Motor effort and adaptive sampling in perceptual decision-making

People usually switch their attention between the options when trying to make a decision. In our experiments, we bound motor effort to such switching behavior during a two-alternative perceptual decision-making task and recorded the sampling patterns by computer mouse cursor tracking. We found that the time and motor cost to make the decision positively correlated with the number of switches between the stimuli and increased with the difficulty of the task. Specifically, the first and last sampled items were chosen in an attempt to minimize the overall motor effort during the task and were manipulable by biasing the relevant motor cost. Moreover, we observed the last-sampling bias that the last sampled item was more likely to be chosen by the subjects. We listed all possible Bayesian Network models for different hypotheses regarding the causal relationship behind the last-sampling bias, and only the model assuming bidirectional dependency between attention and decision successfully predicted the empirical results. Meanwhile, denying that the current decision variable can feedback into the attention switching patterns during sampling, the conventional attentional drift-diffusion model (aDDM) was inadequate to explain the size of the last-sampling bias in our experimental conditions. We concluded that the sampling behavior during perceptual decision-making actively adapted to the motor effort in the specific task settings, as well as the temporary decision.