The role of expectations in visual spatial coding across the visual hierarchy
Year: 2024
Type: article
Abstract: Predictive processing theorizes that the brain predicts events based on prior experiences. Mismatches between the predictions and input lead to prediction errors (PEs). Despite the theory's popularity, our understanding of the role of PEs in visual spatial perception remains limited. Here, we investigated predicted and unpredicted coding of visual locations across the visual hierarchy, utilizing the predictability of the standard population receptive field (pRF) mapping paradigm while sampling BOLD responses at ultra-high field fMRI. Our experiment featured different conditions in which unpredictable stimulus omissions and/or violations (different bar location and orientation) were either embedded in the standard stimulus sequence, or presented separately. These conditions serve to produce prediction errors, both of stimulus presence and of stimulus location. For all conditions, we first calculated test-retest reliability of BOLD responses to identical stimulus sequences in different brain regions. We reasoned that if PEs drive BOLD responses, this should increase test-retest reliability across runs relative to a fully predictable stimulus design. We indeed find this pattern of results selectively in higher-level and not lower level visual cortex. Next, we fit a spatial divisive normalisation (DN-pRF) model to the BOLD timecourses in the standard pRF stimulus sequence, and tested whether bold timecourses in conditions with unexpected stimuli follow this model, which is linear in time. This analysis also indicates that PEs drive high-level visual cortex responses more than low-level visual cortex. These findings suggest that prediction error responses in visual cortex follow the evolution of temporal scales of integration, from fast to slow, along the visual hierarchy. This hints at a tight relationship between temporal divisive normalization and predictive processing.
Source: Journal of Vision
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $1,850
OpenAlex ID: W4402946690