Paradigms for probing socio-affective inference with blunted affect – preliminary pilot results
Poster No:
560
Submission Type:
Abstract Submission
Authors:
Katharina Wellstein1, Peter Thestrup Waade2, Richa Phogat1, Renate Thienel1, Jayson Jeganathan1, Bryan Paton1, Michael Breakspear1
Institutions:
1The University of Newcastle, New Lambton Heights, NSW, 2Aarhus University, Aarhus, Aarhus
First Author:
Co-Author(s):
Introduction:
The heterogeneity problem in psychiatry is well understood (Feczo et al., 2019). Testable theories and computational modeling have been suggested as possible solutions to the problem (Huys et al., 2016). While neuroimaging and computational mechanistic investigations have improved our understanding of positive symptoms (Adams et al., 2013), negative symptoms remain poorly understood. A recent proposed mechanistic hypothesis about blunted affect suggests a central role of altered learning about other people's affective reactions to one's own affect (Jeganathan & Breakspear, 2021). That is, when individuals perceive that their smiles are not met by others smiling back, socio-affective prediction errors (saPEs) arise which may feel more distressing to some individuals who may avoid saPEs by blunting their affect. We designed an fMRI study to test this mechanistic hypothesis in persons with psychosis and people from the general population with high and low constricted affect scores (Raine, 1991). We designed two trial-by-trial PE-learning tasks: (1) The SAP (Social Affective Prediction) task which operationalizes saPE-learning and (2) the SAPC (SAP Control) task that captures PE-learning in a different context.
In a first pilot study we test whether saPE learning in the SAP task is distinguishable from non-social and non-affective PE learning. This is crucial to show specific neural saPE signatures in our upcoming fMRI study.
In a first pilot study we test whether saPE learning in the SAP task is distinguishable from non-social and non-affective PE learning. This is crucial to show specific neural saPE signatures in our upcoming fMRI study.
Methods:
In the SAP task participants predict whether they will receive a smile from three faces on 120 trials and in the SAPC task they predict whether eggs will spoil or not. The outcome sequence of both tasks is the same and was optimized regarding parameter recoverability based on simulations with a trial-by-trial PE learning model, the enhanced binary Hierarchical Gaussian Filter (Mathys et al., 2014). The eHGF captures how volatility influences perception and hierarchical PE updating according to predictive coding principles, which is what our original hypotheses are based on (Figure 1).
To test for learning differences and similarities between tasks, we are conducting a behavioural pilot study (N=5). Participants played both tasks in counterbalanced order. Their responses were inverted with a 2-Level (eHGF2), 3-Level eHGF (eHGF3), and a Rescola Wagner (RW). Maximum A-Posteriori (MAP) estimates of the following parameters were extracted: ω_(2,eHGF2), ω_(2,eHGF3), ω_(3,eHGF3), and α_RW.
To test for learning differences and similarities between tasks, we are conducting a behavioural pilot study (N=5). Participants played both tasks in counterbalanced order. Their responses were inverted with a 2-Level (eHGF2), 3-Level eHGF (eHGF3), and a Rescola Wagner (RW). Maximum A-Posteriori (MAP) estimates of the following parameters were extracted: ω_(2,eHGF2), ω_(2,eHGF3), ω_(3,eHGF3), and α_RW.
Results:
We conducted Bayesian paired samples t-tests in JASP (JASP, 2023) for each one of the predefined MAP estimates. For both eHGF models' 2nd-level learning rate (ω_2) there is more evidence for a task effect vs. no task effect. For ω_(3,eHGF3) and α_RW we found evidence for the absence of a task effect (Figure 2).
Conclusions:
Given the small preliminary sample, the reported Bayes Factors only provide anecdotal evidence. Additional data is being collected to identify the parameters that differentiate and those that capture similarities of learning in the two tasks
If the evidence will accumulate in favour of the here identified effects with more data, we could show that learning with the eHGF can distinguish between learning in the two tasks. These differences in ω_2 suggest that participants weigh sensory input (or bottom-up information) differently in a social vs. as non-social context. If the evidence for the absence of a task difference should accumulate with more data too for parameter ω_(3,eHGF3), we would be able to show that meta-volatility (participants' beliefs about how fast the environment is changing) may be governed more by the stimulus-outcome sequence than the content of the task.
These results will inform our fMRI study, suggesting that we should be able to identify differences in learning that may be specific to socio-emotional processes in individuals with constricted affect vs. individuals with no constricted affect as well as differences in general learning patterns that are not specific to the socio-affective task context.
If the evidence will accumulate in favour of the here identified effects with more data, we could show that learning with the eHGF can distinguish between learning in the two tasks. These differences in ω_2 suggest that participants weigh sensory input (or bottom-up information) differently in a social vs. as non-social context. If the evidence for the absence of a task difference should accumulate with more data too for parameter ω_(3,eHGF3), we would be able to show that meta-volatility (participants' beliefs about how fast the environment is changing) may be governed more by the stimulus-outcome sequence than the content of the task.
These results will inform our fMRI study, suggesting that we should be able to identify differences in learning that may be specific to socio-emotional processes in individuals with constricted affect vs. individuals with no constricted affect as well as differences in general learning patterns that are not specific to the socio-affective task context.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Emotion, Motivation and Social Neuroscience:
Emotional Perception
Social Neuroscience Other 2
Higher Cognitive Functions:
Decision Making
Modeling and Analysis Methods:
Bayesian Modeling
Keywords:
Computational Neuroscience
Emotions
Experimental Design
FUNCTIONAL MRI
Schizophrenia
Social Interactions
Other - Computational Modeling
1|2Indicates the priority used for review
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Provide references using APA citation style.
Feczo, E., et al. (2019). The Heterogeneity Problem: Approaches to Identify Psychiatric Subtypes | Elsevier Enhanced Reader. Trends in Cognitive Sciences, 23(7), 584–601.
Huys, Q. J. M., et al. (2016). Computational psychiatry as a bridge from neuroscience to clinical applications. Nature Neuroscience, 19(3), Article 3.
JASP, T. (2023). JASP (Version 0.18.1) [Intel]. University of Amsterdam.
Jeganathan, J., et al. (2021). An active inference perspective on the negative symptoms of schizophrenia. The Lancet Psychiatry, 8(8), 732–738.
Mathys, C. D., et al. (2014). Uncertainty in perception and the Hierarchical Gaussian Filter. Frontiers in Human Neuroscience, 8.
Raine, A. (1991). The SPQ: A Scale for the Assessment of Schizotypal Personality Based on DSM-III-R Criteria. Schizophrenia Bulletin, 17(4), 555–564.