Abstract
Fast and slow decisions exhibit distinct behavioral properties, such as the presence of decision bias in faster but not slower responses. This dichotomy is currently explained by assuming that distinct cognitive processes map to separate brain mechanisms. Here, we suggest an alternative single-process account based on the stochastic properties of decision processes. Our experimental results show perceptual biases in a variety of tasks (specifically: learned priors, tilt aftereffect, and tilt illusion) that are much reduced with increasing reaction time. To account for this, we consider a simple yet general explanation: prior and noisy decision-related evidence are integrated serially, with evidence and noise accumulating over time (as in the standard drift diffusion model). With time, owing to noise accumulation, the prior effect is predicted to diminish. This illustrates that a clear behavioral separation—presence vs. absence of bias—may reflect a simple stochastic mechanism.
Highlights
Fast and slow decisions exhibit distinct behavioral properties, such as the presence of decision bias in faster but not slower responses
Our experimental results confirm this prediction, and suggest that time-dependent bias is due to temporal accumulation of sensory evidence and noise
The results showed standard Tilt aftereffect (TAE) magnitudes, with perceived vertical (PV) biased in a direction opposite to that of the context
Summary
Fast and slow decisions exhibit distinct behavioral properties, such as the presence of decision bias in faster but not slower responses. A powerful idea in the neurosciences is that decision makers, brains included, integrate noisy evidence over time to improve performance[8,9] Theories adhering to this principle, such as drift diffusion models (DDM10), offer remarkable explanatory power, notably predicting human reaction-time in decision tasks[11], and accounting for neuronal activity in brain regions correlated with decision-making[8]. In such integrators, the initial state of accumulation is set by prior evidence favoring (biasing) one decision outcome over others[12], implementing an approximation of Bayes’ rule[13]. Our experimental results confirm this prediction, and suggest that time-dependent bias is due to temporal accumulation of sensory evidence and noise
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