A Memory of Errors in Sensorimotor Learning
By David J. Herzfeld, P. Vaswani, Mollie K. Marko, Reza Shadmehr. Science, 2014
335 citations recorded by the source corpus.
Practice makes perfect — or does it? How do we learn from past errors? Herzfeld et al. found that when we practice a movement, the human brain has a memory for errors that is then used to learn faster in new conditions. This memory for error exists in parallel with motor memory's two traditional forms: memory of actions and memory of external perturbations. They also proposed a mathematical model for learning from errors. This model explained previous experimental results and predicted other major findings that they later verified experimentally. Science, this issue p. 1349 The history of previously experienced motor errors changes the amount the brain is willing to learn from an error. The current view of motor learning suggests that when we revisit a task, the brain recalls the motor commands it previously learned. In this view, motor memory is a memory of motor commands, acquired through trial-and-error and reinforcement. Here we show that the brain controls how much it is willing to learn from the current error through a principled mechanism that depends on the history of past errors. This suggests that the brain stores a previously unknown form of memory, a memory of errors. A mathematical formulation of this idea provides insights into a host of puzzling experimental data, including savings and meta-learning, demonstrating that when we are better at a motor task, it is partly because the brain recognizes the errors it experienced before.
Subfields: Cognitive Neuroscience, Social Psychology
View Paper: https://www.semanticscholar.org/paper/446b24893b8eaf05dd8f1f911dad1eb965cdd4f2
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Citations in the corpus
- Computational Principles of Supervised Learning in the Cerebellum (2018, positive): “…the induction of long-term plasticity, but the longer-term statistics of experience can also scale up or down the amount of rapid learning that takes place during cerebellar tasks (Herzfeld et al. 2014, Yang & Lisberger 2017), similar to strategies used in machine learning (Schaul et al. 2012).”