Training reduces error in rating the intensity of emotions

A Computational Model Reveals Learning Dynamics During Interpretation Bias Training With Clinical Applications

BACKGROUND

Some psychopathologies, including anxiety and irritability, are associated with biases when judging ambiguous social stimuli. Interventions targeting these biases, or interpretation bias training (IBT), are amenable to computational modeling to describe their associative learning mechanisms. Here, we translated ALCOVE (attention learning covering map), a model of category learning, to describe learning in youths with affective psychopathology when training on more positive judgments of ambiguous face emotions.

METHODS

A predominantly clinical sample comprised 71 youths (age range, 8-22 years) representing broad distributions of irritability and anxiety symptoms. Of these, 63 youths were included in the test sample by completing an IBT task with acceptable performance for computational modeling. We used a separate sample of 28 youths to translate ALCOVE for individual estimates of learning rate and generalization. In the test sample, we assessed associations between model learning estimates and irritability, anxiety, their shared variance (negative affectivity), and age.

RESULTS

Age and affective symptoms were associated with category learning during IBT. Lower learning rates were associated with higher negative affectivity common in anxiety and irritability. Lower generalization, or improved discrimination between face emotions, was associated with increasing age.

CONCLUSIONS

This work demonstrates a functional consequence of age- and symptom-related learning during interpretation bias. Learning measured by ALCOVE also revealed learning types not accounted for in the prior literature on IBT. This work more broadly demonstrates the utility of measurement models for understanding trial-by-trial processes and identifying individual learning styles.

A Randomized Controlled Trial of Computerized Interpretation Bias Training for Disruptive Mood Dysregulation Disorder: A Fast-Fail Study

OBJECTIVE

To examine targeted, mechanism-based interventions is the next generation of treatment innovation. Biased threat labeling of ambiguous face emotions (interpretation bias) is a potential behavioral treatment target for anger, aggression, and irritability. Changing biases in face-emotion labeling may improve irritability-related outcomes. Here, we report the first randomized, double-blind, placebo-controlled targeted trial of interpretation bias training (IBT) in youths with chronic, severe irritability.

METHOD

Patients with current disruptive mood dysregulation disorder (DMDD; N = 44) were randomly assigned to complete 4 sessions of active (n = 22) or sham (n = 22) computerized IBT training within a 1-week period. The first and last trainings were completed onsite, and 2 trainings were completed at home. We examined the effects of active IBT on labeling bias, primary outcome measures of irritability, and secondary outcome measures of anxiety, depression, and functional impairment. Follow-up assessments were completed immediately after the intervention as well as 1 and 2 weeks later.

RESULTS

We found that active IBT engaged the behavioral target in the active relative to the sham condition, as shown by a significant shift toward labeling ambiguous faces as happy. However, there was no consistent clinical improvement in active IBT relative to the sham condition either immediately after or 2 weeks after training in either the primary or secondary outcome measures.

CONCLUSION

Although this randomized controlled trial of IBT in youths with DMDD engaged the proposed behavioral target, there was no statistically significant improvement on clinical outcome. Identifying and changing behavioral targets is a first step in novel treatment development; these results have broader implications for target-based intervention development.

CLINICAL TRIAL REGISTRATION INFORMATION

Psychological Treatments for Youth With Severe Irritability; https://clinicaltrials.gov/; NCT02531893.

Categorization of Vocal Emotion Cues Depends on Distributions of Input

Learners use the distributional properties of stimuli to identify environmentally relevant categories in a range of perceptual domains, including words, shapes, faces, and colors. We examined whether similar processes may also operate on affective information conveyed through the voice. In Experiment 1, we tested how adults (18–22-year-olds) and children (8–10-year-olds) categorized affective states communicated by vocalizations varying continuously from “calm” to “upset.” We found that the threshold for categorizing both verbal (i.e., spoken word) and nonverbal (i.e., a yell) vocalizations as “upset” depended on the statistical distribution of the stimuli participants encountered. In Experiment 2, we replicated and extended these findings in adults using vocalizations that conveyed multiple negative affect states. These results suggest perceivers’ flexibly and rapidly update their interpretation of affective vocal cues based upon context.

The Development of Emotion Reasoning in Infancy and Early Childhood

Historically, research characterizing the development of emotion recognition has focused on identifying specific skills and the age periods, or milestones, at which these abilities emerge. However, advances in emotion research raise questions about whether this conceptualization accurately reflects how children learn about, understand, and respond to others’ emotions in everyday life. In this review, we propose a developmental framework for the emergence of emotion reasoning—that is, how children develop the ability to make reasonably accurate inferences and predictions about the emotion states of other people. We describe how this framework holds promise for building upon extant research. Our review suggests that use of the term emotion recognition can be misleading and imprecise, with the developmental processes of interest better characterized by the term emotion reasoning. We also highlight how the age at which children succeed on many tasks reflects myriad developmental processes. This new framing of emotional development can open new lines of inquiry about how humans learn to navigate their social worlds.