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131 Cards in this Set
- Front
- Back
Johnston & Ellis 95
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Uniform (same as adult so uniform density
Differential model kids - fewer dimensions adult so same density |
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?
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5 yrs
D fx 4 classifcation no fx 4 recog- consistent w norm-uniform or exemplar differential |
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Brennan 85
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Caracature generator
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Rhodes 87
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recog = c> Veridical> anti- c
best recog 16% C |
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Benson & Perrett 91
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Add texture
best recog 4.4% |
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Rhodes 87
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Prior face space
C fx = mem exaggerated like C? C make d features more salient |
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Stevenage 95
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-c = increae vector length (angle same)
more D so easier to recog |
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Tanaka and simon 96
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build neural net model C fx
average from 3 vectors = norm exaggerate to get C c= better recog supports norm based model |
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Carey (2002)
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lateral caricatures
norm account incorrect generate laterals |
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Rhodes 98
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lateral = btw anti-c and veridical
non- consistent norm act |
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Lewis & Johnson 98
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Lateral C no -ive fx inconsisten euclidean distance for recog
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Exemplar : Rhodes 93
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Absolute coding
makes image more Diff more competing reps but more diff stored rep balance= c- better veridical |
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Lewis and Johnston 99
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Veronoi model
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Boyatt & Rhodes 98
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Other race faces and caricatures
C advantage when exaggerate away other race norm consistent |
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Lee & Perrett 97
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brief presentations caracture adv at 40% exaggerated = brief exposure fx
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Probs w voronoi
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identity regions- fills whole veroni, therefore false positive for unfamiliar face
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face space r
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similar to voroni - comparision btw exemplars
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Nosofsky 86
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generalized context model
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Valentine ferrara 91
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generalized context model linked to face space
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Generalized context model
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uses summed similarity rule: probability of response = sum of probe to exemplars devided by sum of similarity of probe to all exemplars
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Probs w Gen. Context model
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highly distinctive - high caracature recognition & unable to account for caracature fx
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Zaki & Nosofsky 2001
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P's learned new faces, response to morph face greater than parent faces of morph
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Lee Byatt & Rhodes
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GCM- explains caracature fx : 16 famous faces - recog carac, veridicals, anticarac
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GCM- hig recog though extremely caracatured
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- wrong as face stops looking like face after while
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Lewis 99- familiarity
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people seen a lot are faster than less salient
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Valentine2001
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new face = new encounter ed : overcomes false positive rct in voronoi
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computational
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input randomly generated normally distributed data ; add parameters
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Shephard 77
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multidimensional scaling on pattern of clustering, 100 faces 32 most important faces rated, 10/11 factors account for variance
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lewis -face space-r
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15-22 dimensions
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Turk & Pentland
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similar to no of eigenfaces employed to generate recognisable face
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Light 79
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faces rated as highly distinctive - easier to recog
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Burton, Bruce & Dench 94
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distinctiveness= dist btw face representation and av face
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Burton & Vokey
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Typicality paradox
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typical
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densely populated
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distinctive
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sparse
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lewis
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if seen in 2D one see highly typical than distinctiveness therefore distinctiveness ratings should be skewed
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lewis
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larger no of dimensions the smaller the skew
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too large dimensions
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decreases variability of distinctiveness
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skew
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larger skew in ratings may b due to fewer dimensions used to process 3/4 faces that frontal
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Benson & Prett 94
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line drawn faces - caracature fx better for typical than distinctive relationship is curvilinear - sim to -ive power function w looking at distinctive and caracature for best likeness
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Bothwell brigham and malpass 89
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other race harder to recog than own
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own race faces
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high exposure fx
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valentine 91 - face space and race fx
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other race faces clustered far away
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norm based race faces
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hard to disting btw norm vectors and other race faces
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Valentine & endo 92 -exemplar based w races
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better for understand own race bias
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Valentine & endo 92 -exemplar based w races
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other faces normally distrib at distance from own race average
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Valentine & endo 92 -exemplar based w races
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have less contact therefore less representations
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destinctions btw races
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blue eyes etc
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Byatt & Rodes 99
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disagreed w norm based model for caracatured advantage for other race faces - predicted
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Byatt & Rodes 99
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agreed w absolute -coding - caracature advantage great for other race faces when away from norm
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Valentine & Bruce : face categorisation
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: typicality advantage
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Valentine & Bruce 86
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intact vs jumbled faces ; all known exemplars contribute to activation ;
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Valentine & Bruce 86
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equation for activation: r categorisation (x) = sum Ai { acti(x)}
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Valentine & Bruce 86
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no within -category competition
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sum of activations of exemplars
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threshold dependent on non -face probes (scrambled)
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Lewis 98
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found typicality fx for non-prob cat faces vs human faces
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Valentine & Bruce 86
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rctn time for familiarity slower than categ
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unfamiliar : lewis & Johnston 99
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veronoi model cant deal w unfamiliar faces ; probe faces recog as familiar person but at diff speeds (depending on cell distance)
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Valentine 2001
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threshold activation
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Lewis & Johnson 97
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face learning memory task
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Face learning memory task
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unfam face, test faces (target vs distractor) ; famous not included therefore remove all previously known faces from criteria
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Face learning memory task
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accounts for false positive advantage
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Valentine 91
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typical unfamiliar faces- take longer to be responded to than distinctive unfamiliar
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Valentine 91
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Typical produces high false positives rate
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Busey 98
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location of face space det factors like distinct
