An inductive bias for generalization in mouse olfactory learning

Abstract

Animals must generalize from limited experience, yet behavioral experiments in the laboratory setting rarely assess whether or how rapidly they generalize. This contrasts with machine learning systems, where generalization is considered a fundamental test of learning, and emphasizes performance evaluation with new in-distribution or out-of-distribution examples. Here, we used an olfactory categorization task to investigate rules of generalization versus memorization in mice. We trained mice to discriminate between two target odorants mixed with a variable number (0-13) of background odors. There are 32766 possible mixture stimuli to be classified, yet mice learn to generalize from as few as 8 unique mixtures. This generalization is not due to limited memory capacity: mice successfully learned to group the same set of mixtures when category labels were randomly shuffled. Analysis of individual variability revealed features in learning dynamics during training that predict performance in the generalization phase. A linear supervised learning algorithm could describe the generalization from few exemplars well, whereas nonlinear classifiers were necessary to explain memorization. Our experiments suggest that mice have an inductive bias towards generalization, consistent with a preference for simple rules, and will memorize only when forced to do so.