Full Length ArticleUnderstanding mechanisms behind discrimination using diffusion decision modeling☆
Section snippets
Disentangling mechanisms behind discrimination
Consider a hiring manager who, when reviewing candidates to interview, encounters social information before decision-relevant information. This could occur when the information is presented first, such as a name suggesting race at the top of a resume, or prioritized in attention, such as first noticing a headshot indicating gender on a LinkedIn page (Jaeger, Sleegers, Evans, Stel, & van Beest, 2019). How might social information influence selection? One possibility is that social information
Signal detection model
Previous work has revealed two distinct approaches for reducing discrimination within the JBT. Using Signal Detection Theory (SDT), performance on the JBT has been modeled in terms of bias and noise.
Bias occurs when one social group is more likely to get a favorable response (e.g., acceptance to an honor society) than another social group. SDT analyses model bias through the criterion parameter, with relatively lower values indicating a lower bar for giving a favorable response to members of a
Diffusion decision model
The Diffusion Decision Model (DDM; Ratcliff, 1978; Ratcliff, Smith, Brown, & McKoon, 2016) is a sequential sampling model used to explain the process underlying decisions in two-choice tasks by simultaneously modeling choices and their speed. The DDM decomposes decisions into four components: relative start point (β), threshold separation (α), drift rate (δ), and non-decision time (τ). See Table 1 for a description of model parameters and Fig. 1 for a graphic summary.
In the JBT, faces and
Analytic approach
Although not the focus of our hypotheses, we first present analyses of acceptance decisions in order to put the results of the SDT and DDM models in context. Decisions were analyzed with multilevel logistic regression (Bates, Mächler, Bolker, & Walker, 2015; R Core Team, 2020).
Models include random intercepts for participants and targets, and random slopes by participant for attractiveness and qualification (Barr, Levy, Scheepers, & Tily, 2013; Judd, Westfall, & Kenny, 2012). Interventions were
Methods
We reanalyzed three studies from Axt and Lai (2019) that tested interventions to reduce discrimination in the JBT. We included the timed (Study 2b), deliberative (Study 4), and awareness (Study 5) interventions. Each study included a control condition for comparison. These interventions were chosen given the prior evidence that each has a distinct (and even opposing) effect on the decisions made in the JBT (2019), with the timed and deliberative interventions impacting the number of errors made
Methods
We replicated Study 1 using a randomized experiment. This study was pre-registered, using α = 0.05 in all analyses: https://osf.io/bwnj2.
General discussion
The current work examined mechanisms through which socially biased judgments emerge and are reduced in the JBT. More physically attractive candidates were more likely to be accepted than less physically attractive candidates in control conditions. This discrimination was reflected both in an initial preference for physically attractive applicants (a relative start point effect) and attractiveness being incorporated into decisions (a drift rate effect). The latter is consistent with two
Conclusion
The present work reveals how discrimination can occur due to imperfect accuracy and the use of social information in judgment. Intervention strategies had unique impacts on the decision process, and DDM analyses clarified how these strategies reduced or exacerbated the impact of social information.
Subsequent research may look to other methods to validate and extend these findings. For instance, the drift rate results suggest participants completing the standard JBT use attractiveness
Data availability
All data and materials are available on the Open Science Framework (https://tinyurl.com/jbtddm). All measures, manipulations, and exclusions are disclosed.
Declaration of Competing Interest
This research was partly supported by Project Implicit. J. R. Axt is Director of Data and Methodology for Project Implicit, Inc., a nonprofit organization with the mission to “develop and deliver methods for investigating and applying phenomena of implicit social cognition, including especially phenomena of implicit bias based on age, race, gender, or other factors.”
Jordan Axt is an assistant professor at McGill University. His research explores how people form and express intergroup bias in attitudes and behavior.
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Jordan Axt is an assistant professor at McGill University. His research explores how people form and express intergroup bias in attitudes and behavior.
David Johnson is a postdoctoral researcher at the Lab for Applied Social Science Research at the University of Maryland. His research employs computational models and secondary data analyses to study the psychological processes that underlie decisions
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This paper has been recommended for acceptance by Michael Kraus.