CTN Seminar: Chris Sims Rensselaer Polytechnic
Room: DC1304
Title: Why Simplicity Enables Intelligence: Efficient Coding in Human Learning and Generalization
Abstract:
Human intelligence depends critically on the ability to learn representations that generalize beyond past experience. While reinforcement learning theory formalizes how agents should act to maximize reward, it provides little guidance on how internal representations should be structured to support generalization. In this talk, I propose that efficient coding provides a unifying representational principle. When agents are constrained to use the simplest representations compatible with reward maximization, they are forced to discover abstract structure in the environment and to selectively encode features that matter for behaviour. I present a computational framework in which efficient coding augments the classical reinforcement learning objective, leading to compact internal state spaces that support robust generalization. Behavioural experiments show that this framework accounts for human generalization patterns that standard models struggle to explain. I further demonstrate that the same principle explains long-standing regularities in perceptual generalization, including the universal law of generalization. These results suggest that abstraction, generalization, and perceptual similarity arise from a common normative pressure to efficiently encode information under resource constraints.
