Shape-invariant encoding of dynamic primate facial expressions in human perception.
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ABSTRACT:
SUBMITTER: Taubert N
PROVIDER: S-EPMC8195610 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Although a great deal of research has been conducted on the recognition of basic facial emotions (e.g., anger, happiness, sadness), much less research has been carried out on the more subtle facial expressions of an individual's mental state (e.g., anxiety, disinterest, relief). Of particular concern is that these mental state expressions provide a crucial source of communication in everyday life but little is known about the accuracy with which natural dynamic facial expressions of mental states are identified and, in particular, the variability in mental state perception that is produced. Here we report the findings of two studies that investigated the accuracy and variability with which dynamic facial expressions of mental states were identified by participants. Both studies used stimuli carefully constructed using procedures adopted in previous research, and free-report (Study 1) and forced-choice (Study 2) measures of response accuracy and variability. The findings of both studies showed levels of response accuracy that were accompanied by substantial variation in the labels assigned by observers to each mental state. Thus, when mental states are identified from facial expressions in experiments, the identities attached to these expressions appear to vary considerably across individuals. This variability raises important issues for understanding the identification of mental states in everyday situations and for the use of responses in facial expression research.
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Project description:With a shift in interest toward dynamic expressions, numerous corpora of dynamic facial stimuli have been developed over the past two decades. The present research aimed to test existing sets of dynamic facial expressions (published between 2000 and 2015) in a cross-corpus validation effort. For this, 14 dynamic databases were selected that featured facial expressions of the basic six emotions (anger, disgust, fear, happiness, sadness, surprise) in posed or spontaneous form. In Study 1, a subset of stimuli from each database (N = 162) were presented to human observers and machine analysis, yielding considerable variance in emotion recognition performance across the databases. Classification accuracy further varied with perceived intensity and naturalness of the displays, with posed expressions being judged more accurately and as intense, but less natural compared to spontaneous ones. Study 2 aimed for a full validation of the 14 databases by subjecting the entire stimulus set (N = 3812) to machine analysis. A FACS-based Action Unit (AU) analysis revealed that facial AU configurations were more prototypical in posed than spontaneous expressions. The prototypicality of an expression in turn predicted emotion classification accuracy, with higher performance observed for more prototypical facial behavior. Furthermore, technical features of each database (i.e., duration, face box size, head rotation, and motion) had a significant impact on recognition accuracy. Together, the findings suggest that existing databases vary in their ability to signal specific emotions, thereby facing a trade-off between realism and ecological validity on the one end, and expression uniformity and comparability on the other.
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