Data from: Contextual inference underlies the learning of sensorimotor repertoires
Data files
Sep 19, 2021 version files 4.68 GB
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evoked_recovery_participant1.mat
25.77 MB
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evoked_recovery_participant2.mat
28.66 MB
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evoked_recovery_participant3.mat
24.55 MB
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evoked_recovery_participant4.mat
23.73 MB
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evoked_recovery_participant5.mat
24.74 MB
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evoked_recovery_participant6.mat
25.69 MB
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evoked_recovery_participant7.mat
24.60 MB
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evoked_recovery_participant8.mat
24.07 MB
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memory_updating_participant1.mat
180.34 MB
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memory_updating_participant10.mat
189.73 MB
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memory_updating_participant11.mat
168.70 MB
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memory_updating_participant12.mat
185.11 MB
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memory_updating_participant13.mat
183.81 MB
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memory_updating_participant14.mat
177.96 MB
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memory_updating_participant15.mat
183.26 MB
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memory_updating_participant16.mat
184.07 MB
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memory_updating_participant17.mat
173.81 MB
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memory_updating_participant18.mat
173.10 MB
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memory_updating_participant19.mat
174.39 MB
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memory_updating_participant2.mat
165.76 MB
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memory_updating_participant20.mat
175.71 MB
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memory_updating_participant21.mat
169.21 MB
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memory_updating_participant22.mat
162.89 MB
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memory_updating_participant23.mat
179.31 MB
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memory_updating_participant24.mat
185.28 MB
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memory_updating_participant3.mat
177.84 MB
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memory_updating_participant4.mat
203.56 MB
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memory_updating_participant5.mat
173.53 MB
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memory_updating_participant6.mat
157.23 MB
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memory_updating_participant7.mat
189.83 MB
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memory_updating_participant8.mat
177.88 MB
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memory_updating_participant9.mat
169.42 MB
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README.txt
6.33 KB
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spontaneous_recovery_participant1.mat
25.35 MB
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spontaneous_recovery_participant2.mat
27.69 MB
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spontaneous_recovery_participant3.mat
27.29 MB
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spontaneous_recovery_participant4.mat
30.01 MB
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spontaneous_recovery_participant5.mat
28.45 MB
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spontaneous_recovery_participant6.mat
26.13 MB
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spontaneous_recovery_participant7.mat
29.14 MB
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spontaneous_recovery_participant8.mat
25.14 MB
Abstract
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 allows us to account for key features of motor learning 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 novel 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.