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

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.