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Alyson Saville, Benjamin Balas; Invariant properties of detecting personal familiarity in "thin slices" of behavior. Journal of Vision 2013;13(9):868. doi: 10.1167/13.9.868.
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© ARVO (1962-2015); The Authors (2016-present)
Glimpses of nonverbal behavior (or ‘thin slices’) offer ample visual information to make reliable judgments about individuals. Previous work has largely focused on personality characteristics and traits of the individual; however the nature of dyadic relationships (strangers, lovers, or friends) can also be determined (Ambady & Gray, 2002). Judgments from thin slices are known to be accurate, but the visual features supporting accurate performance are unknown. We explored (1) whether personal familiarity was detectable within the context of ‘thin slices’ of genuine interaction and (2), the invariant properties of thin-slice recognition. We asked participants to discriminate between familiar and unfamiliar social interactions in two experiments. In each task, participants sequentially viewed two 6-s silent videos on each trial. One clip depicted an individual interacting with an unfamiliar partner; the other depicted the same person interacting with a personally-familiar partner. All sequences were cropped so that only the target individual was visible. In Experiment 1, participants viewed either the original sequences (N=16), reversed sequences (N=16), or a static-image "slideshow" of the sequence (N=16). In Experiment 2, all participants viewed the original sequences and either clips played at double-speed (N=18) or half-speed (N=18). Participants in Experiment 1 classified the videos at above chance levels in the forward (M=60.4%) and reverse (M=60.6%) conditions, but were significantly better in the static-image slideshow condition (M=71.4% - One-way ANOVA, p = 0.018). In Experiment 2, we observed a main effect of task speed (p = .046), indicating a larger performance cost for fast videos compared to slow videos. We conclude that detecting personal familiarity via spontaneous natural gesture depends on information in static images more than face or body movement. While static images are typically less important for recognizing nonverbal behavior, we argue they may be valuable for making familiarity judgments from thin slices of behavior.
Meeting abstract presented at VSS 2013
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