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A Framework for Testing Identifiability of Bayesian Models of Perception
Luigi Acerbi · Wei Ji Ma · Sethu Vijayakumar

Thu Dec 11 11:00 AM -- 03:00 PM (PST) @ Level 2, room 210D

Bayesian observer models are very effective in describing human performance in perceptual tasks, so much so that they are trusted to faithfully recover hidden mental representations of priors, likelihoods, or loss functions from the data. However, the intrinsic degeneracy of the Bayesian framework, as multiple combinations of elements can yield empirically indistinguishable results, prompts the question of model identifiability. We propose a novel framework for a systematic testing of the identifiability of a significant class of Bayesian observer models, with practical applications for improving experimental design. We examine the theoretical identifiability of the inferred internal representations in two case studies. First, we show which experimental designs work better to remove the underlying degeneracy in a time interval estimation task. Second, we find that the reconstructed representations in a speed perception task under a slow-speed prior are fairly robust.

Author Information

Luigi Acerbi (University of Helsinki)

Assistant professor Luigi Acerbi leads the *Machine and Human Intelligence* group at the Department of Computer Science of the University of Helsinki. His research spans Bayesian machine learning and computational and cognitive neuroscience. He is member of the *Finnish Centre for Artificial Intelligence* (FCAI) and of ELLIS (*European Laboratory for Learning and Intelligent Systems*).

Wei Ji Ma (New York University)
Sethu Vijayakumar (University of Edinburgh)

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