Recurrent neural networks with explicit representation of dynamic latent variables can mimic behavioral patterns in a physical inference task
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s41467-022-33581-6.pdf
Description
Published version
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2.8 MB
Format
Adobe PDF
Checksum (MD5)
72a519d50568f25f0f5fb6258b0384f8
Author(s)
Rajalingham, Rishi
Piccato, Aída
Jazayeri, Mehrdad
Date Issued
2022
Journal
Nature Communications
Publisher
Springer Science and Business Media LLC
Citation
Rajalingham, Rishi, Piccato, Aída and Jazayeri, Mehrdad. 2022. "Recurrent neural networks with explicit representation of dynamic latent variables can mimic behavioral patterns in a physical inference task." Nature Communications, 13 (1).
Version
Final published version
Abstract
AbstractPrimates can richly parse sensory inputs to infer latent information. This ability is hypothesized to rely on establishing mental models of the external world and running mental simulations of those models. However, evidence supporting this hypothesis is limited to behavioral models that do not emulate neural computations. Here, we test this hypothesis by directly comparing the behavior of primates (humans and monkeys) in a ball interception task to that of a large set of recurrent neural network (RNN) models with or without the capacity to dynamically track the underlying latent variables. Humans and monkeys exhibit similar behavioral patterns. This primate behavioral pattern is best captured by RNNs endowed with dynamic inference, consistent with the hypothesis that the primate brain uses dynamic inferences to support flexible physical predictions. Moreover, our work highlights a general strategy for using model neural systems to test computational hypotheses of higher brain function.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1038/S41467-022-33581-6
