Journal article
Predicting Individuals' Learning Success from Patterns of Pre-Learning MRI Activity
PloS one, Vol.6(1), pp.e16093-e16093
2011
DOI: 10.1371/journal.pone.0016093
PMCID: PMC3021541
PMID: 21264257
Abstract
Performance in most complex cognitive and psychomotor tasks improves with training, yet the extent of improvement varies among individuals. Is it possible to forecast the benefit that a person might reap from training? Several behavioral measures have been used to predict individual differences in task improvement, but their predictive power is limited. Here we show that individual differences in patterns of time-averaged T2*-weighted MRI images in the dorsal striatum recorded at the initial stage of training predict subsequent learning success in a complex video game with high accuracy. These predictions explained more than half of the variance in learning success among individuals, suggesting that individual differences in neuroanatomy or persistent physiology predict whether and to what extent people will benefit from training in a complex task. Surprisingly, predictions from white matter were highly accurate, while voxels in the gray matter of the dorsal striatum did not contain any information about future training success. Prediction accuracy was higher in the anterior than the posterior half of the dorsal striatum. The link between trainability and the time-averaged T2*-weighted signal in the dorsal striatum reaffirms the role of this part of the basal ganglia in learning and executive functions, such as task-switching and task coordination processes. The ability to predict who will benefit from training by using neuroimaging data collected in the early training phase may have far-reaching implications for the assessment of candidates for specific training programs as well as the study of populations that show deficiencies in learning new skills.
Details
- Title: Subtitle
- Predicting Individuals' Learning Success from Patterns of Pre-Learning MRI Activity
- Creators
- Loan T. K Vo - University of Illinois Urbana-ChampaignDirk B Walther - The Ohio State UniversityArthur F Kramer - University of Illinois Urbana-ChampaignKirk I Erickson - University of PittsburghWalter R Boot - Florida State UniversityMichelle W Voss - University of Illinois Urbana-ChampaignRuchika S Prakash - The Ohio State UniversityHyunkyu Lee - University of Illinois Urbana-ChampaignMonica Fabiani - University of Illinois Urbana-ChampaignGabriele Gratton - University of Illinois Urbana-ChampaignDaniel J Simons - University of Illinois Urbana-ChampaignBradley P Sutton - University of Illinois Urbana-ChampaignMichelle Y Wang - University of Illinois Urbana-Champaign
- Resource Type
- Journal article
- Publication Details
- PloS one, Vol.6(1), pp.e16093-e16093
- DOI
- 10.1371/journal.pone.0016093
- PMID
- 21264257
- PMCID
- PMC3021541
- NLM abbreviation
- PLoS One
- ISSN
- 1932-6203
- eISSN
- 1932-6203
- Publisher
- Public Library of Science; San Francisco, USA
- