The role of recoding in implicit social cognition: Investigating the scope and interpretation of the ReAL model for the implicit association test

Improving the reliability and validity of the IAT with a dynamic model driven by similarity

The Implicit Association Test (IAT), like many behavioral measures, seeks to quantify meaningful individual differences in cognitive processes that are difficult to assess with approaches like self-reports. However, much like other behavioral measures, many IATs appear to show low test-retest reliability and typical scoring methods fail to quantify all of the decision-making processes that generate the overt task performance. Here, we develop a new modeling approach for IATs based on the geometric similarity representation (GSR) model. This model leverages both response times and accuracy on IATs to make inferences about representational similarity between the stimuli and categories. The model disentangles processes related to response caution, stimulus encoding, similarities between concepts and categories, and response processes unrelated to the choice itself. This approach to analyzing IAT data illustrates that the unreliability in IATs is almost entirely attributable to the methods used to analyze data from the task: GSR model parameters show test-retest reliability around .80-.90, on par with reliable self-report measures. Furthermore, we demonstrate how model parameters result in greater validity compared to the IAT D-score, Quad model, and simple diffusion model contrasts, predicting outcomes related to intergroup contact and motivation. Finally, we present a simple point-and-click software tool for fitting the model, which uses a pre-trained neural network to estimate best-fit parameters of the GSR model. This approach allows easy and instantaneous fitting of IAT data with minimal demands on coding or technical expertise on the part of the user, making the new model accessible and effective.

No elephant in the room: The incremental validity of implicit self-esteem measures

Correlational research aiming to validate measures and the construct of implicit self-esteem (ISE) has produced heterogeneous results in the past. We argue that this might be caused by two underappreciated obstacles: the situational malleability and construct irrelevant variance in conventional ISE measures. To minimize these problems, we applied process and latent state-trait modeling to Implicit Association Test and Name Letter Task data collected on four occasions across six weeks in a preregistered online study (initial N = 360, final N = 302). We investigated the relation of supposed trait ISE parameters with trait explicit self-esteem (ESE) and criteria. Results indicated no latent trait correlation among the different supposed indicators of ISE, small latent trait correlations of indicators of ISE and ESE, and little incremental validity of the supposed ISE measures in predicting potential criterion measures over and above ESE. These findings align with previous critical evaluations regarding the supposed measures of ISE and the conceptual validity of ISE as an association. Implications for future research on ISE are discussed.