Data from: Contextual inference underlies the learning of sensorimotor repertoires

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.

人类终其一生都在学习、储存并精进一套运动记忆（motor memories）库。举例而言，经由日常经验，我们能够熟练操控一大批具备各异动力学特性的物体。然而，我们仍不清楚支撑以下两个核心问题的原则：一是我们持续不断的感觉运动经验（sensorimotor experience）流如何被分割为独立的记忆单元，二是我们如何适应并运用这套不断扩充的记忆库。本研究基于一项核心原则构建了一套运动学习理论：记忆的创建、更新与提取均由单一计算过程——上下文推理（contextual inference）所调控。该理论揭示，适应性既可以通过创建与更新记忆（即真正的学习）实现，也可以通过改变现有记忆的差异化提取方式（即表观学习）达成。这一理论视角使我们能够统一解释此前缺乏合理解释的多项运动学习核心特征：自发恢复、节省效应、顺向干扰、环境一致性对学习速率的影响，以及外显学习与内隐学习的区分。至关重要的是，该理论还预测了两项全新的现象——诱发恢复与依赖于情境的单试次学习，并通过实验验证了这些预测。上述结果表明，相较于经典的单一情境机制，上下文推理才是支撑我们将多样化经验转化为运动行为的核心原则。