We computed a DDM to test H3 & H4 on the reaction time data8, see Figure 6A. A hierarchical
form of this model was built accounting for inter-subject variability with a varying intercept and a
population-level effect for each Status and an interaction term for Description for each 𝜏, 𝜈 and 𝛼.
We inspected the regression weights, see Figure 6B, for differences in Status and Description for
boundary separation 𝛼, see Figure 7B, to see whether the difference in reaction time comes from a
change in the participant’s strategy, e.g., prioritizing speed over the accuracy. We found that in the
sham-AI condition, participants had slightly larger boundary separation, 𝛼, making them slightly
more conservative as compared to the no-AI condition. However, we also found that 𝜈 (drift rate)
see Figure 7A, was higher for sham-AI as compared to no-AI. Thus, information accumulation was
relatively faster in the sham-AI condition, see Figure 6B. Similarly, 𝜏 was also affected by Status
AI enhance sour performance