November 3-8, 2019
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Introduction to the 苹果淫院-FPR Culture, Mind & Brain Program - Laurence Kirmayer
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Toward a Social-Cultural Computational Psychiatry: Challenges and Prospects - Maxwell Ramstead
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H4, Hyperscanning: History, Hypes and Hopes - Guillaume Dumas
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Neural Coupling During Cooperation as a Biomarker for Human Social Contact - Edda Bilek
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Dynamic Causal Modelling: Tutorial and First Results for Multi-Brain Data - Edda Bilek
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Emotion Cognition Interactions as Deep Active Inference - Ryan Smith
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Expanding Outcome Measures in Schizophrenia Research: Does RDoC Pose a Threat? - Phoebe Friesen
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Open Science: From Open Knowledge to Open Washing - Guillaume Dumas
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Deeply Felt Affect: Understanding Emotions Through Deep Active Inference - Casper Hesp
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Introduction to the 苹果淫院-FPR Culture, Mind & Brain Program -听Laurence Kirmayer
Social and cultural neuroscience has provided new insights into the mechanisms and meanings of human cognition and adaptation. This introduction will outline the workshop and consider the conceptual and methodological challenges of building bridges between the social sciences and neurosciences. Topics will include: the relevance of social science for neuroscience; implications of 4-E cognitive science for social and cultural neuroscience; ecosocial approaches to studying the brain in health and illness; and strategies for integrating ethnographic methods and neuroscience in global mental health.
References
Choudhury, S., & Kirmayer, L. J. (2009). Cultural neuroscience and psychopathology: Prospects for cultural psychiatry. Progress in brain research, 178, 263-283.
(09)17820-2
Kirmayer, L. J. (2012). The future of critical neuroscience. In S. Choudhury & J. Slaby (Eds.), Critical neuroscience. A handbook of the social and cultural contexts of neuroscience (pp. 367-383) Chichester, England: Wiley-Blackwell.
Kirmayer, L. J., & Crafa, D. (2014). What kind of science for psychiatry?. Frontiers in human neuroscience, 8, 435.
Kirmayer, L. J., & Gold, I. (2012). Re-socializing psychiatry. In S. Choudhury & J. Slaby (Eds.) Critical neuroscience. A handbook of the social and cultural contexts of neuroscience (pp. 307-330) Chichester, England: Wiley-Blackwell.
Ramstead, M. J., Veissi猫re, S. P., & Kirmayer, L. J. (2016). Cultural affordances: scaffolding local worlds through shared intentionality and regimes of attention. Frontiers in psychology, 7, 1090.
Seligman, R., Choudhury, S., & Kirmayer, L. J. (2016). Locating culture in the brain and in the world: from social categories to the ecology of mind. In J. Y. Chiao, S, C, Li, & R. Seligman (Eds.), The Oxford handbook of cultural neuroscience (pp. 3-20). Oxford, England: Oxford University Press.
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Toward a Social-Cultural Computational Psychiatry: Challenges and Prospects -听Maxwell听Ramstead
This brief presentation will situate our activities for the week in the context of the Social-Cultural Computational Psychiatry and Neuroscience Network. I will discuss the inception of the project, goals for workshop, and the structure and process of the workshop.
References
Friston, K. J., Stephan, K. E., Montague, R., & Dolan, R. J. (2014). Computational psychiatry: the brain as a phantastic organ. The Lancet psychiatry, 1(2), 148-158.
Friston, K. J., Redish, A. D., & Gordon, J. A. (2017). Computational nosology and precision psychiatry. Computational psychiatry, 1, 2-23.
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I Interact Therefore I Am -听Dimitris Bolis
In this talk, we draw on dialectics and Bayesian accounts of cognition, suggesting that a fine-grained analysis of the multiscale dynamics of social interaction might allow us to reconsider the self beyond the static individual, that is how it emerges and manifests itself in sociocultural relations. In this light, we put forward the dialectical mis-attunement hypothesis, which views various psychiatric conditions, such as autism, not as (disordered) function within single brains but rather as a dynamic interpersonal mismatch. To operationalize our suggestion, we present two-person psychophysiology and multilevel analysis of intersubjectivity, which by virtue of measuring and modeling social interaction, allow us to move beyond the individual in neuropsychiatry. In brief, our current results indicate that in real-time social interactions humans highly align with each other across multiple time scales and levels of description, from decision-making and gaze behavior to facial expressions and metacognition. Interestingly, such an alignment is most prominent in collaborative contexts and between people who have a bond of mutual affection.
References
Bolis, D., & Schilbach, L. (2018). 鈥業 interact therefore I am鈥�: The self as a historical product of dialectical attunement. Topoi, 1-14.
Bolis, D., Balsters, J., Wenderoth, N., Becchio, C., & Schilbach, L. (2017). Beyond autism: introducing the dialectical misattunement hypothesis and a bayesian account of intersubjectivity. Psychopathology, 50(6), 355-372.
