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Uncertainty and Invariance in the Human Visual Cortex.

The way in which input noise perturbs the behavior of a system depends on the internal processing structure of the system. In visual psychophysics, there is a long tradition of using external noise methods (i.e. adding noise to visual stimuli) as tools for system identification. Here, we demonstrate that external noise affects processing of visual scenes at different cortical areas along the human ventral visual pathway, from retinotopic regions to higher occipitotemporal areas implicated in visual shape processing. We found that, when the contrast of the stimulus was held constant, the further away from the retinal input a cortical area was the more its activity, as measured with fMRI, depended on the signal-to-noise ratio (SNR) of the visual stimulus. A similar pattern of results was observed when trials with correct and incorrect responses were analyzed separately. We interpret these findings by extending signal detection theory to fMRI data analysis. This approach reveals the sequential ordering of decision stages in the cortex by exploiting the relation between fMRI response and stimulus SNR. In particular, our findings provide novel evidence that occipitotemporal areas in the ventral visual pathway form a cascade of decision stages with increasing degree of signal uncertainty and feature invariance.

BRAINfest 2017

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