Neurally-informed modelling of static and dynamic decision biases

Different accounts have been developed to explain the mechanisms underlying value biases during perceptual decision-making, within the model framework of bounded accumulation. The starting point bias account suggests a shift in the starting point of evidence accumulation, in the direction of the more valuable alternative. The drift rate bias account suggests that the mean rate of accumulation is steepened for the more valuable alternative. While most studies have supported a starting point bias (SPB) approach, recent work (Afacan-Seref et al., 2018) suggests that drift rate biases (DRB) may also be applied in certain circumstances. Here, we used human EEG signatures of competitive motor preparation to construct a cognitive decision model that can explain the biasing mechanisms through which participants perform a value-biased orientation discrimination task under a strict deadline. Motor preparation dynamics showed signs of a value bias that emerged prior to evidence onset and increased steadily with time. Accordingly, we constructed a model that included an anticipatory dynamic urgency signal towards the High Value alternative. This model provided a better fit to behaviour than models with either a starting point or a drift rate bias but no anticipatory dynamics. These results point to a role for value-modulated, anticipatory motor preparation in fast-paced decision-making tasks, and suggest a unitary mechanism that can generate both static (starting point) and dynamic (drift rate) biases at the same time.