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Causal inference in cue combination is to decide whether the cues have asingle cause or multiple causes. Although the Bayesian causal inference modelexplains the problem of causal inference in cue combination successfully, howcausal inference in cue combination could be implemented by neural circuits, isunclear. The existing method based on calculating log posterior ratio withvariable elimination has the problem of being unrealistic and task-specific. Inthis paper, we take advantages of the special structure of the Bayesian causalinference model and propose a hierarchical inference algorithm based onimportance sampling. A simple neural circuit is designed to implement theproposed inference algorithm. Theoretical analyses and experimental resultsdemonstrate that our algorithm converges to the accurate value as the samplesize goes to infinite. Moreover, the neural circuit we design can be easilygeneralized to implement inference for other problems, such as themulti-stimuli cause inference and the same-different judgment.

Sampling-based Causal Inference in Cue Combination and its Neural Implementation