy individuals elicit a pattern of aberrant behavioural, neural, and physiological responses comparable with those found in anxiety disorders-particularly when processing uncertainty in unstable environments. In our study, both a state anxious and a control group learned probabilistic stimulus-outcome mappings in a volatile task environment while we recorded their electrophysiological (EEG) signals. By using a hierarchical Bayesian model of inference and learning, we assessed the effect of state anxiety on Bayesian belief updating with a focus on uncertainty estimates. State anxiety was associated with an underestimation of environmental uncertainty, and informational uncertainty about the reward tendency. Anxious individuals' beliefs about reward contingencies were more precise (had smaller uncertainty) and thus more resistant to updating, ultimately leading to impaired reward-based learning. State anxiety was also associated with greater uncertainty about volatility. We interpret this pattern as evidence that state anxious individuals are less tolerant to informational uncertainty about the contingencies governing their environment and more willing to be uncertain about the level of stability of the world itself. Further, we tracked the neural representation of belief update signals in the trial-by-trial EEG amplitudes. In control participants, lower-level precision-weighted prediction errors (pwPEs) about reward tendencies were represented in the ERP signals across central and parietal electrodes peaking at 496 ms, overlapping with the late P300 in classical ERP analysis. The state anxiety group did not exhibit a significant representation of low-level pwPEs, and there were no significant differences between the groups. Smaller variance in low-level pwPE about reward tendencies in state anxiety could partially account for the null results. Expanding previous computational work on trait anxiety, our findings establish that temporary anxious states in healthy individuals impair reward-based learning in volatile environments, primarily through changes in uncertainty estimates, which play a central role in current Bayesian accounts of perceptual inference and learning.
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Affiliation(s)
- Thomas P Hein
- Goldsmiths, University of London, Psychology Department, Whitehead Building, New Cross, London, SE146NW, United Kingdom
| | - Jan de Fockert
- Goldsmiths, University of London, Psychology Department, Whitehead Building, New Cross, London, SE146NW, United Kingdom
| | - Maria Herrojo Ruiz
- Goldsmiths, University of London, Psychology Department, Whitehead Building, New Cross, London, SE146NW, United Kingdom; Center for Cognition and Decision Making, Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russian Federation.
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44
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Brihmat N, Boulanouar K, Darmana R, Biganzoli A, Gasq D, Castel-Lacanal E, Marque P, Loubinoux I. Controlling for lesions, kinematics and physiological noise: impact on fMRI results of spastic post-stroke patients. MethodsX 2020; 7:101056. [PMID: 32995309 PMCID: PMC7509233 DOI: 10.1016/j.mex.2020.101056] [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: 06/06/2019] [Accepted: 09/01/2020] [Indexed: 11/15/2022] Open
Abstract
Functional magnetic resonance imaging (fMRI) is a widely used technique for assessing brain function in both healthy and pathological populations. Some factors, such as motion, physiological noise and lesion presence, can contribute to signal change and confound the fMRI data, but fMRI data processing techniques have been developed to correct for these confounding effects. Fifteen spastic subacute stroke patients underwent fMRI while performing a highly controlled task (i.e. passive extension of their affected and unaffected wrists). We investigated the impact on activation maps of lesion masking during preprocessing and first- and second-level analyses, and of adding wrist extension amplitudes and physiological data as regressors using the Statistical Parametric Mapping toolbox (SPM12). We observed a significant decrease in sensorimotor region activation after the addition of lesion masks and movement/physiological regressors during the processing of stroke patients’ fMRI data. Our results demonstrate that:The unified segmentation routine results in good normalization accuracy when dealing with stroke lesions regardless of their size; Adding a group lesion mask during the second-level analysis seems to be a suitable option when none of the patients have lesions in target regions. Otherwise, no masking is acceptable; Movement amplitude is a significant contributor to the sensorimotor activation observed during passive wrist extension in spastic stroke patients; Movement features and physiological noise are relevant factors when interpreting for sensorimotor activation in studies of the motor system in patients with brain lesions. They can be added as nuisance covariates during large patient groups’ analyses.
