Adaptive precision pooling of model neuron activities predicts the efficiency of human visual learning.

When performing a perceptual task, precision pooling occurs when an organism's decisions are based on the activities of a small set of highly informative neurons. The Adaptive Precision Pooling Hypothesis links perceptual learning and decision making by stating that improvements in performance occur when an organism starts to base its decisions on the responses of neurons that are more informative for a task than the responses that the organism had previously used. We trained human subjects on a visual slant discrimination task and found their performances to be suboptimal relative to an ideal probabilistic observer. Why were subjects suboptimal learners? Our computer simulation results suggest a possible explanation, namely that there are few neurons providing highly reliable information for the perceptual task, and that learning involves searching for these rare, informative neurons during the course of training. This explanation can account for several characteristics of human visual learning, including the fact that people often show large differences in their learning performances with some individuals showing no performance improvements, other individuals showing gradual improvements during the course of training, and still others showing abrupt improvements. The approach described here potentially provides a unifying framework for several theories of perceptual learning including theories stating that learning is due to adaptations of the weightings of read-out connections from early visual representations, external noise filtering or internal noise reduction, increases in the efficiency with which learners encode task-relevant information, and attentional selection of specific neural populations which should undergo adaptation.