Model parameters were estimated w/Bayesian stats. Psych data are always nested (eg. associations are held by individuals but thresholds are individual x condition). Parameters in the model was drawn as shown below.
JAGS/R code to run our model and a video tutorial provided! osf.io/cv2a5/ (11/13)

Model parameters also do a great job predicting self-reported behavior towards target groups, showing the predictive validity of the model (8/13)

But there's more to the results! Here's a what they look like for the Race IAT. Check out the preprint for full discussion (7/13)

But there's more to the results! Here's a what they look like for the Race IAT. Check out the preprint for full discussion (7/13)

In an IAT, the evidence accumulated for one response ("categorize this stimulus as Bad/Black") builds up faster if those concepts are aligned & are "pulling" together. Since the model involves concepts pulling together/away from each other, we call it the tug-of-war model..(4/13)

..the angle between axes representing cognitive concepts (eg., angle between valence & race axes). If a category is not associated with positivity/negativity, the angle should be 90° (orthogonal concepts). Similarly, positive and negative associations will create (0-90°)..(2/13)