UC Davis

Thinking about other people often requires complex cognitive processing to integrate their many and varied features. The three papers I present further our understanding of how we integrate this information into coherent social judgments. In Chapter 1, I tested whether mental representations of others’ appearance rapidly update to new information. After learning to ascribe valenced behaviors to a target person and subsequently visualizing his face in a reverse-correlation task, participants learned new information that was (a) counter-attitudinal and diagnostic about his character or (b) neutral and non-diagnostic before generating a second visualization. Visualizations at Time 2 assimilated to counter-attitudinal information, suggesting that representations of others’ appearance may rapidly update to new information. In Chapter 2, I examined whether and how the salience of emotion expression (scowling, smiling) or race (Black, White) cues shapes racially biased weapon identification (gun, tool). Across two manipulations of salience, racially biased weapon identification was weaker when the salience of emotion versus race was heightened. Using diffusion modeling, I tested competing cognitive accounts of this effect. Consistent support emerged for an initial bias account, whereby the decision process initiates closer to “gun” responses upon seeing Black (vs. White) faces, and this racially biased shift in the starting position is weaker when emotion (vs. race) is salient. In Chapter 3, I developed a solution to the inability of conventional measures of social judgment to distinguish the unique contributions of multiple features (e.g., social categories, behaviors). In particular, I introduced a computational model to separately measure the use of multiple features underlying social judgments. Using data from a judgments task in which emotion and sex cues varied in target faces, I initially validate the model’s capacity to measure the use of those cues and demonstrated how the model be applied to answer long-standing questions in the field.