A simple two-stage model predicts response time distributions

AUTOR(ES)

Carpenter, R H S

FONTE

Blackwell Science Inc

RESUMO

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.

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