Here, we take a computational approach to understand the mechanisms underlying face perception biases in depression. Thirty participants diagnosed with Major Depressive Disorder and thirty healthy control participants took part in three studies involving recognition of identity and emotion in faces. We used signal detection theory 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. Our use of highly-controlled face stimuli ensures that such asymmetry is truly perceptual in nature, rather than the result of studying expressions with inherently di↵erent discriminability. We found no systematic e↵ect of depression on the perceptual interactions between face expression and identity, suggesting that depression is not associated with di culty attending to one of these dimensions while filtering out the other. We show through simulation that the overall pattern of results, as well as other biases found in the literature, can be explained by a neurocomputational model in which neural populations encoding positive expressions are selectively suppressed.

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The primary goal in this work is to develop a dynamical model capturing the influence of seasonal and latitudinal variations on the expression of Drosophila clock genes. To this end, we study a specific dynamical system with strange attractors that exhibit changes of Drosophila activity in a range of latitudes and across different seasons. Bifurcations of this system are analyzed to peruse the effect of season and latitude on the behavior of clock genes. Existing experimental data collected from the activity of Drosophila melanogaster corroborate the dynamical model.

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