A Neural Network Model of Evaluation in Attitudes and Stereotypes

1. A Neural Network Model of Evaluation in Attitudes and Stereotypes • Stephen J. Read, Phillip Ehret, & Brian Monroe • University of Southern California

2. Iterative Reprocessing (IR) modelCunningham, Zelazo, Packer, & Van Bavel (2007) • Challenges dual-attitude and dual system models • Proposes that evaluations do not progress in two distinct pathways, one automatic and one controlled, but instead evolve as information about valenced stimuli is reprocessed. • Stimulus initially processed by lower-order evaluative processes • On subsequent iterations, higher-order semantic and associated evaluative processes will be recruited to recalculate and modify the initial evaluation. • Using Emergent 5.1.0 neural network modeling software (Aisa, Mingus, & O'Reilly, 2008), we have constructed a neural network that successfully represents several key concepts of the IR model.

3. Cunningham & Zelazo (2007)

4. Cunningham & Zelazo (2007)

5. Issues Addressed • Attitudes Stored or Constructed? • How many attitudes? • Implicit versus Explicit Attitudes: Dual Attitudes? • Dual Process Models of Attitudes • How Many Processes/Systems?

6. Our Model Overview • Simulates evaluation of individuals who vary on race, gender, occupation, situation, and traits • An eleven-layer network incorporating layers for: • Stimulus inputs • Basic Race and Gender identification • Higher-order semantic knowledge • Context or Situation • Occupation • Attributes • Positive and negative evaluation • Represented separately (Cacioppo, Gardner, & Berntsen, 1997)

7. Processing overview

8. Processing Hierarchy • Highly perceptual information, such as Race and Gender, processed early • Associated evaluation processed early • More complex social information, such as occupation (e.g., doctor, lawyer, gang member), or social situations (boardroom, hospital) processed much later • Associated evaluation processed later

9. Early processing of perceptual cues • Ito and Urland (2003). JPSP • ERPs to pictures of Black and White males and females. • attention preferentially directed to Black targets very early in processing (by about 100 ms after stimulus onset. • Attention to gender also emerged early but about 50 ms later than attention to race. • Face processing literature suggests that face-structure processed by about 170 msec.

10. Processing Overview • Network presented with visual observations about ‘individuals’ consisting of features such as skin tone, hair length, clothing, and features of their physical environment • Network quickly identifies Race, either black or white, and the Gender of the individual, and initially evaluates the individual based on these two characteristics. • Initial evaluation of race and sex is part of the first iterations of the stimuli and likely occurs in limbic structures such as the amygdala.

11. Processing Overview (continued) • As activation continues to spread, the stimuli are reprocessed in later iterations in higher-order semantic layers representing the orbitofrontal cortex, anterior cingulate cortex, and lateral prefrontal cortex that recognize higher level concepts such as physical location (e.g., office or street) and profession. • Individual’s profession activates stereotypic attributes, such as intelligent for a doctor, or violent for a gang member. • Attributes then activate associated positive or negative evaluations, revising overall evaluations in the positive and negative evaluation layers representing the amygdala.

13. Layers • Input Layer: 1 X 17 • Race Layer Gender Layer

14. Layers • Context Layer • Professions Layer • Attributes Layer

15. Layers • Amygdala (Evaluative System) • One positive and one negative layer • Scalar layers • Values: 0-1 • Represented across 12 nodes

18. Training • Network trained in two steps. • Initial training to establish a simple stereotype based on perceptual cues • highly negative evaluations of black males, • slightly less negative evaluations of black females • highly positive evaluations of white males and females. • Learning then nearly reduced to zero for basic race and sex evaluations to maintain the stereotype • A later training environment is used to allow learning of appropriate higher-level semantic representations of the stimuli and the appropriate attitudes and corresponding evaluations.

19. Primary Training • Only race, gender, conjunction, and amygdala layers learning. • Only presented with black and white males and females • 1:1:1:1 training ratio • Because this layer lets you directly train level of evaluation don't need to vary frequency

20. Secondary Training Evaluations

21. Secondary Training • 16 unique ‘individuals’ presented in a random sequence • Black and white; male and female; doctor, gang member, athlete, or businessman • Also 2 black and 2 white males. And 1 white and 1 black female. • 118 presentations per epoch • 72 male, 46 female (61% male) • 82 white, 36 black (70% white)

22. Secondary Training Attributes

24. Testing • Network then tested with specific individuals, such as a black male doctor. • By monitoring the evolution of both the positive and negative evaluation, we can see clear effects of later iterations and revisions of evaluations.

29. Context - before Person Information • A number of recent studies (e.g., Barden, Maddux, Petty, & Brewer, 2004; Correll, Wittenbrink, Park, Judd, & Goyle, 2011; Van Bavel, Xiao, & Cunningham, 2012; Wittenbrink, Judd, & Park, 2001) suggest that preceding context can strongly influence impact of person information • Example: • Black Doctor in Hospital • What happens to time course of evaluation if Hospital context is presented before the rest of the Doctor information?

30. Context - before Person Information • All cues for Black Doctor presented simultaneously • VERSUS • Hospital cues are presented and processing proceeds for 30 cycles. • Then person cues of Black Doctor are presented and processing proceeds for 80 cycles

32. Discussion • Issues

33. Constructed vs. Stored • Stable attitude stored in connection weights, but evaluation of specific instance constructed

34. How many attitudes? • Separates stable attitude (weights) from constructed evaluation • Large, if not infinite, number of evaluations that can be constructed, depending on extent of processing and the context cues

35. Implicit versus Explicit Attitudes: Dual Attitudes? • Continuum, depending on extent of processing • Evaluation integrates multiple processes • Explicit not separate from implicit, but partially based on it

36. How Many Processes/Systems? • Multiple processing systems feed into single evaluation system