Presaccadic EEG activity predicts visual saliency in free‐viewing contour integration

Abstract

While viewing a scene, the eyes are attracted to salient stimuli. We set out to identify the brain signals controlling this process. In a contour integration task, in which participants searched for a collinear contour in a field of randomly oriented Gabor elements, a previously established model was applied to calculate a visual saliency value for each fixation location. We studied brain activity related to the modeled saliency values, using coregistered eye tracking and EEG. To disentangle EEG signals reflecting salience in free viewing from overlapping EEG responses to sequential eye movements, we adopted generalized additive mixed modeling (GAMM) to single epochs of saccade-related EEG. We found that, when saliency at the next fixation location was high, amplitude of the presaccadic EEG activity was low. Since presaccadic activity reflects covert attention to the saccade target, our results indicate that larger attentional effort is needed for selecting less salient saccade targets than more salient ones. This effect was prominent in contour-present conditions (half of the trials), but ambiguous in the contour-absent condition. Presaccadic EEG activity may thus be indicative of bottom-up factors in saccade guidance. The results underscore the utility of GAMM for EEG-eye movement coregistration research.

Unfold: An integrated toolbox for overlap correction, non-linear modeling, and regression-based EEG analysis

“…Appropriate analysis of such datasets requires a paradigm shift away from simple averaging techniques towards more sophisticated, regression-based approaches (Amsel, 2011;Frömer, Maier, & Abdel Rahman, 2018;Hauk, Davis, Ford, Pulvermüller, & Marslen-Wilson, 2006;Pernet, Chauveau, Gaspar, & Rousselet, 2011;N. J. Smith & Kutas, 2015b;Van Humbeeck, Meghanathan, Wagemans, van Leeuwen, & Nikolaev, 2018) that can deconvolve overlapping potentials and also control or model the effects of both linear and non-linear covariates on the neural response. Importantly, the basic algorithms to deconvolve overlapping signals and to model the influences of both linear and non-linear covariates already exist.…”

Planning to revisit: neural activity in refixation precursors

“…We used a dataset of EEG eye-movement coregistration, from which we previously analyzed stimulus conditions (Van Humbeeck et al, 2018) and compared the EEG of refixations and ordinary fixations . The precursor fixations from this dataset have not been analyzed previously.…”

“…To decide between these opposing predictions we studied EEG coregistered with eye movement. We reanalyzed a dataset containing EEG and eye movement recordings collected during unrestricted visual search for a contour in a field of Gabor elements (Van Humbeeck et al, 2018). Our previous analysis of refixations in this dataset were aimed at the mechanisms of refixation control .…”

Analyzing combined eye-tracking/EEG experiments with (non)linear deconvolution models

“…If such sequential effects occur often enough in an experiment, they can be explicitly modeled within the deconvolution framework. For example, in a scene viewing study, on could add an additional predictor that codes whether a fixation happened early or late after scene onset (Fischer, Graupner, Velichkovsky, & Pannasch, 2013) or whether it was the first fixation or a refixation on a particular image region (Van Humbeeck et al, 2018).…”