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Kristen Baker, Catherine Mondloch; Building a representation of newly encountered faces: A role for context?. Journal of Vision 2018;18(10):557. doi: https://doi.org/10.1167/18.10.557.
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© ARVO (1962-2015); The Authors (2016-present)
Recognizing identity in naturalistic images of unfamiliar faces is challenging (Jenkins et al., 2011); two images of the same person often are misperceived as belonging to different people and images of different people often are misperceived as belonging to the same person. Thus, face learning involves increased tolerance of within-person variability in appearance and improved discrimination. We examined the process by which a perceiver determines the range of inputs that are attributable to a newly learned identity, such that novel images of that identity are recognized while those of a similar identity are excluded. Based on Tanaka's (2007) hypothesis that each identity is represented by an attractor field in multi-dimensional face space, the size of which is constrained by nearest neighbors, we predicted that learning a new face in the context of a similar identity would facilitate learning. In Experiment 1, participants (n=40) sorted 45 ambient images of three identities (15/identity) in the learning phase; two identities were similar (near neighbours) and one was dissimilar (far neighbour). In the test phase, participants identified new images of the learned identities when intermixed with novel identities. Performance (d', hits, FAs) did not vary for near vs. far neighbours, ps>0.15—perhaps because accuracy approached ceiling. In Experiment 2, participants (to date, n=24) sorted only 15 images to capture the representation of identity earlier in the learning process. Performance was worse in Experiment 2, p< 0.001; participants made fewer hits and more false alarms. Nonetheless, performance did not vary for near vs. far neighbours, ps>0.21. Collectively our findings confirm that identity learning involves both improved recognition of new instances (increased tolerance of variability) and improved discrimination. To date, though, we find no evidence that this learning is best accounted for by Valentine's (1991) multi-dimensional face space model, calling for revised theories of face recognition.
Meeting abstract presented at VSS 2018
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