Computational Modeling of Perceptual Decision Making
A key computation underlying perceptual decisions is the temporal integration of ‘‘evidence’’ in favor of different states of the world. Studies from psychology and neuroscience have shown that observers integrate multiple samples of noisy perceptual evidence over time toward a decision.
An influential model posits perfect evidence integration (i.e., without forgetting), enabling optimal decisions based on stationary evidence. However, in real-life environments, the perceptual evidence typically changes continuously. We used a computational model to show that, under such conditions, performance can be improved by means of leaky (forgetful) integration, if the integration time scale is adapted toward the predominant signal duration.
We then tested whether human observers employ such an adaptive integration process. Observers had to detect visual luminance ‘‘signals’’ of variable strength, duration, and onset latency, embedded within longer streams of noise. Different sessions entailed predominantly short or long signals. The rate of performance improvement as a function of signal duration indicated that observers indeed changed their integration timescale with the predominant signal duration, in accordance with the adaptive integration account. Our findings establish that leaky integration of perceptual evidence is flexible and that cognitive control
mechanisms can exploit this flexibility for optimizing the decision process.