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51 Cards in this Set

  • Front
  • Back
Goldstien & chance 80
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
Light 79
Prototypical face = T/D fx recog?
found T face harder recog and more erroneous recog
Valentine & Bruce 86
D faces slower 2 classify as face vs jumbled

T face recog slower
Johnston et al 97
18 T and 18 D
Sim ratings for pairs
Construct face space
6 dimensions
T faces = central D faces= outer space
Hosie & Milne 1999
Von restroff fx
T face act like D face if lots D and one T
Varying size area "capture" identity
Valentine 91
Face space
multi dimensional
norm vs exemplar
Valentine &endo 92
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
Burton & Vokey
inc no Dimensions and get more T- normal distribution
- if know amount skew real can est dimensions
Vokey & Read 95
attractiveness, familiarity/ T/D likeability memorability
T= more attractive
attractive or ugly memorable
Langlois & Roggman 90
morphed faces
average = more T and more attractive
Perrett 94
A vs B vs C
avergeness = not whole story
Valentine & Ferrera 91
made neural network model T/D fx cat and reg
T faces cat better
D faces recog better
Light79, Bruce 86
Typical vs distinctive -faster and more accurate
Valentine & Bruce 86
faster typical- for categorisation" is a face"
Goldstien and chance
face on distinctiveness
Valentine 91
face space- relative to euclidean metric - similarity is analogue of distance in real world
Craw 95
Challenges this
diff to challenge
Faces from homogenus population (single race)
vector representations are normally distributed
faces are bio constraints
central limit theorem
Burton bruce dench 94
measure facial features deviates from norm
face space densely packed exemplars

typical close to average
typical faces
easy catog, difficult recog can't see wood for trees
Valentine 91
ease of recog: - error of encoding similarity of vector to most sim exemplar, similary of vector to 2nd most sim exemplar
Valentine and endo 92
able to make predictions on race fx
doesn’t matter what aspects of face encoding - doesn't matter if specific distances or holistic
Tanaka & farah 93
dimensions are configural and are second order relations (Rhodes 88)
Turk & Pentland 91
dimensions may be sim to eigenfaces (PCA) - holistic images, variable transparency
Valentine and endo 92
norm is preferred version), - prototype face/schema
Goldenstein & Chance 98
legacy of schema theory
Valentine and bruce 86
Prototype hyp sim to their theory
Rhodes 87
prototype thoeory explains caracature advantage
lateral carac
orthoganal to area of caracature
Rhodes & Tremewa n94
early work confirms that lateral caracatures should be harder than anticaracatures as a change of directions of vectors
Lewis & Johnston 98
did more rigerous work with this and contradicted them
rhodes 87
16% manipulated worse recognitoin
byatt & rhodes
absolute coding : exemplare based face-space model
move vector to space of lower density; therefore easier to recognise from original
byatt & rhodes
there is a limit on caracaturization that comes from recognition 16%
Byatt & Rhodes 98
based on all nearest exemplars in a certain range, the size of the range effects how exemplars work together
Voronoi -lewis & Johnston 99
face space tesselated, leading to recall of identity, defineed by voronoi cells around a veridical exemplar - based on geometric nearest neighbour
Voronoi -lewis & Johnston 99
normal distributed ; voronoi cells w centres at chararter representation
Voronoi -lewis & Johnston 99
caracatures - representations more central to id region than veridicals
voronoi and characature advantage
explains adv for low advantage of photographic caracatures
Benson & perrett 91
4.4% photocaracture for likeness
Perceptual nose
fx encoding of faces into multidimensional space
Perceptual nose
area of closeness to others, local exemplar density, distance from norm (closer the more noise)
similarity metric
similarity metric based on prototype norm face
distance encoded
distance from norm encoded, proposed direction from norm= more important