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131 Cards in this Set

  • Front
  • Back
Johnston & Ellis 95
Uniform (same as adult so uniform density
Differential model kids - fewer dimensions adult so same density
5 yrs
D fx 4 classifcation
no fx 4 recog- consistent w norm-uniform or exemplar differential
Brennan 85
Caracature generator
Rhodes 87
recog = c> Veridical> anti- c
best recog 16% C
Benson & Perrett 91
Add texture
best recog 4.4%
Rhodes 87
Prior face space
C fx = mem exaggerated like C?
C make d features more salient
Stevenage 95
-c = increae vector length (angle same)
more D so easier to recog
Tanaka and simon 96
build neural net model C fx
average from 3 vectors = norm
exaggerate to get C
c= better recog
supports norm based model
Carey (2002)
lateral caricatures
norm account
incorrect generate laterals
Rhodes 98
lateral = btw anti-c and veridical
non- consistent norm act
Lewis & Johnson 98
Lateral C no -ive fx inconsisten euclidean distance for recog
Exemplar : Rhodes 93
Absolute coding
makes image more Diff
more competing reps but more diff stored rep
balance= c- better veridical
Lewis and Johnston 99
Veronoi model
Boyatt & Rhodes 98
Other race faces and caricatures
C advantage when exaggerate away other race norm
Lee & Perrett 97
brief presentations caracture adv at 40% exaggerated = brief exposure fx
Probs w voronoi
identity regions- fills whole veroni, therefore false positive for unfamiliar face
face space r
similar to voroni - comparision btw exemplars
Nosofsky 86
generalized context model
Valentine ferrara 91
generalized context model linked to face space
Generalized context model
uses summed similarity rule: probability of response = sum of probe to exemplars devided by sum of similarity of probe to all exemplars
Probs w Gen. Context model
highly distinctive - high caracature recognition & unable to account for caracature fx
Zaki & Nosofsky 2001
P's learned new faces, response to morph face greater than parent faces of morph
Lee Byatt & Rhodes
GCM- explains caracature fx : 16 famous faces - recog carac, veridicals, anticarac
GCM- hig recog though extremely caracatured
- wrong as face stops looking like face after while
Lewis 99- familiarity
people seen a lot are faster than less salient
new face = new encounter ed : overcomes false positive rct in voronoi
input randomly generated normally distributed data ; add parameters
Shephard 77
multidimensional scaling on pattern of clustering, 100 faces 32 most important faces rated, 10/11 factors account for variance
lewis -face space-r
15-22 dimensions
Turk & Pentland
similar to no of eigenfaces employed to generate recognisable face
Light 79
faces rated as highly distinctive - easier to recog
Burton, Bruce & Dench 94
distinctiveness= dist btw face representation and av face
Burton & Vokey
Typicality paradox
densely populated
if seen in 2D one see highly typical than distinctiveness therefore distinctiveness ratings should be skewed
larger no of dimensions the smaller the skew
too large dimensions
decreases variability of distinctiveness
larger skew in ratings may b due to fewer dimensions used to process 3/4 faces that frontal
Benson & Prett 94
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
Bothwell brigham and malpass 89
other race harder to recog than own
own race faces
high exposure fx
valentine 91 - face space and race fx
other race faces clustered far away
norm based race faces
hard to disting btw norm vectors and other race faces
Valentine & endo 92 -exemplar based w races
better for understand own race bias
Valentine & endo 92 -exemplar based w races
other faces normally distrib at distance from own race average
Valentine & endo 92 -exemplar based w races
have less contact therefore less representations
destinctions btw races
blue eyes etc
Byatt & Rodes 99
disagreed w norm based model for caracatured advantage for other race faces - predicted
Byatt & Rodes 99
agreed w absolute -coding - caracature advantage great for other race faces when away from norm
Valentine & Bruce : face categorisation
: typicality advantage
Valentine & Bruce 86
intact vs jumbled faces ; all known exemplars contribute to activation ;
Valentine & Bruce 86
equation for activation: r categorisation (x) = sum Ai { acti(x)}
Valentine & Bruce 86
no within -category competition
sum of activations of exemplars
threshold dependent on non -face probes (scrambled)
Lewis 98
found typicality fx for non-prob cat faces vs human faces
Valentine & Bruce 86
rctn time for familiarity slower than categ
unfamiliar : lewis & Johnston 99
veronoi model cant deal w unfamiliar faces ; probe faces recog as familiar person but at diff speeds (depending on cell distance)
Valentine 2001
threshold activation
Lewis & Johnson 97
face learning memory task
Face learning memory task
unfam face, test faces (target vs distractor) ; famous not included therefore remove all previously known faces from criteria
Face learning memory task
accounts for false positive advantage
Valentine 91
typical unfamiliar faces- take longer to be responded to than distinctive unfamiliar
Valentine 91
Typical produces high false positives rate
Busey 98
location of face space det factors like distinct
Bartlett & hurry 80
typical face: familiarity, feeling of "oldness"
Typicality : Vokey & Read 92
attractiveness, familiarity, likeability. Memorability,
context and typicality
context induced- prior exposure
Context free
structurally induced- memory not indexed
Bartlett 84
distinctive have high familiarity- prior exposure, easy to encode , context-free
O'toole, Deffenbacher 94
digitised pics, put into neural netwrk, white vs asian, memorisability small, local features used, familiarity global aspects used
Uttal Barauch & Allen 95
high spatial freq- used for discrim : global shape info underlies familiarity
Race bias: brigham malpass 82
field studies
Lab studies of race bias
Messiner & brigham 2001
Goldstien & chance
race fx : tested white children and adults recog white japenese adults - scores increase w age, white better than japenese recognised
MacLin and Malpass 2001
cross race fx sim from 5 yrs to adulthood
Wright boyd & Tredoux 2003
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
Malpass& Kravitz 69
prob w data: low recog performance for other race faces- differential experience
O'toole 95 perceptual expertise can help