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Affiliation(s)
- Nabila Brihmat
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France
| | - Kader Boulanouar
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France
| | - Robert Darmana
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France
| | - Arnauld Biganzoli
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France
| | - David Gasq
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France.,University Hospital of Toulouse, Department of Functional & Physiological Explorations, Toulouse, France
| | - Evelyne Castel-Lacanal
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France.,University Hospital of Toulouse, Department of Rehabilitation and Physical Medicine, Toulouse, France
| | - Philippe Marque
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France.,University Hospital of Toulouse, Department of Rehabilitation and Physical Medicine, Toulouse, France
| | - Isabelle Loubinoux
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France
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Henco L, Brandi ML, Lahnakoski JM, Diaconescu AO, Mathys C, Schilbach L. Bayesian modelling captures inter-individual differences in social belief computations in the putamen and insula. Cortex 2020; 131:221-236. [DOI: 10.1016/j.cortex.2020.02.024] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Revised: 12/21/2019] [Accepted: 02/14/2020] [Indexed: 02/07/2023]
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Henco L, Diaconescu AO, Lahnakoski JM, Brandi ML, Hörmann S, Hennings J, Hasan A, Papazova I, Strube W, Bolis D, Schilbach L, Mathys C. Aberrant computational mechanisms of social learning and decision-making in schizophrenia and borderline personality disorder. PLoS Comput Biol 2020; 16:e1008162. [PMID: 32997653 PMCID: PMC7588082 DOI: 10.1371/journal.pcbi.1008162] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 10/26/2020] [Accepted: 07/19/2020] [Indexed: 12/13/2022] Open
Abstract
Psychiatric disorders are ubiquitously characterized by debilitating social impairments. These difficulties are thought to emerge from aberrant social inference. In order to elucidate the underlying computational mechanisms, patients diagnosed with major depressive disorder (N = 29), schizophrenia (N = 31), and borderline personality disorder (N = 31) as well as healthy controls (N = 34) performed a probabilistic reward learning task in which participants could learn from social and non-social information. Patients with schizophrenia and borderline personality disorder performed more poorly on the task than healthy controls and patients with major depressive disorder. Broken down by domain, borderline personality disorder patients performed better in the social compared to the non-social domain. In contrast, controls and major depressive disorder patients showed the opposite pattern and schizophrenia patients showed no difference between domains. In effect, borderline personality disorder patients gave up a possible overall performance advantage by concentrating their learning in the social at the expense of the non-social domain. We used computational modeling to assess learning and decision-making parameters estimated for each participant from their behavior. This enabled additional insights into the underlying learning and decision-making mechanisms. Patients with borderline personality disorder showed slower learning from social and non-social information and an exaggerated sensitivity to changes in environmental volatility, both in the non-social and the social domain, but more so in the latter. Regarding decision-making the modeling revealed that compared to controls and major depression patients, patients with borderline personality disorder and schizophrenia showed a stronger reliance on social relative to non-social information when making choices. Depressed patients did not differ significantly from controls in this respect. Overall, our results are consistent with the notion of a general interpersonal hypersensitivity in borderline personality disorder and schizophrenia based on a shared computational mechanism characterized by an over-reliance on beliefs about others in making decisions and by an exaggerated need to make sense of others during learning specifically in borderline personality disorder. People suffering from psychiatric disorders frequently experience difficulties in social interaction, such as an impaired ability to use social signals to build representations of others and use these to guide behavior. Compuational models of learning and decision-making enable the characterization of individual patterns in learning and decision-making mechanisms that may be disorder-specific or disorder-general. We employed this approach to investigate the behavior of healthy participants and patients diagnosed with depression, schizophrenia, and borderline personality disorder while they performed a probabilistic reward learning task which included a social component. Patients with schizophrenia and borderline personality disorder performed more poorly on the task than controls and depressed patients. In addition, patients with borderline personality disorder concentrated their learning efforts more on the social compared to the non-social information. Computational modeling additionally revealed that borderline personality disorder patients showed a reduced flexibility in the weighting of newly obtained social and non-social information when learning about their predictive value. Instead, we found exaggerated learning of the volatility of social and non-social information. Additionally, we found a pattern shared between patients with borderline personality disorder and schizophrenia who both showed an over-reliance on predictions about social information during decision-making. Our modeling therefore provides a computational account of the exaggerated need to make sense of and rely on one’s interpretation of others’ behavior, which is prominent in both disorders.
