Some simple max-lik fitting (nice tutorial: cran.r-project.org/web/packages...), and we can see the best fit to Laura's data indeed comes from models where context-related foils lead to lower drift rate---but ALSO faster non-decision processing! These foils seem harder to ignore AND faster to read.

Enter Ratcliff's diffusion model of decision-making, tried-and-true and easy to work with in R (thanks to @singmann.bsky.social and the rtdists package). Harder decisions can be modeled as lower drift rates, which have a joint effect, widening the distribution of expected RTs and increasing errors.

We focus on how Maze measures can be affected by the difficulty of deciding between target and foil. In Laura's dissertation, she found a cool pattern: when you use a foil associated with the literal meaning of a prior metaphor ("early bird" → "fly"), you get slower RTs, and more errors.

Sad to be missing AMLaP, home with some surprise COVID, but very thankful to my co-author Laura Pissani for going solo on our Saturday talk on diffusion models for the Maze task! For anyone else stuck at home, let me tell you here about what we've learned by modeling Maze RTs as decision-making.

Gorgeous example of a preposition pruning error in what would have already been a confusing and wonderful RC. (Context: www.nytimes.com/athletic/572...)