Abstract
AbstractReinforcement learning, the ability to change motor behavior based on external reward, has been suggested to play a critical role in early stages of speech motor development and is widely used in clinical rehabilitation for speech motor disorders. However, no current evidence exists that demonstrates the capability of reinforcement to drive changes in human speech behavior. Speech provides a unique test of the universality of reinforcement learning across motor domains: speech is a complex, high-dimensional motor task whose goals do not specify a task to be performed in the environment but ultimately must be self-generated by each speaker such that they are understood by those around them. Across four experiments, we examine whether reinforcement learning alone is sufficient to drive changes in speech behavior and parametrically test two features known to affect reinforcement learning in reaching: how informative the reinforcement signal is as well as the availability of sensory feedback about the outcomes of one’s motor behavior. We show that learning does occur and is more likely when participants receive auditory feedback that gives an implicit target for production, even though they do not explicitly imitate that target. Contrary to results from upper limb control, masking feedback about movement outcomes has no effect on speech learning. Together, our results suggest a potential role for reinforcement learning in speech but that it likely operates differently than in other motor domains.
Publisher
Cold Spring Harbor Laboratory
Reference64 articles.
1. Effects of Selected Practice and Feedback Variables on Speech Motor Learning;Journal of Medical Speech-Language Pathology,2000
2. Summary Feedback Schedules and Speech Motor Learning in Parkinson’s Disease;Journal of Medical Speech-Language Pathology,2002
3. Dialect divergence and convergence in New Zealand English
4. Understanding the nature of apraxia of speech: Theory, analysis, and treatment
5. Bates, D. , Maechler, M. , Bolker, B. , & Walker, S. (2014). lme4: Linear mixed-effects models using Eigen and S4. R Package Version, 1.1-12(7).
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献