title: A simple two-stage model predicts response time distributions.
abstract: The neural mechanisms underlying reaction times have previously been modelled in two distinct ways. When stimuli are hard to detect, response time tends to follow a random-walk model that integrates noisy sensory signals. But studies investigating the influence of higher-level factors such as prior probability and response urgency typically use highly detectable targets, and response times then usually correspond to a linear rise-to-threshold mechanism. Here we show that a model incorporating both types of element in series - a detector integrating noisy afferent signals, followed by a linear rise-to-threshold performing decision - successfully predicts not only mean response times but, much more stringently, the observed distribution of these times and the rate of decision errors over a wide range of stimulus detectability. By reconciling what previously may have seemed to be conflicting theories, we are now closer to having a complete description of reaction time and the decision processes that underlie it.
authors: The Physiological Laboratory, Downing Site, Cambridge CB2 3EG, UK. rhsc1@cam.ac.uk
authors: R H S
authors: RH
authors: Carpenter
authors: B A J
authors: BA
authors: Reddi
authors: A J
authors: AJ
authors: Anderson
mesh_terms: D000328:Adult; D000818:Animals; D003198:Computer Simulation; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008959:Models, Neurological; D009039:Motion Perception / Q000502:physiology*; D009415:Nerve Net / Q000502:physiology*; D011930:Reaction Time / Q000502:physiology*; D014793:Visual Cortex / Q000502:physiology*; D055815:Young Adult