Idiosyncratic choice bias in decision tasks naturally emerges from neuronal network dynamics

TL;DR

This paper demonstrates that idiosyncratic choice biases naturally arise from neuronal network dynamics, challenging the view that they are merely noise or measurement artifacts in decision-making experiments.

Contribution

It provides theoretical evidence that choice biases are an inherent outcome of neuronal network dynamics, not just experimental noise or participant-specific factors.

Findings

Significant biases observed in perceptual and motor tasks

Biases emerge from symmetric neuronal network dynamics

Biases are virtually inevitable in decision tasks

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. It is tempting to account for it as resulting from the (unknown) participant-specific history and thus treat it as a measurement noise. Indeed, idiosyncratic choice biases are typically considered as nuisance. Care is taken to account for them by adding an ad-hoc bias parameter or by counterbalancing the choices to average them out. Here we quantify idiosyncratic choice biases in a perceptual discrimination task and a motor task. We report substantial and significant biases in both cases. Then, we present theoretical evidence that even in idealized experiments, 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.