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Using observer models to formalize the mechanisms underlying face perception biases in depression

Fabian Soto
Florida International University ~ Department of Psychology
Mr. Jason Hays
Florida International University ~ Psychology
Dr. Christopher Beevers
University of Texas at Austin ~ Psychology

Here, we take a computational approach to understand the mechanisms underlying face perception biases in depression. Participants diagnosed with Major Depressive Disorder (MDD, N=30) and healthy controls (N=30) took part in a study involving recognition of identity and emotion in faces. We used signal detection theory (SDT) to determine whether any perceptual biases exist in depression aside from decisional biases. We found lower sensitivity to happiness in general, and lower sensitivity to both happiness and sadness with ambiguous stimuli. We found no systematic effect of depression on the perceptual interactions between face expression and identity, suggesting that depression is not associated with difficulty selectively attending to one of these dimensions. Our use of SDT allows us to link these psychophysical results to an neurocomputational model of the encoding of facial expression. We show through simulation that the overall pattern of results, as well as other biases found in the literature, can be explained by selective suppression of neural populations encoding positive expressions in MDD. In a second study, we used reverse correlation to show that one source of this suppression could be a difference between participants diagnosed with MDD and healthy controls in the information sampled in order to detect happiness and sadness in faces. We show that the psychophysical observer models obtained through reverse correlation offer a complementary way to account for the results of our first study. Our model-based approach is a step forward toward understanding the mechanisms underlying face perception biases in psychiatric disorders.



signal detection theory
neural encoding
face perception
observer model
fascinating work! Last updated 2 years ago

Since I am also working on the mechanisms of depression, I just want to say that I really loved this work, and the combination of the filtering analysis and SDT. Did you also look at differences in individual's SDT parameters compared to their filters? (not sure whether that would be possible, but that would be really interesting).

Dr. Marieke Van Vugt 1 comment
Cite this as:

Soto, F., Hays, J., & Beevers, C. (2021, July). Using observer models to formalize the mechanisms underlying face perception biases in depression. Paper presented at Virtual MathPsych/ICCM 2021. Via