Effects of affective content and motivational context on neural gain functions during naturalistic scene perception

Abstract

Visual scene processing is modulated by semantic, motivational, and emotional factors, in addition to physical scene statistics. An open question is to what extent those factors affect low-level visual processing. One index of low-level visual processing is the contrast response function (CRF), representing the change in neural or psychophysical gain with increasing stimulus contrast. Here we aimed to (a) establish the use of an electrophysiological technique for assessing CRFs with complex emotional scenes and (b) examine the effects of motivational context and emotional content on CRFs elicited by naturalistic stimuli, including faces and complex scenes (humans, animals). Motivational context varied by expectancy of threat (a noxious noise) versus safety. CRFs were measured in 18 participants by means of sweep steady-state visual evoked potentials. Results showed a facilitation in visuocortical sensitivity (contrast gain) under threat, compared with safe conditions, across all stimulus categories. Facial stimuli prompted heightened neural response gain, compared with scenes. Within the scenes, response gain was smaller for scenes high in emotional arousal, compared with low-arousing scenes, consistent with interference effects of emotional content. These findings support the notion that motivational context alters the contrast sensitivity of cortical tissue, differing from changes in response gain (activation) when visual cues themselves carry motivational/affective relevance.

Higher amplitudes in steady‐state visual evoked potentials driven by square‐wave versus sine‐wave contrast modulation – A dual‐laboratory study

“…In the current study we used cut‐outs from larger scenes with intermediate complexity. More complex stimuli (including faces and naturalistic scenes) are common in cognitive‐affective perception and attention research (Alonso‐Prieto et al, 2013; Bekhtereva et al, 2019, 2021; Gruss et al, 2012; Rossion et al, 2012; Schettino et al, 2019; Tebbe et al, 2021; Wieser et al, 2014; Wieser, Miskovic, & Keil, 2016). Besides stimulus complexity, differences in stimulus size may play a role as larger stimuli evoke larger ssVEP amplitudes (Duszyk et al, 2014).…”

Higher amplitudes in steady-state visual evoked potentials driven by square-wave versus sine-wave contrast modulation – a dual-laboratory study.

“…In the current study we used cut-outs from larger scenes with intermediate complexity. More complex stimuli (including faces and naturalistic scenes) are common in cognitive-affective perception and attention research (Alonso-Prieto et al, 2013;Bekhtereva et al, 2019Bekhtereva et al, , 2021Gruss et al, 2012;Rossion et al, 2012;Schettino et al, 2019;Tebbe et al, 2021;Wieser et al, 2014;Wieser, Miskovic, et al, 2016). Besides stimulus complexity, differences in stimulus size may play a role as larger stimuli evoke larger ssVEP amplitudes (Duszyk et al, 2014).…”

Attentional Engagement and Disengagement: Quantifying Attentional Shifts Utilizing the Steady State Visual Evoked Potentials

“…Using this approach, it is possible to determine whether and if so, when, a change in the data occurred. To this end, the trial-averaged time varying signal of each participant (for the individual level analysis) and the group averaged signal (for the group level analysis) in each attentional condition was first down sampled to include a total of 31 points (out of the original 3074 points), in line with previous work (e.g., Tebbe et al, 2021). The signal was then averaged across these data points and the difference between each point and this averaged signal was calculated.…”