Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 62,198 bioRxiv papers from 276,130 authors.
Visual recognition involves integrating visual information with other sensory information and prior knowledge. In accord with Bayesian inference under conditions of unreliable visual input, the brain relies on the prior as a source of information to achieve the inference process. This drives a top-down process to improve the neural representation of visual input. However, the extent to which non-stimulus-driven top-down information affects processing in the ventral stream is still unclear. We conducted a perceptual decision-making task using blurred images, while conducting functional magnetic resonance imaging. We then transformed brain activity into deep neural network features to distinguish bottom-up and top-down signals. We found that top-down information unrelated to the stimulus had a minimal effect on lower-level visual processes. The neural representations of degraded stimuli that were misrecognized were still correlated with the correct object category in the lower levels of processing. In contrast, activity in the higher cognitive areas was more strongly correlated with recognition reported by the subjects. The results indicated a discrepancy between the results of processing at the lower and higher levels, indicating the existence of a stimulus-independent top-down signal flowing back down the hierarchy. These findings suggest that integration between bottom-up and top-down information takes the form of competing evidence in higher visual areas between prior-driven top-down and stimulus-driven bottom-up signals. These findings could provide important insight into the different modes of integration of neural signals in the visual cortex that contribute to the visual inference process.
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