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A computational account of the mechanisms underlying face perception biases in depression.

ABSTRACT: Here, we take a computational approach to understand the mechanisms underlying face perception biases in depression. Thirty participants diagnosed with major depressive disorder and 30 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 different discriminability. We found no systematic effect of depression on the perceptual interactions between face expression and identity. We also found that decisional strategies used in our task were different for people with depression and controls, but in a way that was highly specific to the stimulus set presented. We show through simulation that the observed perceptual effects, as well as other biases found in the literature, can be explained by a computational model in which channels encoding positive expressions are selectively suppressed. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

SUBMITTER: Soto FA

PROVIDER: S-EPMC8425597 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Neural architecture underlying classification of face perception paradigms.

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Mature microsatellites: mechanisms underlying dinucleotide microsatellite mutational biases in human cells.

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Neuro-computational mechanisms and individual biases in action-outcome learning under moral conflict.

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| S-EPMC9988878 | biostudies-literature

Handedness is related to neural mechanisms underlying hemispheric lateralization of face processing.

Project description:While the right-hemispheric lateralization of the face perception network is well established, recent evidence suggests that handedness affects the cerebral lateralization of face processing at the hierarchical level of the fusiform face area (FFA). However, the neural mechanisms underlying differential hemispheric lateralization of face perception in right- and left-handers are largely unknown. Using dynamic causal modeling (DCM) for fMRI, we aimed to unravel the putative processes that mediate handedness-related differences by investigating the effective connectivity in the bilateral core face perception network. Our results reveal an enhanced recruitment of the left FFA in left-handers compared to right-handers, as evidenced by more pronounced face-specific modulatory influences on both intra- and interhemispheric connections. As structural and physiological correlates of handedness-related differences in face processing, right- and left-handers varied with regard to their gray matter volume in the left fusiform gyrus and their pupil responses to face stimuli. Overall, these results describe how handedness is related to the lateralization of the core face perception network, and point to different neural mechanisms underlying face processing in right- and left-handers. In a wider context, this demonstrates the entanglement of structurally and functionally remote brain networks, suggesting a broader underlying process regulating brain lateralization.

| S-EPMC4890016 | biostudies-literature

All-or-none neural mechanisms underlying face categorization: evidence from the N170.

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| S-EPMC9890453 | biostudies-literature

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Project description:This paper studies the effect of income (wealth) inequality on interpersonal trust. We propose a theoretical framework that links trust, trustworthiness and inequality. The key feature is that agents do not necessarily observe the entire income distribution but base their assessment on reference groups (i.e. they might hold a biased view of reality). In this framework the negative impact of inequality on interpersonal trust is related to the individual-specific perception of inequality. This has important implications for the empirical analyses since researchers typically do not observe perceptions but only objective measures of inequality (e.g. the Gini coefficient). We show that the use of the latter is appropriate only under restrictive assumptions and in general will result in an underestimation of the true effect. An unbiased estimate of the effect of inequality on trust can be obtained with a measure of individual-specific perceptions of inequality. Survey data support our framework. Perceptions of higher inequality exert a strong negative effect on trust.Supplementary informationThe online version contains supplementary material available at (10.1007/s10888-021-09490-x).

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Disrupted visual input unveils the computational details of artificial neural networks for face perception.

Project description:BackgroundConvolutional Neural Network (DCNN), with its great performance, has attracted attention of researchers from many disciplines. The studies of the DCNN and that of biological neural systems have inspired each other reciprocally. The brain-inspired neural networks not only achieve great performance but also serve as a computational model of biological neural systems.MethodsHere in this study, we trained and tested several typical DCNNs (AlexNet, VGG11, VGG13, VGG16, DenseNet, MobileNet, and EfficientNet) with a face ethnicity categorization task for experiment 1, and an emotion categorization task for experiment 2. We measured the performance of DCNNs by testing them with original and lossy visual inputs (various kinds of image occlusion) and compared their performance with human participants. Moreover, the class activation map (CAM) method allowed us to visualize the foci of the "attention" of these DCNNs.ResultsThe results suggested that the VGG13 performed the best: Its performance closely resembled human participants in terms of psychophysics measurements, it utilized similar areas of visual inputs as humans, and it had the most consistent performance with inputs having various kinds of impairments.DiscussionIn general, we examined the processing mechanism of DCNNs using a new paradigm and found that VGG13 might be the most human-like DCNN in this task. This study also highlighted a possible paradigm to study and develop DCNNs using human perception as a benchmark.

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