Control of Sensorimotor Variability by Consequences

Abstract

Studies of reaction-time distributions provide a useful quantitative approach to understand decision processes at the neural level and at the behavioral level. A strong relationship between the spread of latencies and the median is generally accepted even though there has been no attempt to disentangle experimentally these two parameters. Here we test the ability to independently control the median and the variability in reaction times. Reaction times were measured in human subjects instructed to make a discrimination between a target and a distractor in a 2AFC task. In a first experiment, saccadic latencies were measured. In a second experiment, we used manual response reaction times. Subjects were trained to produce four different reaction-time distributions. A reinforcing feedback was given depending on both the variability and the median of the latency distributions. When low variability was reinforced, the standard deviation (SD) of reaction-time distributions were reduced by a factor of two and when high variability was reinforced, the SD returned to baseline level. Our procedure independently affected the spread and the median of the distribution patterns. By fitting the latency distributions using the Reddi and Carpenter LATER model, we found that these effects could be simulated by changing the distribution of the noise affecting the decision process. Our results demonstrate that learned contingencies can affect reaction time variability and support the view that the so-called noise level in decision processes can undergo long-term changes.