📢 New preprint!
How do humans learn from arbitrary, abstract goals? We show that, when goal spaces can be compressed, costly working-memory processes give way to internalized reward functions, enabling efficient goal-dependent reinforcement learning. @annecollins.bsky.social arxiv.org/abs/2509.06810

✅ Works with tractable & intractable models
✅ Handles continuous & discrete latent spaces
✅ Applicable to real-world datasets.

We tested our method on various cognitive models, including reinforcement learning models, Bayesian models, and GLM-HMM. A collective effort with @drjingjing.bsky.social @wdt.bsky.social @annecollins.bsky.social‬

New paper out in Behavioral Research Methods! We introduce a simulation-based method using RNNs to infer trial-varying latent variables from computational cognitive models.
Link: doi.org/10.3758/s134...
#ComputationalCognitiveModeling #SBI