Reverse Correlation Methods for Social Cognition
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1 Reverse Correlation Methods for Social Cognition Ron Dotsch Daniel Wigboldus Ad van Knippenberg Gent, March 9, 2010
2 Interspecies differences in face categorization
3 Reverse Correlation Classification image technique (Ahumada, 1996)
4 Reverse Correlation Classification image technique (Ahumada, 1996) Responses to stimuli in experiment contain information about how task is performed
5 Reverse Correlation Classification image technique (Ahumada, 1996) Responses to stimuli in experiment contain information about how task is performed Randomly (instead of systematically) vary stimuli on a lot of (instead of specific) dimensions
6 Reverse Correlation Classification image technique (Ahumada, 1996) Responses to stimuli in experiment contain information about how task is performed Randomly (instead of systematically) vary stimuli on a lot of (instead of specific) dimensions Use correlation between stimulus features and response pattern to infer decision criteria (classification image)
7 Reverse correlation task Reverse correlation image classification task (Dotsch, Wigboldus, Langner, & van Knippenberg, 2008; Mangini & Biederman, 2004)
8 Reverse correlation task Reverse correlation image classification task (Dotsch, Wigboldus, Langner, & van Knippenberg, 2008; Mangini & Biederman, 2004)
9 Reverse correlation task Reverse correlation image classification task (Dotsch, Wigboldus, Langner, & van Knippenberg, 2008; Mangini & Biederman, 2004)
10 Reverse correlation task A 6 Base + noise Base - noise
11 Classification images Chinese Moroccan
12 Classification images Chinese Moroccan
13 Classification images Chinese Moroccan
14 Noise generation y = sin x y = sin x at 90º
15 Noise generation y = sin x y = sin x at 90º
16 Noise generation 4092 parameters (contrasts/amplitudes)
17 Mangini & Biederman (2004)
18 Humans vs. Baboons Martin-Malivel, Mangini, Fagot, and Biederman (2006)
19 Humans vs. Baboons Martin-Malivel, Mangini, Fagot, and Biederman (2006)
20 In more detail
21 Reverse Correlation History Auditory work (Ahumada & Lovell, 1971) Low level visual perception: humans differ from ideal observers (Ahumada, 1996; Beard & Ahumada, 1998) Sparked host of studies on visual perception: Letter discrimination (Watson & Rosenholtz, 1997) Perceptual learning (Knoblauch, Thomas, & D Zmura, 1999) Illusory contours (Gold, Murray, Bennett, & Sekuler, 2000) Many more! (see Eckstein & Ahumada, 2002)
22 Reverse Correlation History Higher order visual perception (face processing):
23 Reverse Correlation History Higher order visual perception (face processing): Bubbles (Gosselin & Schyns, 2001)
24 Reverse Correlation History Higher order visual perception (face processing): Bubbles (Gosselin & Schyns, 2001) Mangini & Biederman (2004)
25 Reverse Correlation History Higher order visual perception (face processing): Bubbles (Gosselin & Schyns, 2001) Mangini & Biederman (2004) And now: Social cognition?
26 Visualizing bias Dotsch, Wigboldus, Langner, & van Knippenberg (2008)
27 Chinese, Japanese, or Korean?
28 Chinese, Japanese, or Korean? Japanese
29 Chinese, Japanese, or Korean?
30 Chinese, Japanese, or Korean? Chinese
31 Chinese, Japanese, or Korean?
32 Chinese, Japanese, or Korean? Chinese
33 Image classification task Chinese Moroccan
34 Image classification task Low Moderate High
35 Image classification task Low Moderate High
36 Part 2:Trait ratings (N = 70) Criminal Trustworthy Low Moderate High -2 Low Moderate High ST IAT ST IAT F(2, 68) = 39.00, p <.001, partial η! =.53 F(2, 68) = 43.95, p <.001, partial η! =.56
37 Criminal Classification Image
38 Moroccan + Criminal + =
39 Moroccan + Criminal + =
40 Criminality added
41 Original
42 Results % Categorized as Moroccan Low Prejudice High Prejudice Moroccan Faces Criminal Moroccan Faces GLM with prejudice as continuous variable: Prejudice main effect: F(1, 76) = 7.45, p <.01, partial η! =.09 Face main effect: F(1, 76) = 24.35, p <.01, partial η! =.24 Face x Prejudice interaction: F(1, 76) = 5.58, p =.02, partial η! =.07.
43 Other Reverse Correlation Examples
44 Memory bias for attractive alternatives Karremans, Dotsch, & Corneille (in prep)
45 Conditions (N = 82, all females) Attractive Unattractive
46 Base image (50% morph)
47 Main effect Attractive
48 Main effect Unattractive
49 Interaction (within attractive condition) No relation > relation Relation > no relation
50 Interaction (within unattractive condition) No relation > relation Relation > no relation
51 Ingroup projection Imhoff, Dotsch, Bianchi, Wigboldus, & Banse (in prep.)
52 Portugal vs. Germany Germany's striker Miroslav Klose and Portuguese defender Pepe argue during the Euro 2008 Championships quarter-final football match between Portugal and Germany at the St. Jakob Park Stadium on June 19, 2008 in Basel, Switzerland.
53 Germany
54 Portugal
55 Base image (50% morph)
56 Results German European (N = 53)
57 Results Portuguese European (N = 50)
58 General discussion Classification image is a index of decision strategy, not necessarily a picture in the head Can be spontaneous Not necessarily exploratory Holds lots of promise for social cognition research
59 Thank you! My advisors: Daniel Wigboldus Ad van Knippenberg My collaborators: Oliver Langner Alexander Todorov Johan Karremans Olivier Corneille Contact me: Roland Imhoff Mauro Bianchi
60 Dimensions of face evaluation Dotsch, Todorov, & Wigboldus (in prep)
61 Primary trait dimensions Trustworthy and Untrustworthy Dominant and Submissive
62 Primary trait dimensions Trustworthy and Untrustworthy Dominant and Submissive
63 Primary trait dimensions Trustworthy and Untrustworthy Dominant and Submissive
64 Threat + = Dominant Untrustworthy
65 Hi-Speed Threat 100 ms 500 ms
66 High Speed Threat 100 ms
67 High Speed Threat 500 ms
68 High Speed Threat 500 ms > 100 ms
69 Thank you! My advisors: Daniel Wigboldus Ad van Knippenberg My collaborators: Oliver Langner Alexander Todorov Johan Karremans Olivier Corneille Contact me: Roland Imhoff Mauro Bianchi
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