An image-computable model on how endogenous and exogenous attention differentially alter visual perception

Abstract

Attention can facilitate or impair texture segmentation, altering whether objects are isolated from their surroundings in visual scenes. We simultaneously explain several empirical phenomena of texture segmentation and its attentional modulation with a single image-computable model. At the model’s core, segmentation relies on the interaction between sensory processing and attention, with different operating regimes for involuntary and voluntary attention systems. Model comparisons were used to identify computations critical for texture segmentation and attentional modulation. The model reproduced (i) the central performance drop, which is the parafoveal advantage for segmentation over the fovea, (ii) the peripheral improvements and central impairments induced by involuntary attention and (iii) the uniform improvements across eccentricity by voluntary attention. The proposed model reveals distinct functional roles for involuntary and voluntary attention and provides a generalizable quantitative framework for predicting the perceptual impact of attention across the visual field.

Differential Effects of endogenous and exogenous attention on sensory tuning

“…Critically, endogenous attention enhanced sensitivity to SFs at, above and below the target SF, whereas exogenous attention preferentially enhanced SFs above the target SF. These findings provide evidence for the assumed differential SF profiles required to model the effects of these two types of attention on performance (Jigo et al, 2021).…”

“…Furthermore, our findings help constrain models of spatial vision and of visual attention by furthering our knowledge of the neural computations underlying the effects of attention on basic dimensions of spatial vision. Specifically, they provide evidence for the assumed differential SF profiles required to model the effects of these two types of attention on performance (Jigo et al, 2021).…”

“…A recent image-computable normalization model of attention reproduces the well-established differential effects of endogenous and exogenous attention on contrast sensitivity and spatial resolution by assuming that the attention effects are mediated by differences in SF tuning (Jigo, Heeger & Carrasco, 2021). Here, we investigate, for the first time, whether and how endogenous and exogenous attention differentially mediate sensory representations.…”

A dynamic 1/f noise protocol to assess visual attention without biasing perceptual processing

“…Moreover, it can shed new light on a fundamental question of attention research, namely whether attention is space-based or objectbased (e.g., Duncan, 1984;Lavie & Driver, 1996;Moore, Yantis, & Vaughan, 1998; for a review, see Chen, 2012). Finally, accurate estimation of the size and shape of the attention focus, as well as its temporal and spatial dynamics during voluntary (endogenous) or automatic (exogenous) attentional orienting, or prior to goal-directed actions has crucial implications for conceptual and computational models of visual attention (e.g., Schneider, 1995;Reynolds & Heeger, 2009;Maunsell, 2015;Denison, Carrasco, & Heeger, 2021;Jigo et al, 2021).…”

“…Depending on the attentional state of the observer, the same retinal input elicits different neurophysiological responses (Hillyard & Anllo-Vento, 1998;Gandhi, Heeger, & Boynton, 1999;Reynolds, Pasternak, & Desimone, 2000). As a behavioral consequence of these modulations, attention increases spatial resolution (Yeshurun & Carrasco, 1998;Jigo, Heeger, & Carrasco, 2021), enhances contrast sensitivity (Lee, Itti, Koch, & Braun, 1999;Pestilli, Ling, & Carrasco, 2009;Jigo & Carrasco, 2020), and even alters visual appearance (Rolfs & Carrasco, 2012;Carrasco & Barbot, 2019). Given that stimuli presented in the focus of attention are recognized faster than those appearing outside the focus (Posner, Snyder, & Davidson, 1980), the spatial deployment of attention is frequently deduced from manual response times.…”