Humans spend a lifetime learning, storing and refining a repertoire of memories. However, it is unknown what principle underlies how our continuous stream of sensorimotor experience is segmented into separate memories and how we adapt and use this growing repertoire. Here we develop a theory of learning based on the key principle that memory creation, updating and expression are all controlled by a single computation ??? contextual inference. Our theory reveals that adaptation can arise both by creating and updating memories (proper learning) and by changing how existing memories are differentially expressed (apparent learning). By instantiating these insights in the specific domain of motor learning, our theory accounts for key empirical phenomena that had no unified explanation: spontaneous recovery, savings, anterograde interference, how environmental consistency affects learning rate and the distinction between explicit and implicit learning. Critically, our theory also predicts new phenomena — evoked recovery and context-dependent single-trial learning — which we confirm experimentally. These results suggest that contextual inference, rather than classical single-context mechanisms, is the key principle underlying how a diverse set of experiences is reflected in our motor behaviour.

 

Máté Lengyel

Professor of Computational Neuroscience
Computational and Biological Learning Lab
Department of Engineering, University of Cambridge

Senior Research Fellow
Department of Cognitive Science
Central European University

Máté Lengyel – What’s in a learning curve? Computational principles underlying the learning of sensorimotor repertoires