Idiosyncratic choice bias naturally emerges from intrinsic stochasticity in neuronal dynamics

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.

Authors: Lior Lebovich, Ran Darshan, Yoni Lavi, David Hansel, Yonatan Loewenstein
Year of publication: 2019
Journal: Nature Human Behaviour

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“Working memory”