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Emotion expression salience and racially biased weapon identification: A diffusion modeling approach

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Abstract

Racial stereotypes are commonly activated by informational cues that are detectable in people’s faces. Here, we used a sequential priming task to examine whether and how the salience of emotion (angry/scowling vs. happy/smiling expressions) or apparent race (Black vs. White) information in male face primes shapes racially biased weapon identification (gun vs. tool) decisions. In two experiments (Ntotal = 546) using two different manipulations of facial information salience, racial bias in weapon identification was weaker when the salience of emotion expression versus race was heightened. Using diffusion decision modeling, we tested competing accounts of the cognitive mechanism by which the salience of facial information moderates this behavioral effect. Consistent support emerged for an initial bias account, whereby the decision process began closer to the “gun” response upon seeing faces of Black versus White men, and this racially biased shift in the starting position was weaker when emotion versus race information was salient. We discuss these results vis-à-vis prior empirical and theoretical work on how facial information salience moderates racial bias in decision-making.

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Notes

  1. We did not derive clear predictions about threshold separation and non-decision time, but we report results pertaining to these parameters for completeness.

  2. Notably, an evidence accumulation account better explains racial bias in the first-person shooter task (FPST; Correll et al., 2015; Johnson et al., 2018; Pleskac et al., 2018). For a discussion of procedural differences between the FPST and the WIT, see Todd et al. (2021).

  3. The emotion expression and apparent race of these face stimuli were likely construed unambiguously. In the face-categorization task in Experiment 2, emotion expression and race were both “correctly” classified on ≥ 95% of trials, supporting the assumption that both sources of information were clear and easy to identify (see Tables S7 and S8, OSM).

  4. The only exception was for the LMEM on incorrect response times reported in the OSM. Due to boundary fit conditions, we removed the by-stimulus random intercept from the model.

  5. Although the LMEM on error rates in Experiment 2 afforded the inclusion of by-participant random slopes for Race Prime, we chose to prioritize consistency within and across experiments over that single model’s random effects structure. Inclusion versus exclusion of the additional random effect did not meaningfully change the results.

  6. As highlighted in Table 1, the threshold separation and relative start point parameters cannot be identified across conditions of Target Object: The relative start point parameter reflects the position at which participants are closer to a gun versus tool decision at target onset; the threshold separation parameter reflects the extent to which evidence must be accumulated to reach a gun versus tool decision, presumably determined before target onset. Presumably both the extent of evidence accumulated from the target object (i.e., drift rate) and the processing time prior to a response being recorded (i.e., non-decision time) may vary by Target Object.

  7. We thank an anonymous reviewer for raising this point.

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Funding

This research was facilitated by NSF grant BCS-1764097 (awarded to ART).

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Correspondence to Samuel A. W. Klein or Andrew R. Todd.

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Klein, S.A.W., Todd, A.R. Emotion expression salience and racially biased weapon identification: A diffusion modeling approach. Psychon Bull Rev (2024). https://doi.org/10.3758/s13423-024-02526-z

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