Contextual inference underlies the learning of sensorimotor repertoires

Autor: Daniel M. Wolpert, Máté Lengyel, James B. Heald
Přispěvatelé: Heald, James B [0000-0002-7293-7914], Wolpert, Daniel M [0000-0001-7266-0049], Apollo - University of Cambridge Repository
Rok vydání: 2021
Předmět:
Zdroj: Nature
ISSN: 1476-4687
0028-0836
DOI: 10.1038/s41586-021-04129-3
Popis: Humans spend a lifetime learning, storing and refining a repertoire of motor memories. For example, through experience, we become proficient at manipulating a large range of objects with distinct dynamical properties. 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 motor 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). This insight enables us to account for key features of motor learning that had no unified explanation: spontaneous recovery1, savings2, anterograde interference3, how environmental consistency affects learning rate4,5 and the distinction between explicit and implicit learning6. 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 mechanisms1,4,7–9, is the key principle underlying how a diverse set of experiences is reflected in our motor behaviour. A theory of motor learning based on the principle of contextual inference reveals that adaptation can arise by both creating and updating memories and changing how existing memories are differentially expressed, and predicts evoked recovery and context-dependent single-trial learning.
Databáze: OpenAIRE
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