Depression is associated with biased facial emotion processing, yet existing paradigms measure only the recognition of pre-selected stimuli, conflating the process of forming an internal representation with its endpoint product. Here, we used a genetic-algorithm (GA) face synthesis task to disentangle these components. Fifty-seven undergraduates (40 Low Risk, 17 At Risk by Beck Depression Inventory-II) iteratively evolved photorealistic 3D faces to match their internal representations of 13 emotions across seven generations in a 199-dimensional face shape space. We extracted five evolutionary metrics capturing how participants constructed their representations (convergence speed, velocity, stability, range) and the structure they ultimately produced (peak intensity). At Risk participants converged on embarrassment representations significantly faster than Low Risk participants (d = 0.88, p = .001, FDR-corrected, permutation-validated), reaching their template in roughly half the number of generations. Anger representations showed greater evolutionary instability in the At Risk group (d = -0.75, p = .029, permutation-validated). Critically, endpoint face intensities did not differ between groups for any emotion. These results suggest that depression severity is associated with rigid self-conscious emotion schemas and unstable anger representations during face generation, reflecting altered cognitive processes rather than distorted perceptual products. The findings extend cognitive schema theory to the domain of facial affect representation and highlight self-conscious emotions as an underexplored locus of depression-related perceptual bias.
Cui, B., Bex, P. J.
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