Distributional learning drives statistical suppression and deafening

Abstract

Humans and other animals use information about how likely it is for something to happen. The absolute and relative probability of an event influences a remarkable breadth of behaviors, from foraging for food to comprehending linguistic constructions -- even when these probabilities are learned implicitly. It is less clear how, and under what circumstances, statistical learning of simple probabilities might drive changes in perception and cognition. Here, across a series of 29 experiments, we probe listeners’ sensitivity to task-irrelevant changes in the probability distribution of tones’ acoustic frequency across tone-in-noise detection and tone duration decisions. We observe that the task-irrelevant frequency distribution influences the ability to detect a sound and the speed with which perceptual decisions are made. The shape of the probability distribution, its range, and a tone’s relative position within that range impact observed patterns of suppression and enhancement of tone detection and decision making. Perceptual decisions are also modulated by a newly discovered perceptual bias, with lower frequencies in the distribution more often and more rapidly perceived as longer, and higher frequencies as shorter. Perception is sensitive to rapid distribution changes, but distributional learning from previous probability distributions also carries over. In fact, massed exposure to a single point along the dimension results in a sustained ’statistical deafening’ along a range of subsequently encountered frequencies. This seemingly maladaptive loss of sensitivity - occurring entirely in the absence of feedback or reward - points to a gain mechanism that suppresses sensitivity to regions along a perceptual dimension that are less likely to be encountered.Significance StatementOrganisms as diverse as honeybees and humans pick up on probabilities in the world around them. People implicitly learn the likelihood of a color, price range, or even syntactic structure. How does statistical learning affect how we detect events and make decisions, especially when probabilities are completely irrelevant to the task at hand, and can change without warning? We find that people learn and track changes in perceptual probabilities irrelevant to a task and that this learning drives dynamic shifts in perception characterized by graded effects of enhancement – and primarily – suppression across acoustic frequency. This can result in a remarkably long-lived ’statistical deafening’ that seems maladaptive but may instead reflect use of likelihood to guide and sharpen perception.