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Affiliation(s)
- Lara Henco
- Independent Max Planck Research Group for Social Neuroscience, Max Planck Institute of Psychiatry, Munich, Germany
- Graduate School for Systemic Neurosciences, Munich, Germany
- * E-mail:
| | - Andreea O. Diaconescu
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), University of Toronto, Canada
| | - Juha M. Lahnakoski
- Independent Max Planck Research Group for Social Neuroscience, Max Planck Institute of Psychiatry, Munich, Germany
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Marie-Luise Brandi
- Independent Max Planck Research Group for Social Neuroscience, Max Planck Institute of Psychiatry, Munich, Germany
| | - Sophia Hörmann
- Independent Max Planck Research Group for Social Neuroscience, Max Planck Institute of Psychiatry, Munich, Germany
| | - Johannes Hennings
- Department of Dialectical Behavioral Therapy, kbo-Isar-Amper-Klinikum Munich-East, Munich/Haar, Germany
| | - Alkomiet Hasan
- Department of Psychiatry and Psychotherapy, University Hospital Munich, LMU Munich, Munich, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatic, University of Augsburg, Medical Faculty, Augsburg, Germany
| | - Irina Papazova
- Department of Psychiatry and Psychotherapy, University Hospital Munich, LMU Munich, Munich, Germany
| | - Wolfgang Strube
- Department of Psychiatry and Psychotherapy, University Hospital Munich, LMU Munich, Munich, Germany
| | - Dimitris Bolis
- Independent Max Planck Research Group for Social Neuroscience, Max Planck Institute of Psychiatry, Munich, Germany
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
| | - Leonhard Schilbach
- Independent Max Planck Research Group for Social Neuroscience, Max Planck Institute of Psychiatry, Munich, Germany
- Graduate School for Systemic Neurosciences, Munich, Germany
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
- Medical Faculty, LMU Munich, Munich, Germany
| | - Christoph Mathys
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
- Scuola Internazionale Superiore di Studi Avanzati (SISSA),Trieste, Italy
- Interacting Minds Centre, Aarhus University, Aarhus, Denmark
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Diaconescu AO, Stecy M, Kasper L, Burke CJ, Nagy Z, Mathys C, Tobler PN. Neural arbitration between social and individual learning systems. eLife 2020; 9:54051. [PMID: 32779568 PMCID: PMC7476763 DOI: 10.7554/elife.54051] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Accepted: 08/10/2020] [Indexed: 12/20/2022] Open
Abstract
Decision making requires integrating knowledge gathered from personal experiences with advice from others. The neural underpinnings of the process of arbitrating between information sources has not been fully elucidated. In this study, we formalized arbitration as the relative precision of predictions, afforded by each learning system, using hierarchical Bayesian modeling. In a probabilistic learning task, participants predicted the outcome of a lottery using recommendations from a more informed advisor and/or self-sampled outcomes. Decision confidence, as measured by the number of points participants wagered on their predictions, varied with our definition of arbitration as a ratio of precisions. Functional neuroimaging demonstrated that arbitration signals were independent of decision confidence and involved modality-specific brain regions. Arbitrating in favor of self-gathered information activated the dorsolateral prefrontal cortex and the midbrain, whereas arbitrating in favor of social information engaged the ventromedial prefrontal cortex and the amygdala. These findings indicate that relative precision captures arbitration between social and individual learning systems at both behavioral and neural levels.
