st er s. Po C st er s. Po 0 00 F1 d. ec te Po st e d. te 00 00 0 Po s te d. te pr ot ec O’Toole, A. J., Roark, D. A., & Abdi, H. (2002). Trends in Cognitive Sciences, 6, 261-266. Hancock, P. J. B., Bruce, V., & Burton, A. M. (2000). Trends in Cognitive Sciences, 4, 330-337. Knight, B., & Johnston, A. (1997). Visual Cognition, 4, 265-273. Hill, H., Jinno, Y., & Johnston, A. (2003). Perception, 32, 561-566. .C rs F1 ht ig yr op References rs 0 ec ot pr rs te Po s 10 00 .F pr ht yr ig rs .C op 00 F1 pr ht ig yr op .C 00 F1 0 00 F1 d. yr ig Po st e ot 0 ec op rs .C op 0 00 F1 te yr d. • Expt 1: Manipulation x Movement, p = .013 • Expt 2: Manipulation x Familiarity, p = .037 3. Participants are less accurate matching a degraded image to a non-degraded video than matching two degraded videos • Changing the format within trials eliminates the movement advantage ed . ec te pr ht Po st e ec ot pr ht ig •Some interactions 2. Familiar faces are matched more accurately than unfamiliar faces • Even when participants do not know the face is familiar ct STATIC ot rs .C op MOVING • Expt 1: F(1,15) = 8.56, p = .01, eta2 = .363 • Expt 2: F(1,32) = 3.98, p = .054, eta2 = .111 1. It is possible to match faces based on movement alone • But this depends heavily on stimulus and task • Participants are more accurate when matching PLDs than avatars 1. 2. 3. 4. o 0 ot pr ht yr ig STATIC Conclusions .C rs te Po s 00 F1 d. ec te ot pr yr ig rs .C op te Po s 0 00 F1 d. te C op te rs . • Expt 1: Dynamic > Static, p = .009 Po s te pr o ig ht eta2 •Dynamic > Static…sometimes 00 F1 0 te d. ot ec pr ht op y rig Experiment 2: Matching Video to PLD and Video to avatar (N=33 undergrads) ht te Po s 00 .F te d ec pr ht yr ig Po st e 0 00 F1 d. ec te C • Expt 1: F(1,15) = 17.46, p = .001, = .538 • Expt 2: F(1,32) = 6.33, p = .017, eta2 = .165 • 2 Familiar/Unfamiliar) x 2 (PLD/Avatar) x 2 (Dynamic to Dynamic (MOVING)/Static to Static (STATIC)) (Dynamic/Static and Static/Dynamic conditions were also run, but are not Experiment 1: Matching PLD to PLD and avatar to avatar (N=16 undergrads) Experiment 2 •PLD > Avatar • Familiar > Unfamiliar 3. Do any of these effects change when participants have a non-degraded image to compare to? (Experiment 1 vs Experiment 2) • 2 s clips of 6 familiar (famous) and 6 unfamiliar faces, converted to PLDs and avatars. Sequential same/different task, within subjects MOVING STATIC • Expt 2: Dynamic = Static, p = 1 Methods s. MOVING ot MOVING STATIC 2. Does familiarity improve movement-based face matching? (Familiar vs Unfamiliar) ht pr ot 10 pr ht yr ig rs .C op 00 10 .F Questions • Is performance affected by the image manipulation used? (PLD vs avatar) os te r yr ig rs . F1 d. ot ec te .C op rs te Po s • It is unclear which is better at reducing static cues: facial point-light-displays (PLDs) or shape normalised avatars (but see 4) 1. Can movement act as cue to identity when static facial information is degraded? (Moving vs Static clips) Results C op 00 0 ot pr ht yr ig Experiment 1 • To examine movement-based face recognition, it is important to reduce static facial information3 ed ht te ec Po s te yr .C op rs te • Familiar faces are generally easier to match than unfamiliar faces2 - but few studies have tested this with moving faces shown) pr rs . Email: [email protected] • Characteristic facial movements can be used as an alternative pathway to recognise individuals1 os ot ec te d. C op yr F1 00 0 of Western Sydney 2 Newcastle University 3Macquarie University 4CSIRO d. ig 1 University Background py rig F1 ig ht Po st e ed . Rachel J. Bennetts1, Darren Burke2, Kevin Brooks3, Jeesun Kim1, Simon Lucey4, Jason Saragih4 & Rachel A. Robbins1 ht pr ot ec t Avatars versus point-light faces: Movement matching is better without a face
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