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Bartlett & hurry 80
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typical face: familiarity, feeling of "oldness"
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Typicality : Vokey & Read 92
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attractiveness, familiarity, likeability. Memorability,
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context and typicality
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context induced- prior exposure
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Context free
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structurally induced- memory not indexed
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Bartlett 84
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distinctive have high familiarity- prior exposure, easy to encode , context-free
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O'toole, Deffenbacher 94
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digitised pics, put into neural netwrk, white vs asian, memorisability small, local features used, familiarity global aspects used
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Uttal Barauch & Allen 95
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high spatial freq- used for discrim : global shape info underlies familiarity
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Race bias: brigham malpass 82
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field studies
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Lab studies of race bias
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Messiner & brigham 2001
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Goldstien & chance
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race fx : tested white children and adults recog white japenese adults - scores increase w age, white better than japenese recognised
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MacLin and Malpass 2001
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cross race fx sim from 5 yrs to adulthood
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Wright boyd & Tredoux 2003
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own rave bias- people better at recog own face : south africa vs uk : shown pics black and white, had seen faces in deck?; P's give q abt interacial contact; confidence accuracy best w own race
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Malpass& Kravitz 69
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prob w data: low recog performance for other race faces- differential experience
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O'toole 95 perceptual expertise can help
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Anthony 92
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other races recog greater than own
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Absolute encoding
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distinctiveness and change in exemplar density
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caractures & absolute encoding
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caractures more distinctive
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caractures & absolute encoding
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caractures high sep from other points (potential distractors) = distinctive
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anticaracatures
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decrease exemplar density
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Rhodes 87
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caracatures dont always enhance performance - decrease in exemp density doesn't work sometimes
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Rhodes & Mclean 90
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norm doesn't rep true tendency
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Rhodes & Mclean 90
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low contact- compare far away from own norm
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Rhodes & Mclean 90
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high contact norm shifts to rep all faces in space
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Absolute encoding & race faces
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dimensions tuned through perceptual learning
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Absolute encoding & race faces
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features w disting btw exemplars doesn't related to other races (ie black)
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Absolute encoding & race faces
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Dense clustering around feature (asian/ white) therefore harder to recog
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Valentine 91
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Absolute encoding & race faces : learn to expand face space and decrease exemplar density
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Byatt &Rhodes 97
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right norm caracatures
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Byatt &Rhodes 97
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wrong norm caracatures
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Byatt &Rhodes 97
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Eng norm vs chinese caracatures : comparable
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See pics
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Byatt &Rhodes 97
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absolute encoding & race faces supported best
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Veridical
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undistorted
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exemplar
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absolute encoding model
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Shepherd, Ellis davies 77 distinctive faces
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multidimensional scaling ; faceshape, hairlength; sig dimens 3/4 dimensions encoding face
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Johnstone & Milne
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use multidimensional model and describe unidimensional scale - typical vs distinctive
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Light 79
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36 pics- make judge btw 2 pics, sim/ diff, spatial distance calculated - high distance from centre and distinctiveness correlation
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Light 79
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Mean dist from centre 4 typical = shorter than for distinctive
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Lewis 2004
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towards a unified account of face recog 15-22 dimensions
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Face space additional dimensions
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for facial featurs ie eyebrows
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Tredoux 2003
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own race bias
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Connors 61
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DNA vindicated prisoner for nearly decade, cell partner convicted for rape
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Wright 2001
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own race bias
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Kassin 2001
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90 experts agreed to ORB- reliable for scientific testimony
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Slone 2000
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conflicts over orb
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Messinger & brigham 2001
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small fx for orb - across data and across diff methods and conditions
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Wright 2001
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compared orb in Safrica and UK
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Chiriro & Valentine
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African cs UK sample - high vs low contact groups
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Chiriro & Valentine
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Black Zimbabwe- school - high contact vs rural village- missionary priest
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Chiriro & Valentine
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White UK -english village low contact vs english high contact
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Chiriro & Valentine
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results: Black high contact : no own race bias, low white and high white- both good at white , black low good at black
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Wright, Boyd &tredoux 2003
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bristol uni- low contact white vs uni capetown - white high contact, black high contact (uni 75% white academic staff)
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Wright, Boyd &tredoux 2003
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60 faces shown, 30 from old, 30 from new
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Wright, Boyd &tredoux 2003
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what about international students, time outside of the uni?, large black community in , media, expt fx,teachers more salient?
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Wright, Boyd &tredoux 2003
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both white and black accurate at white faces
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Wright 2001
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cognitive specialising ? Tested non students in shopping centre: Black - better at black than white
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Chance & Goldsein 96
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majority of studies find fx
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Kassin 89
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important that surveys are reliable
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Chance & Goldstein 96
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studies - replicatability
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Bothwell, Brigham & malpass 89
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80% samples ORB fx : replicatability
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Anthony 92
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consistency across racial groups : 2.5 x the variance of effect in white vs blacks
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Malpass 80
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generalisability : use standard recognition paradigm across studies
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Fallshore 95
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generalisability : used matching task
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Lewis 96
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also other race classification advantage: comes from facilitated classification process
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Tanaka & Taylor
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ORCA sim fx to novices in basic level categorisation fx
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Metanalysis- fx ORB
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own race: mirror fx, high hit and low false alarms, 2 discrimination accuracy 3 whites more likely ORB 4 study time = increases ORB 5 date of study (cohort fx)
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