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Affiliation(s)
- Andreea Oliviana Diaconescu
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Zurich, Switzerland.,Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich, Zurich, Switzerland.,University of Basel, Department of Psychiatry (UPK), Basel, Switzerland.,Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), University of Toronto, Toronto, Canada
| | - Madeline Stecy
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Zurich, Switzerland.,Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich, Zurich, Switzerland.,Rutgers Robert Wood Johnson Medical School, New Brunswick, United States
| | - Lars Kasper
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Zurich, Switzerland.,Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich, Zurich, Switzerland.,Institute for Biomedical Engineering, MRI Technology Group, ETH Zürich & University of Zurich, Zurich, Switzerland
| | - Christopher J Burke
- Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich, Zurich, Switzerland
| | - Zoltan Nagy
- Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich, Zurich, Switzerland
| | - Christoph Mathys
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Zurich, Switzerland.,Interacting Minds Centre, Aarhus University, Aarhus, Denmark.,Scuola Internazionale Superiore di Studi Avanzati (SISSA), Trieste, Italy
| | - Philippe N Tobler
- Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich, Zurich, Switzerland
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48
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Dopamine manipulations modulate paranoid social inferences in healthy people. Transl Psychiatry 2020; 10:214. [PMID: 32624569 PMCID: PMC7335741 DOI: 10.1038/s41398-020-00912-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 06/11/2020] [Accepted: 06/23/2020] [Indexed: 12/14/2022] Open
Abstract
Altered dopamine transmission is thought to influence the formation of persecutory delusions. However, despite extensive evidence from clinical studies there is little experimental evidence on how modulating the dopamine system changes social attributions related to paranoia, and the salience of beliefs more generally. Twenty seven healthy male participants received 150mg L-DOPA, 3 mg haloperidol, or placebo in a double-blind, randomised, placebo-controlled study, over three within-subject sessions. Participants completed a multi-round Dictator Game modified to measure social attributions, and a measure of belief salience spanning themes of politics, religion, science, morality, and the paranormal. We preregistered predictions that altering dopamine function would affect (i) attributions of harmful intent and (ii) salience of paranormal beliefs. As predicted, haloperidol reduced attributions of harmful intent across all conditions compared to placebo. L-DOPA reduced attributions of harmful intent in fair conditions compared to placebo. Unexpectedly, haloperidol increased attributions of self-interest about opponents' decisions. There was no change in belief salience within any theme. These results could not be explained by scepticism or subjective mood. Our findings demonstrate the selective involvement of dopamine in social inferences related to paranoia in healthy individuals.
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Smith R, Steklis HD, Steklis NG, Weihs KL, Lane RD. The evolution and development of the uniquely human capacity for emotional awareness: A synthesis of comparative anatomical, cognitive, neurocomputational, and evolutionary psychological perspectives. Biol Psychol 2020; 154:107925. [DOI: 10.1016/j.biopsycho.2020.107925] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 06/17/2020] [Accepted: 06/23/2020] [Indexed: 01/09/2023]
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50
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Ketamine Affects Prediction Errors about Statistical Regularities: A Computational Single-Trial Analysis of the Mismatch Negativity. J Neurosci 2020; 40:5658-5668. [PMID: 32561673 DOI: 10.1523/jneurosci.3069-19.2020] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 05/12/2020] [Accepted: 06/05/2020] [Indexed: 12/12/2022] Open
Abstract
The auditory mismatch negativity (MMN) is significantly reduced in schizophrenia. Notably, a similar MMN reduction can be achieved with NMDA receptor (NMDAR) antagonists. Both phenomena have been interpreted as reflecting an impairment of predictive coding or, more generally, the "Bayesian brain" notion that the brain continuously updates a hierarchical model to infer the causes of its sensory inputs. Specifically, neurobiological interpretations of predictive coding view perceptual inference as an NMDAR-dependent process of minimizing hierarchical precision-weighted prediction errors (PEs), and disturbances of this putative process play a key role in hierarchical Bayesian theories of schizophrenia. Here, we provide empirical evidence for this theory, demonstrating the existence of multiple, hierarchically related PEs in a "roving MMN" paradigm. We applied a hierarchical Bayesian model to single-trial EEG data from healthy human volunteers of either sex who received the NMDAR antagonist S-ketamine in a placebo-controlled, double-blind, within-subject fashion. Using an unrestricted analysis of the entire time-sensor space, our trial-by-trial analysis indicated that low-level PEs (about stimulus transitions) are expressed early (102-207 ms poststimulus), while high-level PEs (about transition probability) are reflected by later components (152-199 and 215-277 ms) of single-trial responses. Furthermore, we find that ketamine significantly diminished the expression of high-level PE responses, implying that NMDAR antagonism disrupts the inference on abstract statistical regularities. Our findings suggest that NMDAR dysfunction impairs hierarchical Bayesian inference about the world's statistical structure. Beyond the relevance of this finding for schizophrenia, our results illustrate the potential of computational single-trial analyses for assessing potential pathophysiological mechanisms.
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