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51 Cards in this Set
- Front
- Back
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Goldstien & chance 80
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Kids vs adults same other face memory task
kids n/diff; adults = own race advantage dev schema for faces faces more sim to schema easier to recog |
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Light 79
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Prototypical face = T/D fx recog?
found T face harder recog and more erroneous recog |
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Valentine & Bruce 86
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D faces slower 2 classify as face vs jumbled
T face recog slower |
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Johnston et al 97
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18 T and 18 D
Sim ratings for pairs Construct face space 6 dimensions T faces = central D faces= outer space |
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Hosie & Milne 1999
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Von restroff fx
T face act like D face if lots D and one T Varying size area "capture" identity |
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Valentine 91
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Face space
multi dimensional norm vs exemplar |
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Valentine &endo 92
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Own race advantage recog and classify#
T&D fx for own and other race faces exemplar predict this, norm acct doesnt both prdict own race bias and T/D fx own race Expect more T than D faces -not found |
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Burton & Vokey
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inc no Dimensions and get more T- normal distribution
- if know amount skew real can est dimensions |
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Vokey & Read 95
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attractiveness, familiarity/ T/D likeability memorability
T= more attractive attractive or ugly memorable |
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Langlois & Roggman 90
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morphed faces
average = more T and more attractive |
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Perrett 94
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A vs B vs C
avergeness = not whole story |
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Valentine & Ferrera 91
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made neural network model T/D fx cat and reg
T faces cat better D faces recog better |
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Light79, Bruce 86
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Typical vs distinctive -faster and more accurate
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Valentine & Bruce 86
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faster typical- for categorisation" is a face"
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Goldstien and chance
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face on distinctiveness
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Valentine 91
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face space- relative to euclidean metric - similarity is analogue of distance in real world
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Craw 95
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Challenges this
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Euclidean
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diff to challenge
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Faces from homogenus population (single race)
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vector representations are normally distributed
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faces are bio constraints
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central limit theorem
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Burton bruce dench 94
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measure facial features deviates from norm
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face space densely packed exemplars
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typical close to average
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typical faces
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Sparsely
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distinctive
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dense
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easy catog, difficult recog can't see wood for trees
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Valentine 91
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ease of recog: - error of encoding similarity of vector to most sim exemplar, similary of vector to 2nd most sim exemplar
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Valentine and endo 92
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able to make predictions on race fx
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Exemplar
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doesnt matter what aspects of face encoding - doesn't matter if specific distances or holistic
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Tanaka & farah 93
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dimensions are configural and are second order relations (Rhodes 88)
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Turk & Pentland 91
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dimensions may be sim to eigenfaces (PCA) - holistic images, variable transparency
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Valentine and endo 92
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norm is preferred version), - prototype face/schema
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Goldenstein & Chance 98
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legacy of schema theory
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Valentine and bruce 86
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Prototype hyp sim to their theory
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Rhodes 87
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prototype thoeory explains caracature advantage
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lateral carac
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orthoganal to area of caracature
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Rhodes & Tremewa n94
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early work confirms that lateral caracatures should be harder than anticaracatures as a change of directions of vectors
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Lewis & Johnston 98
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did more rigerous work with this and contradicted them
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rhodes 87
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16% manipulated worse recognitoin
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byatt & rhodes
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absolute coding : exemplare based face-space model
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caracatures
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move vector to space of lower density; therefore easier to recognise from original
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byatt & rhodes
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there is a limit on caracaturization that comes from recognition 16%
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Byatt & Rhodes 98
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based on all nearest exemplars in a certain range, the size of the range effects how exemplars work together
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Voronoi -lewis & Johnston 99
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face space tesselated, leading to recall of identity, defineed by voronoi cells around a veridical exemplar - based on geometric nearest neighbour
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Voronoi -lewis & Johnston 99
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normal distributed ; voronoi cells w centres at chararter representation
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Voronoi -lewis & Johnston 99
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caracatures - representations more central to id region than veridicals
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voronoi and characature advantage
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explains adv for low advantage of photographic caracatures
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Benson & perrett 91
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4.4% photocaracture for likeness
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Perceptual nose
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fx encoding of faces into multidimensional space
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Perceptual nose
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area of closeness to others, local exemplar density, distance from norm (closer the more noise)
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similarity metric
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similarity metric based on prototype norm face
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distance encoded
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distance from norm encoded, proposed direction from norm= more important
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