Bolis, D., & Schilbach, L. (2017). Beyond one Bayesian brain: Modeling intra-and inter-personal processes during social interaction: Commentary on 鈥淢entalizing homeostasis: The social origins of interoceptive inference鈥� by Fotopoulou & Tsakiris. Neuropsychoanalysis, 19(1), 35-38.
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H4, Hyperscanning: History, Hypes and Hopes -听Guillaume Dumas
Progress in neuroimaging has allowed social neuroscientists to capture brain activity of multiple persons engaged in real-time interactions. This method was termed originally 鈥淗yperscan鈥� because it was introduced as magnetic resonance scanners synchronized through the internet. Quickly, it expanded to other modalities such as EEG or fNIRS. Those new types of recordings have uncovered neural correlates of social interaction, including inter-brain synchronizations, during multiple social tasks, even beyond dyadic context. We will cover some methodological aspects, including the specificity of experimental protocols, signal processing and statistical analyses. We will highlight some of the key results brought by the hyperscanning community. Finally, we will discuss the future of this experimental paradigm.
References
Montague, P. R., Berns, G. S., Cohen, J. D., McClure, S. M., Pagnoni, G., Dhamala, M., 鈥� Fisher, R. E. (2002). Hyperscanning: Simultaneous fMRI during Linked Social Interactions. NeuroImage, 16(4), 1159鈥�1164. .
Barraza, P., Dumas, G., Liu, H., Blanco-Gomez, G., van den Heuvel, M. I., Baart, M., & P茅rez, A. (2019). Implementing EEG hyperscanning setups. MethodsX, 6, 428-436. .
Dumas, G., Lachat, F., Martinerie, J., Nadel, J., & George, N. (2011). From social behaviour to brain synchronization: Review and perspectives in hyperscanning. IRBM, 32(1), 48鈥�53.
Babiloni, F., & Astolfi, L. (2012). Social neuroscience and hyperscanning techniques: Past, present and future. Neuroscience and biobehavioral reviews, 44, 1鈥�18. .
Liu, D., Liu, S., Liu, X., Zhang, C., Li, A., Jin, C., 鈥� Zhang, X. (2018). Interactive brain activity: Review and progress on EEG-based hyperscanning in social interactions. Frontiers in psychology, 9, 1862. .
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Neural Coupling During Cooperation as a Biomarker for Human Social Contact -听Edda Bilek
In this talk, I will present prior work on fMRI Hyperscanning in cooperative scenarios. We will cover how interaction specific cross-brain neural synchrony (neural coupling) can be assessed and validated. Furthermore, results from clinical studies involving patients with a social interaction disorder (Borderline Personality Disorder) will be presented.
References
Bilek, E., Ruf, M., Sch盲fer, A., Akdeniz, C., Calhoun, V. D., Schmahl, C., ... & Meyer-Lindenberg, A. (2015). Information flow between interacting human brains: Identification, validation, and relationship to social expertise.Proceedings of the National Academy of Sciences, 112(16), 5207-5212.
Bilek, E., St枚脽el, G., Sch盲fer, A., Clement, L., Ruf, M., Robnik, L., ... & Meyer-Lindenberg, A. (2017). State-dependent cross-brain information flow in Borderline Personality Disorder. JAMA psychiatry, 74(9), 949-957.
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Dynamic Causal Modelling: Tutorial and First Results for Multi-Brain Data -听Edda Bilek
This tutorial session introduces dynamic causal modelling (DCM) for the analysis of (multi-brain) neuroimaging data of any modality. Theoretical background as well as practical application to task data will be covered. Finally, recent results from the use of DCM for fMRI-hyperscanning data will be examined and discussed.
References
Zeidman, P., Jafarian, A., Corbin, N., Seghier, M. L., Razi, A., Price, C. J., & Friston, K. J. (2019). A guide to group effective connectivity analysis, part 1: First level analysis with DCM for fMRI. NeuroImage, 200.
Zeidman, P., Jafarian, A., Corbin, N., Seghier, M. L., Razi, A., Price, C. J., & Friston, K. J. (2019). A guide to group effective connectivity analysis, part 2: Second level analysis with PEB . NeuroImage, 200.
