Publication:
Strength of baseline inter-trial correlations forecasts adaptive capacity in the vestibulo-ocular reflex

Thumbnail Image

Open/View Files

Date

2017

Journal Title

Journal ISSN

Volume Title

Publisher

Public Library of Science
The Harvard community has made this article openly available. Please share how this access benefits you.

Research Projects

Organizational Units

Journal Issue

Citation

Beaton, Kara H., Aaron L. Wong, Steven B. Lowen, and Mark Shelhamer. 2017. “Strength of baseline inter-trial correlations forecasts adaptive capacity in the vestibulo-ocular reflex.” PLoS ONE 12 (4): e0174977. doi:10.1371/journal.pone.0174977. http://dx.doi.org/10.1371/journal.pone.0174977.

Research Data

Abstract

Individual differences in sensorimotor adaptability may permit customized training protocols for optimum learning. Here, we sought to forecast individual adaptive capabilities in the vestibulo-ocular reflex (VOR). Subjects performed 400 head-rotation steps (400 trials) during a baseline test, followed by 20 min of VOR gain adaptation. All subjects exhibited mean baseline VOR gain of approximately 1.0, variable from trial to trial, and showed desired reductions in gain following adaptation with variation in extent across individuals. The extent to which a given subject adapted was inversely proportional to a measure of the strength and duration of baseline inter-trial correlations (β). β is derived from the decay of the autocorrelation of the sequence of VOR gains, and describes how strongly correlated are past gain values; it thus indicates how much the VOR gain on any given trial is informed by performance on previous trials. To maximize the time that images are stabilized on the retina, the VOR should maintain a gain close to 1.0 that is adjusted predominantly according to the most recent error; hence, it is not surprising that individuals who exhibit smaller β (weaker inter-trial correlations) also exhibited the best adaptation. Our finding suggests that the temporal structure of baseline behavioral data contains important information that may aid in forecasting adaptive capacities. This has significant implications for the development of personalized physical therapy protocols for patients, and for other cases when it is necessary to adjust motor programs to maintain movement accuracy in response to pathological and environmental changes.

Description

Keywords

Biology and Life Sciences, Physiology, Sensory Physiology, Visual System, Eye Movements, Medicine and Health Sciences, Neuroscience, Sensory Systems, Anatomy, Head, Eyes, Ocular System, Cognitive Science, Cognitive Psychology, Learning, Psychology, Social Sciences, Learning and Memory, Physical Sciences, Mathematics, Geometry, Fractals, Musculoskeletal System, Mathematical and Statistical Techniques, Statistical Methods, Multivariate Analysis, Principal Component Analysis, Statistics (Mathematics), Ocular Anatomy, Retina

Terms of Use

This article is made available under the terms and conditions applicable to Other Posted Material (LAA), as set forth at Terms of Service

Endorsement

Review

Supplemented By

Referenced By

Related Stories