Anthony 92
other races recog greater than own
Absolute encoding
distinctiveness and change in exemplar density
caractures & absolute encoding
caractures more distinctive
caractures & absolute encoding
caractures high sep from other points (potential distractors) = distinctive
decrease exemplar density
Rhodes 87
caracatures don’t always enhance performance - decrease in exemp density doesn't work sometimes
Rhodes & Mclean 90
norm doesn't rep true tendency
Rhodes & Mclean 90
low contact- compare far away from own norm
Rhodes & Mclean 90
high contact norm shifts to rep all faces in space
Absolute encoding & race faces
dimensions tuned through perceptual learning
Absolute encoding & race faces
features w disting btw exemplars doesn't related to other races (ie black)
Absolute encoding & race faces
Dense clustering around feature (asian/ white) therefore harder to recog
Valentine 91
Absolute encoding & race faces : learn to expand face space and decrease exemplar density
Byatt &Rhodes 97
right norm caracatures
Byatt &Rhodes 97
wrong norm caracatures
Byatt &Rhodes 97
Eng norm vs chinese caracatures : comparable
See pics

Byatt &Rhodes 97
absolute encoding & race faces supported best
absolute encoding model
Shepherd, Ellis davies 77 distinctive faces
multidimensional scaling ; faceshape, hairlength; sig dimens 3/4 dimensions encoding face
Johnstone & Milne
use multidimensional model and describe unidimensional scale - typical vs distinctive
Light 79
36 pics- make judge btw 2 pics, sim/ diff, spatial distance calculated - high distance from centre and distinctiveness correlation
Light 79
Mean dist from centre 4 typical = shorter than for distinctive
Lewis 2004
towards a unified account of face recog 15-22 dimensions
Face space additional dimensions
for facial featurs ie eyebrows
Tredoux 2003
own race bias
Connors 61
DNA vindicated prisoner for nearly decade, cell partner convicted for rape
Wright 2001
own race bias
Kassin 2001
90 experts agreed to ORB- reliable for scientific testimony
Slone 2000
conflicts over orb
Messinger & brigham 2001
small fx for orb - across data and across diff methods and conditions
Wright 2001
compared orb in Safrica and UK
Chiriro & Valentine
African cs UK sample - high vs low contact groups
Chiriro & Valentine
Black Zimbabwe- school - high contact vs rural village- missionary priest
Chiriro & Valentine
White UK -english village low contact vs english high contact
Chiriro & Valentine
results: Black high contact : no own race bias, low white and high white- both good at white , black low good at black
Wright, Boyd &tredoux 2003
bristol uni- low contact white vs uni capetown - white high contact, black high contact (uni 75% white academic staff)
Wright, Boyd &tredoux 2003
60 faces shown, 30 from old, 30 from new
Wright, Boyd &tredoux 2003
what about international students, time outside of the uni?, large black community in , media, expt fx,teachers more salient?
Wright, Boyd &tredoux 2003
both white and black accurate at white faces
Wright 2001
cognitive specialising ? Tested non students in shopping centre: Black - better at black than white
Chance & Goldsein 96
majority of studies find fx
Kassin 89
important that surveys are reliable
Chance & Goldstein 96
studies - replicatability
Bothwell, Brigham & malpass 89
80% samples ORB fx : replicatability
Anthony 92
consistency across racial groups : 2.5 x the variance of effect in white vs blacks
Malpass 80
generalisability : use standard recognition paradigm across studies
Fallshore 95
generalisability : used matching task
Lewis 96
also other race classification advantage: comes from facilitated classification process
Tanaka & Taylor
ORCA sim fx to novices in basic level categorisation fx
Metanalysis- fx ORB
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)