Abstract
Being able to detect pain from facial expressions is critical for pain communication. Alongside identifying the specific facial codes used in pain recognition, there are other types of more basic perceptual features, such as spatial frequency (SF) which refers to the amount of detail in a visual display. Low-SF carries coarse information, which can be seen from a distance, and high-SF carries fine-detailed information that can only be perceived when viewed close up. Since this type of basic information has not been considered in the recognition of pain we therefore investigated the role of low- and high-SF information in the decoding of facial expressions of pain. Sixty-four pain free adults completed two independent tasks: a multiple expression identification task of pain and core emotional expressions, and a dual expression “either-or” task (pain vs. fear, pain vs. happiness). While both low-SF and high-SF information makes the recognition of pain expressions possible, low-SF information seemed to play a more prominent role. This general low-SF bias would seem an advantageous way of potential threat detection, as facial displays will be degraded if viewed from a distance or in peripheral vision. One exception was found, however, in the “pain-fear” task, where responses were not affected by SF type. Together this not only indicates a flexible role for SF information that depends on task parameters (goal context), but suggests that in challenging visual conditions, we perceive an overall affective quality of pain expressions rather than detailed facial features.
Original language | English |
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Pages (from-to) | 1670-1682 |
Number of pages | 13 |
Journal | Pain |
Volume | 156 |
Issue number | 9 |
DOIs | |
Publication status | Published - Sept 2015 |
Keywords
- pain
- facial expression
- recognition
- spatial frequency
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Chris Eccleston
- Department for Health - Professor
- Centre for Pain Research
- Bath Institute for the Augmented Human
- Centre for Bioengineering & Biomedical Technologies (CBio)
Person: Research & Teaching, Core staff, Affiliate staff