The Predictive Role of Low Spatial Frequencies in Automatic Face Processing: A Visual Mismatch Negativity Investigation.

Laurent Vercueil,Sylvain Harquel,Marie Gomot,Klara Kovarski,Adeline Lacroix,David Alleysson,Frédéric Dutheil,Martial Mermillod

doi:10.3389/fnhum.2022.838454

Abstract

Visual processing is thought to function in a coarse-to-fine manner. Low spatial frequencies (LSF), conveying coarse information, would be processed early to generate predictions. These LSF-based predictions would facilitate the further integration of high spatial frequencies (HSF), conveying fine details. The predictive role of LSF might be crucial in automatic face processing, where high performance could be explained by an accurate selection of clues in early processing. In the present study, we used a visual Mismatch Negativity (vMMN) paradigm by presenting an unfiltered face as standard stimulus, and the same face filtered in LSF or HSF as deviant, to investigate the predictive role of LSF vs. HSF during automatic face processing. If LSF are critical for predictions, we hypothesize that LSF deviants would elicit less prediction error (i.e., reduced mismatch responses) than HSF deviants. Results show that both LSF and HSF deviants elicited a mismatch response compared with their equivalent in an equiprobable sequence. However, in line with our hypothesis, LSF deviants evoke significantly reduced mismatch responses compared to HSF deviants, particularly at later stages. The difference in mismatch between HSF and LSF conditions involves posterior areas and right fusiform gyrus. Overall, our findings suggest a predictive role of LSF during automatic face processing and a critical involvement of HSF in the fusiform during the conscious detection of changes in faces.

Highlights

spatial frequencies (SF) refer to a spectrum of spatial information in an image, expressed visual Mismatch Negativity (vMMN) Spatially Filtered Faces as a number of cycles per degree of visual angle and derived from the Fourier transform (Morrison and Schyns, 2001; Park et al, 2012; Bachmann, 2016)
We investigated the involvement of low spatial frequencies (LSF) and high spatial frequencies (HSF) in predictive processes during automatic face processing
The results showed that the vMMN was larger for HSF faces than for LSF faces, revealing lower prediction error for LSF than for HSF

Summary

Introduction

While low spatial frequencies (LSF) convey coarse information mainly through the dorsal stream, high spatial frequencies (HSF) convey fine details through the ventral stream (see Skottun, 2015). Emotion categorization would rely more on LSF, at the early stages (e.g., Schyns and Oliva, 1999; Mermillod et al, 2005, 2010; Wang et al, 2021; but Deruelle et al, 2004; Jennings et al, 2017). This pattern can be reversed with additional task constraints, such as an interference effect (Lacroix et al, 2021; Shankland et al, 2021; but Beffara et al, 2015) or the complexity of the emotion (Cassidy et al, 2021), which leads to rely more on HSF. The efficiency of coarse-to-fine processing has been demonstrated in computer vision (e.g., Zhou et al, 2013; Zhang et al, 2014)

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Methods

Conclusion