Improving behavioral performance under full attention by adjusting response criteria to changes in stimulus predictability

Abstract

One of the key features of active perception is the ability to predict critical sensory events. Humans and animals can implicitly learn statistical regularities in the timing of events and use them to improve behavioral performance. Here, we used a signal detection approach to investigate whether such improvements in performance result from changes of perceptual sensitivity or rather from adjustments of a response criterion. In a regular sequence of briefly presented stimuli, human observers performed a noise-limited motion detection task by monitoring the stimulus stream for the appearance of a designated target direction. We manipulated target predictability through the hazard rate, which specifies the likelihood that a target is about to occur, given it has not occurred so far. Analyses of response accuracy revealed that improvements in performance could be accounted for by adjustments of the response criterion; a growing hazard rate was paralleled by an increasing tendency to report the presence of a target. In contrast, the hazard rate did not affect perceptual sensitivity. Consistent with previous research, we also found that reaction time decreases as the hazard rate grows. A simple rise-to-threshold model could well describe this decrease and attribute predictability effects to threshold adjustments rather than changes in information supply. We conclude that, even under conditions of full attention and constant perceptual sensitivity, behavioral performance can be optimized by dynamically adjusting the response criterion to meet ongoing changes in the likelihood of a target.