Learning and exploiting sensory statistics across multiple species

A defining feature of animal intelligence is the ability to discover and update knowledge of statistical regularities in the sensory environment, in service of adaptive behaviour. This allows animals to build appropriate priors, in order to disambiguate noisy inputs, make predictions and act more efficiently. Despite decades of research in the field of human cognition and theoretical neuroscience, it is not known how such learning can be implemented in the brain. We took a cross-species approach by developing well-controlled comparative paradigms in humans, rats, and mice. We compared their performance on a 2-alternative-forced-choice (2AFC) sound categorization task, where stimulus statistics were carefully manipulated without affecting the overall weight of each category. We investigated decision-making in such distinct "statistical contexts" associated with different stimulus prior probabilities. All species optimally adapted their decisions given the underlying sound statistics. Despite the overall sensory-dependent adaptation that is similar across species, the learning speed and trial-to-trial learning updates show interesting individual variabilities. Humans learn the statistics fastest, and most of them form a generative understanding of statistics, by learning the two category distributions separately. Some humans, however, similar to most of mice and some rats, only learned the boundary between the two sound categories, without learning each category separately. In the rest of this talk, I will present results from electrophysiological recordings in the rat’s (pre)frontal regions, showing distinct contributions in learning and utilizing statistical contexts.