Probability detection mechanisms and motor learning

O. V. Lungu, T. Wächter, T. Liu, D. T. Willingham, James Ashe

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


The automatic detection of patterns or regularities in the environment is central to certain forms of motor learning, which are largely procedural and implicit. The rules underlying the detection and use of probabilistic information in the perceptual-motor domain are largely unknown. We conducted two experiments involving a motor learning task with direct and crossed mapping of motor responses in which probabilities were present at the stimulus set level, the response set level, and at the level of stimulus-response (S-R) mapping. We manipulated only one level at a time, while controlling for the other two. The results show that probabilities were detected only when present at the S-R mapping and motor levels, but not at the perceptual one (experiment 1), unless the perceptual features have a dimensional overlap with the S-R mapping rule (experiment 2). The effects of probability detection were mostly facilitatory at the S-R mapping, both facilitatory and inhibitory at the perceptual level, and predominantly inhibitory at the response-set level. The facilitatory effects were based on learning the absolute frequencies first and transitional probabilities later (for the S-R mapping rule) or both types of information at the same time (for perceptual level), whereas the inhibitory effects were based on learning first the transitional probabilities. Our data suggest that both absolute frequencies and transitional probabilities are used in motor learning, but in different temporal orders, according to the probabilistic properties of the environment. The results support the idea that separate neural circuits may be involved in detecting absolute frequencies as compared to transitional probabilities.

Original languageEnglish (US)
Pages (from-to)135-150
Number of pages16
JournalExperimental Brain Research
Issue number2
StatePublished - Nov 2004

Bibliographical note

Funding Information:
Acknowledgements This work was supported by NIH grant NS40106, the Department of Veterans Affairs, and by the Brain Sciences Chair.


  • Motor control
  • Motor learning
  • Probability


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