Abstract

J. Heeman, B. J. White, S. Van der Stigchel, J. Theeuwes, L. Itti, D. P. Munoz, Saliency response in superior colliculus at the future saccade goal predicts fixation duration during free viewing of dynamic scenes, Journal of Neuroscience, pp. 1--24, Society for Neuroscience, Nov 2024. [2023 impact factor: 4.4]

Abstract: Eye movements in daily life occur in rapid succession and often without a predefined goal. Using a free viewing task, we examined how fixation duration prior to a saccade correlates to visual saliency and neuronal activity in the superior colliculus (SC) at the saccade goal. Rhesus monkeys (three male) watched videos of natural, dynamic, scenes while eye movements were tracked and, simultaneously, neurons were recorded in the superficial and intermediate layers of the superior colliculus (SCs and SCi respectively), a midbrain structure closely associated with gaze, attention, and saliency coding. Saccades that were directed into the neuron’s receptive field (RF) were extrapolated from the data. To interpret the complex visual input, saliency at the RF location was computed during the pre-saccadic fixation period using a computational saliency model. We analyzed if visual saliency and neural activity at the saccade goal predicted pre-saccadic fixation duration. We report three major findings: 1) Saliency at the saccade goal inversely correlated with fixation duration, with motion and edge information being the strongest predictors. 2) SC visual saliency responses in both SCs and SCi were inversely related to fixation duration. 3) SCs neurons, and not SCi neurons, showed higher activation for two consecutive short fixations, suggestive of concurrent saccade processing during free viewing. These results reveal a close correspondence between visual saliency, SC processing, and the timing of saccade initiation during free viewing and are discussed in relation to their implication for understanding saccade initiation during real-world gaze behavior.

Themes: Model of Bottom-Up Saliency-Based Visual Attention, Computational Modeling