Publikation
Inverse decision-making using neural amortized Bayesian actors
Dominik Straub; Tobias F. Niehues; Jan Peters; Constantin A. Rothkopf
In: The Thirteenth International Conference on Learning Representations, ICLR 2025, Singapore, April 24-28, 2025. International Conference on Learning Representations (ICLR), Pages 1-22, OpenReview.net, 2025.
Zusammenfassung
Bayesian observer and actor models have provided normative explanations for
many behavioral phenomena in perception, sensorimotor control, and other areas
of cognitive science and neuroscience. They attribute behavioral variability and
biases to interpretable entities such as perceptual and motor uncertainty, prior
beliefs, and behavioral costs. However, when extending these models to more
naturalistic tasks with continuous actions, solving the Bayesian decision-making
problem is often analytically intractable. Inverse decision-making, i.e. performing
inference over the parameters of such models given behavioral data, is computa-
tionally even more difficult. Therefore, researchers typically constrain their models
to easily tractable components, such as Gaussian distributions or quadratic cost
functions, or resort to numerical approximations. To overcome these limitations,
we amortize the Bayesian actor using a neural network trained on a wide range
of parameter settings in an unsupervised fashion. Using the pre-trained neural
network enables performing efficient gradient-based Bayesian inference of the
Bayesian actor model’s parameters. We show on synthetic data that the inferred
posterior distributions are in close alignment with those obtained using analytical
solutions where they exist. Where no analytical solution is available, we recover
posterior distributions close to the ground truth. We then show how our method
allows for principled model comparison and how it can be used to disentangle
factors that may lead to unidentifiabilities between priors and costs. Finally, we
apply our method to empirical data from three sensorimotor tasks and compare
model fits with different cost functions to show that it can explain individuals’
behavioral patterns.