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Updated: Jan 24, 2026

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
Published on: January 23, 2017
Differential sustained and transient temporal processing across visual streams
Anthony Stigliani1, Brianna Jeska1, Kalanit Grill-Spector1,2
1Psychology Department, Stanford University, Stanford, California, United States of America.
Abstract:
How do high-level visual regions process the temporal aspects of our visual experience? While the temporal sensitivity of early visual cortex has been studied with fMRI in humans, temporal processing in high-level visual cortex is largely unknown. By modeling neural responses with millisecond precision in separate sustained and transient channels, and introducing a flexible encoding framework that captures differences in neural temporal integration time windows and response nonlinearities, we predict fMRI responses across visual cortex for stimuli ranging from 33 ms to 20 s. Using this innovative approach, we discovered that lateral category-selective regions respond to visual transients associated with stimulus onsets and offsets but not sustained visual information. Thus, lateral category-selective regions compute moment-to-moment visual transitions, but not stable features of the visual input. In contrast, ventral category-selective regions process both sustained and transient components of the visual input. Our model revealed that sustained channel responses to prolonged stimuli exhibit adaptation, whereas transient channel responses to stimulus offsets are surprisingly larger than for stimulus onsets. This large offset transient response may reflect a memory trace of the stimulus when it is no longer visible, whereas the onset transient response may reflect rapid processing of new items. Together, these findings reveal previously unconsidered, fundamental temporal mechanisms that distinguish visual streams in the human brain. Importantly, our results underscore the promise of modeling brain responses with millisecond precision to understand the underlying neural computations.
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