Inducing Affective Learning Biases with Cognitive Training and Prefrontal tDCS: A Proof-of-Concept Study

Effects of tDCS on emotion recognition and brain oscillations

INTRODUCTION

Emotion recognition, the ability to interpret the emotional state of individuals by looking at their facial expressions, is essential for healthy social interactions and communication. There is limited research on the effects of tDCS on emotion recognition in the literature. This study aimed to investigate the effects of anodal stimulation of the ventromedial prefrontal cortex (vmPFC), a key region for emotion recognition from facial expressions, on emotion recognition and brain oscillations.

METHOD

A single-blind randomized-controlled study was conducted with 54 healthy participants. Before and after brain stimulation emotion recognition tasks were administered and resting-state EEG were recorded. The changes in task performances and brain oscillations were analyzed using repeated-measures two-way ANOVA analysis.

RESULTS

There was no significant difference in the emotion recognition tasks between groups in pre-post measurements. The changes in delta, theta, alpha, beta and gamma frequency bands in the frontal, temporal, and posterio-occipital regions, which were determined as regions of interest in resting state EEG data before and after tDCS, were compared between groups. The results showed that there was a significant difference between groups only in delta frequency before and after tDCS in the frontal and temporal regions. While an increase in delta activity was observed in the experimental group in the frontal and temporal regions, a decrease was observed in the control group.

CONCLUSIONS

The tDCS may not have improved emotion recognition because it may not have had the desired effect on the vmPFC, which is in the lower part of the prefrontal lobe. The changes in EEG frequencies observed section tDCS may be similar to those seen in some pathological processes, which could explain the lack of improvement in emotion recognition. Future studies to be carried out for better understand this effect are important.

Changes in Learning From Social Feedback After Web-Based Interpretation Bias Modification: Secondary Analysis of a Digital Mental Health Intervention Among Individuals With High Social Anxiety Symptoms

BACKGROUND

Biases in social reinforcement learning, or the process of learning to predict and optimize behavior based on rewards and punishments in the social environment, may underlie and maintain some negative cognitive biases that are characteristic of social anxiety. However, little is known about how cognitive and behavioral interventions may change social reinforcement learning in individuals who are anxious.

OBJECTIVE

This study assessed whether a scalable, web-based cognitive bias modification for interpretations (CBM-I) intervention changed social reinforcement learning biases in participants with high social anxiety symptoms. This study focused on 2 types of social reinforcement learning relevant to social anxiety: learning about other people and learning about one's own social performance.

METHODS

Participants (N=106) completed 2 laboratory sessions, separated by 5 weeks of ecological momentary assessment tracking emotion regulation strategy use and affect. Approximately half (n=51, 48.1%) of the participants completed up to 6 brief daily sessions of CBM-I in week 3. Participants completed a task that assessed social reinforcement learning about other people in both laboratory sessions and a task that assessed social reinforcement learning about one's own social performance in the second session. Behavioral data from these tasks were computationally modeled using Q-learning and analyzed using mixed effects models.

RESULTS

After the CBM-I intervention, participants updated their beliefs about others more slowly (P=.04; Cohen d=-0.29) but used what they learned to make more accurate decisions (P=.005; Cohen d=0.20), choosing rewarding faces more frequently. These effects were not observed among participants who did not complete the CBM-I intervention. Participants who completed the CBM-I intervention also showed less-biased updating about their social performance than participants who did not complete the CBM-I intervention, learning similarly from positive and negative feedback and from feedback on items related to poor versus good social performance. Regardless of the intervention condition, participants at session 2 versus session 1 updated their expectancies about others more from rewarding (P=.003; Cohen d=0.43) and less from punishing outcomes (P=.001; Cohen d=-0.47), and they became more accurate at learning to avoid punishing faces (P=.001; Cohen d=0.20).

CONCLUSIONS

Taken together, our results provide initial evidence that there may be some beneficial effects of both the CBM-I intervention and self-tracking of emotion regulation on social reinforcement learning in individuals who are socially anxious, although replication will be important.

Transdiagnostic computations of uncertainty: towards a new lens on intolerance of uncertainty

People radically differ in how they cope with uncertainty. Clinical researchers describe a dispositional characteristic known as "intolerance of uncertainty", a tendency to find uncertainty aversive, reported to be elevated across psychiatric and neurodevelopmental conditions. Concurrently, recent research in computational psychiatry has leveraged theoretical work to characterise individual differences in uncertainty processing. Under this framework, differences in how people estimate different forms of uncertainty can contribute to mental health difficulties. In this review, we briefly outline the concept of intolerance of uncertainty within its clinical context, and we argue that the mechanisms underlying this construct may be further elucidated through modelling how individuals make inferences about uncertainty. We will review the evidence linking psychopathology to different computationally specified forms of uncertainty and consider how these findings might suggest distinct mechanistic routes towards intolerance of uncertainty. We also discuss the implications of this computational approach for behavioural and pharmacological interventions, as well as the importance of different cognitive domains and subjective experiences in studying uncertainty processing.