The role of spatial frequencies for facial pain categorization

Abstract

Studies on low-level visual information underlying pain categorization have led to inconsistent findings. Some show an advantage for low spatial frequency information (SFs) and others a preponderance of mid SFs. This study aims to clarify this gap in knowledge since these results have different theoretical and practical implications, such as how far away an observer can be in order to categorize pain. This study addresses this question by using two complementary methods: a data-driven method without a priori expectations about the most useful SFs for pain recognition and a more ecological method that simulates the distance of stimuli presentation. We reveal a broad range of important SFs for pain recognition starting from low to relatively high SFs and showed that performance is optimal in a short to medium distance (1.2–4.8 m) but declines significantly when mid SFs are no longer available. This study reconciles previous results that show an advantage of LSFs over HSFs when using arbitrary cutoffs, but above all reveal the prominent role of mid-SFs for pain recognition across two complementary experimental tasks.

Detection of emotional faces: the role of spatial frequencies and local features

“…With this method, cutoff frequencies are fixed beforehand, and can vary between studies. Other methods, such as spatial frequency Bubbles (Willenbockel et al, 2010;Plouffe-Demers et al, 2019;Charbonneau et al, 2021), band masking (Gaspar 2008), and bandpass filtering (Gold et al, 1999), can also be used to manipulate the spatial frequency content of stimuli. Spatial frequency Bubbles, for example, involves presenting stimuli with randomly sampled patches of spatial frequencies to determine which frequencies are important for a particular task.…”