Multiple perceptual strategies used by macaque monkeys for face recognition
- PMID: 18787848
- DOI: 10.1007/s10071-008-0179-7
Multiple perceptual strategies used by macaque monkeys for face recognition
Abstract
Successful integration of individuals in macaque societies suggests that monkeys use fast and efficient perceptual mechanisms to discriminate between conspecifics. Humans and great apes use primarily holistic and configural, but also feature-based, processing for face recognition. The relative contribution of these processes to face recognition in monkeys is not known. We measured face recognition in three monkeys performing a visual paired comparison task. Monkey and humans faces were (1) axially rotated, (2) inverted, (3) high-pass filtered, and (4) low-pass filtered to isolate different face processing strategies. The amount of time spent looking at the eyes, mouth, and other facial features was compared across monkey and human faces for each type of stimulus manipulation. For all monkeys, face recognition, expressed as novelty preference, was intact for monkey faces that were axially rotated or spatially filtered and was supported in general by preferential looking at the eyes, but was impaired for inverted faces in two of the three monkeys. Axially rotated, upright human faces with a full range of spatial frequencies were also recognized, however, the distribution of time spent exploring each facial feature was significantly different compared to monkey faces. No novelty preference, and hence no inferred recognition, was observed for inverted or low-pass filtered human faces. High-pass filtered human faces were recognized, however, the looking pattern on facial features deviated from the pattern observed for monkey faces. Taken together these results indicate large differences in recognition success and in perceptual strategies used by monkeys to recognize humans versus conspecifics. Monkeys use both second-order configural and feature-based processing to recognize the faces of conspecifics, but they use primarily feature-based strategies to recognize human faces.
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