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.
Highlights
Studies on low-level visual information underlying pain categorization have led to inconsistent findings
This method is considered more ecological since in everyday life, the distance at which one sees people’s facial expressions can vary considerably. Both of the following experiments included all of the six basic emotions and neutral, only data related to pain will be presented since this article focuses on pain perception in faces
Spatial frequencies for accurate pain categorization were analyzed by producing classification images (CI) which represent how strongly each spatial frequency (SF) is associated with accuracy
Summary
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. Experiment 1 aimed to reveal which SFs are the most useful for pain categorization among other emotional expressions (i.e. anger, disgust, fear, joy, sadness, surprise, neutral or pain) This kind of experiment is standard in the facial expression literature (e.g.14,19) but instead of using cutoffs to create low-pass and high-pass filters[19], we used SF Bubbles. If the sampled SFs are useful for processing a particular facial expression, it will increase the likelihood that participants will respond accurately, and if they are not useful, it decreases the likelihood that participants will respond accurately
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