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Idiosyncratic choice bias naturally emerges from intrinsic stochasticity in neuronal dynamics

Author

Listed:
  • Lior Lebovich

    (The Hebrew University)

  • Ran Darshan

    (The Hebrew University
    Janelia Research Campus)

  • Yoni Lavi

    (The Hebrew University
    Code Institute)

  • David Hansel

    (Learning and Memory Lab; CNRS-UMR8002, 45 Rue des Saints Pères)

  • Yonatan Loewenstein

    (The Hebrew University
    The Hebrew University
    The Hebrew University)

Abstract

Idiosyncratic tendency to choose one alternative over others in the absence of an identified reason is a common observation in two-alternative forced-choice experiments. Here we quantify idiosyncratic choice biases in a perceptual discrimination task and a motor task. We report substantial and significant biases in both cases that cannot be accounted for by the experimental context. Then, we present theoretical evidence that even in an idealized experiment, in which the settings are symmetric, idiosyncratic choice bias is expected to emerge from the dynamics of competing neuronal networks. We thus argue that idiosyncratic choice bias reflects the microscopic dynamics of choice and therefore is virtually inevitable in any comparison or decision task.

Suggested Citation

  • Lior Lebovich & Ran Darshan & Yoni Lavi & David Hansel & Yonatan Loewenstein, 2019. "Idiosyncratic choice bias naturally emerges from intrinsic stochasticity in neuronal dynamics," Nature Human Behaviour, Nature, vol. 3(11), pages 1190-1202, November.
    • Handle: RePEc:nat:nathum:v:3:y:2019:i:11:d:10.1038_s41562-019-0682-7
    DOI: 10.1038/s41562-019-0682-7
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    Cited by:

    1. Oren Amsalem & Hidehiko Inagaki & Jianing Yu & Karel Svoboda & Ran Darshan, 2024. "Sub-threshold neuronal activity and the dynamical regime of cerebral cortex," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    2. Christopher M. Kim & Arseny Finkelstein & Carson C. Chow & Karel Svoboda & Ran Darshan, 2023. "Distributing task-related neural activity across a cortical network through task-independent connections," Nature Communications, Nature, vol. 14(1), pages 1-21, December.

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