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Emotion Cognition Interactions as Deep Active Inference -听Ryan Smith
Individuals are known to differ in their ability to understand or be aware of their own emotions. Low emotional awareness (EA) is also a known correlate and risk factor for the development of a range of psychiatric disorders and co-morbid medical conditions. According to the 鈥渢hree-process model鈥� of affective processing, low EA may follow from maladaptive functioning in the way affective bodily responses are generated, the way they are subsequently represented/conceptualized, and/or in the way top-down attentional processes are applied to one鈥檚 own emotions. Previous neuroimaging work has linked individual differences in EA to structural and functional differences in large-scale neural networks subserving somatovisceral regulation, interoception, conceptualization, and cognitive control. In this talk, I will briefly introduce the three-process model. I will then describe how this model can be understood in computational terms within the active inference framework. Specifically, I describe a deep (active) inference model that reproduces the cognitive-emotional processes and self-report behaviors associated with EA. I will then present simulations to illustrate (seven) distinct mechanisms that (either alone or in combination) can produce phenomena 鈥� such as somatic misattribution, coarse-grained emotion conceptualization, and constrained reflective capacity 鈥� characteristic of low EA. These simulations suggest that the clinical phenotype of impoverished EA can be reproduced by dissociable computational processes. The possibility that different processes are at work in different individuals suggests that they may benefit from distinct clinical interventions. This may be a useful step toward identifying which processes operate in different individuals 鈥� and providing a principled basis for personalized precision medicine.
References
Smith, R., Lane, R. D., Parr, T., & Friston, K. J. (2019). Neurocomputational mechanisms underlying emotional awareness: insights afforded by deep active inference and their potential clinical relevance. Neuroscience & biobehavioral reviews.
Smith, R., Killgore, W. D., & Lane, R. D. (2018). The structure of emotional experience and its relation to trait emotional awareness: A theoretical review. Emotion, 18(5), 670.
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Expanding Outcome Measures in Schizophrenia Research: Does RDoC Pose a Threat? -听Phoebe Friesen
This presentation examines two significant shifts that have been taking place within the field of psychiatry, and asks whether they are moving in compatible directions or not. The first shift is taking place within psychiatric research as a result of the National Institute of Mental Health鈥檚 rejection of the Diagnostic and Statistical Manual of Mental Disorders criteria in favor of the newly developed Research Domain Criteria (RDoC) framework. The second shift involves the adoption of wider outcome measures related to recovery and quality of life (QOL) within schizophrenia research in place of narrow measures such as symptom scales. It is argued that this second shift has been successful in that it has brought several explanatory models into light that were previously difficult to see and that are likely to bear fruit in terms of both understanding schizophrenia and developing tools and treatments for those living with a diagnosis of schizophrenia. In light of this, the question of whether the shift to RDoC will threaten these gains is considered. In response, it is suggested that although there are several reasons to think that the first shift may threaten the knowledge gained by the second shift, there is also reason to be hopeful.
References
Friesen, P. (2019). Expanding outcome measures in schizophrenia research: Does the Research Domain Criteria pose a threat?. Philosophy, Psychiatry, & Psychology, 26(3), 243-260.
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Open Science: From Open Knowledge to Open Washing -听Guillaume Dumas
This talk will explain how open science constitute a complex social phenomenon where multiple perspective of 鈥渙penness鈥� collide. We will explain the premises of this movement, describe some of its core values, and present representative initiatives. Finally, we will discuss the potential pitfalls and the 鈥渙pen washing鈥� phenomenon.
References
Contextualizing Openness: Situating Open Science
Open Science MOOC
Center for Open Science
HackYourPhD
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Modeling Task Behavior with Active Inference 鈥� Ryan Smith
Recently, active inference models have begun to be used to estimate computational processing differences between individuals based on behavior. This approach offers promise in computational psychiatry, where identifying mechanistic differences in clinical populations could guide treatment selection and treatment development. In this presentation, I will first introduce some behavioral tasks, associated computational models, and preliminary empirical results. I will then provide a hands-on demonstration of how to build these task-specific models within Matlab using SPM12.
References
Schwartenbeck, Philipp, et al. (2014). The dopaminergic midbrain encodes the expected certainty about desired outcomes. Cerebral cortex, 25(10), 3434-3445.
Smith, Ryan, et al. (2019). Sensitivity to expected negative outcomes during approach-avoidance conflict in a trans-diagnostic patient sample: A computational (active inference) modeling approach. OSF preprints.
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Deeply Felt Affect: Understanding Emotions Through Deep Active Inference -听Casper听Hesp
Ever wondered how we conduct those fancy active inference simulations? Good news: you can learn to do it yourself with as much as zero modelling experience. During this tutorial, you will learn how construct your very own active-inference agent in SPM12, minimising free energy and all 鈥� in a fictional environment of your choice! The only requirements for this workshop will be (1) your laptop with MATLAB installed and (2) a functional set of brains with a corresponding biomechanical suit. All the free-energy gradients will be doing their work behind the scenes, and no numbers will be crunched beyond filling in some innocent tables. Together, we will work through a step-by-step explanation, accompanied with necessary tips and tricks for quick and painless implementation.
References
Friston, K., FitzGerald, T., Rigoli, F., Schwartenbeck, P., O Doherty, J., & Pezzulo, G. (2016). Active inference and learning. Neuroscience and biobehavioral reviews, 68, 862鈥�879. doi:10.1016/j.neubiorev.2016.06.022.