Efficient coding explains the universal law of generalization in human perception

Balancing costs and performance Deciding whether a novel object is another instance of something already known or an example of something different is an easily solved problem. Empirical mapping of human performance across a wide range of domains has established an exponential relationship between the generalization gradient and interstimuli distance. Sims now shows that this relationship can be derived from a consideration of the costs of optimal information coding. Science, this issue p. 652 Learning from experience is an inevitable outcome of any error-minimizing information processing system. Perceptual generalization and discrimination are fundamental cognitive abilities. For example, if a bird eats a poisonous butterfly, it will learn to avoid preying on that species again by generalizing its past experience to new perceptual stimuli. In cognitive science, the “universal law of generalization” seeks to explain this ability and states that generalization between stimuli will follow an exponential function of their distance in “psychological space.” Here, I challenge existing theoretical explanations for the universal law and offer an alternative account based on the principle of efficient coding. I show that the universal law emerges inevitably from any information processing system (whether biological or artificial) that minimizes the cost of perceptual error subject to constraints on the ability to process or transmit information.