Neural correlates of perisaccadic visual mislocalization in extrastriate cortex

Abstract

When interacting with the visual world using saccadic eye movements (saccades), the perceived location of visual stimuli becomes biased, a phenomenon called perisaccadic mislocalization, which is indeed an exemplar of the brain’s dynamic representation of the visual world. However, the neural mechanism underlying this altered visuospatial perception and its potential link to other perisaccadic perceptual phenomena have not been established. Using a combined experimental and computational approach, we were able to quantify spatial bias around the saccade target (ST) based on the perisaccadic dynamics of extrastriate spatiotemporal sensitivity captured by statistical models. This approach could predict the perisaccadic spatial bias around the ST, consistent with the psychophysical studies, and revealed the precise neuronal response components underlying representational bias. These findings also established the crucial role of response remapping toward ST representation for neurons with receptive fields far from the ST in driving the ST spatial bias. Moreover, we showed that, by allocating more resources for visual target representation, visual areas enhance their representation of the ST location, even at the expense of transient distortions in spatial representation. This potential neural basis for perisaccadic ST representation, also supports a general role for extrastriate neurons in creating the perception of stimulus location.

Time-varying generalized linear models: characterizing and decoding neuronal dynamics in higher visual areas

“…Nor do more complicated GLM-based paradigms scale in a tractable manner to capture high-dimensional structures such as the inter-neuronal relationships described by population-level models, or nonlinear and dynamic representation of stimulus space described by the modulatory or time-varying models. Although augmenting GLMs with regularization or dimensionality reduction procedures, such as sparse or low-dimensional regression models, have shown to be promising for tackling the dimensionality problem ( Gerwinn et al, 2010 ; Sheikhattar et al, 2016 ; Aoi and Pillow, 2018 ; Zoltowski and Pillow, 2018 ; Niknam et al, 2019 ; Semedo et al, 2019 ), identifying behaviorally relevant dimensions ( Akbarian et al, 2021 ; Sani et al, 2021 ; Valente et al, 2021 ; Weng et al, 2023 ) which can directly link representation to readout is another important direction for current and future research.…”

“…There are biologically-oriented modulatory models that incorporate the nonlinear effects of stimulus-driven suppression ( Butts et al, 2007 ), or include contextual effects in sensory responses ( Ahrens et al, 2008 ; Williamson et al, 2016 ), or implement a response gain control mechanism according to the stimulus contrast ( Rabinowitz et al, 2011 ). The GLM-based approach has been used to provide mechanistic level understanding of several perceptual phenomena ( Kayser et al, 2015 ; Zanos et al, 2016 ; Akbarian et al, 2021 ; Weng et al, 2023 ). A biophysical interpretation of one GLM has been suggested as a special case of a conductance-based encoding model, which bridged the disparity between statistical and biophysical models ( Latimer et al, 2019 ).…”

“…The spatial bias captured by the full model (dark green) was eliminated in the reduced model with bias-relevant kernel components removed (light green). Spatial bias is significantly smaller in the reduced model in the period indicated by the gray bar (50-150 ms after saccade onset) related to Figure 3 in Weng et al (2023) .…”

“…The variations of GLMs mentioned in previous sections prove the power of this statistical modeling framework in capturing high-dimensional and nonlinear relationships between neural responses and multiple internal and external covariates on the same timescale as tasks or behaviors. These time-varying models allow for developing model-based decoders that can dynamically read out sensory or behavioral information from time-varying response characteristics in higher cortical areas ( Park et al, 2014 ; Aoi et al, 2020 ; Akbarian et al, 2021 ; Weng et al, 2023 ).…”

“…This model-based approach was thus able to link perisaccadic responses to perception, revealing a potential neural basis for transsaccadic spatial integration. Using a similar approach based on the manipulation of the fitted SV-GLM kernels, Weng and colleagues were able to identify the essential extrastriate sensitivity components responsible for driving a spatial bias in the localization of perisaccadic stimuli ( Figure 6E ), which represents another example of using model-based computational manipulations to link neural responses to perception ( Weng et al, 2023 ).…”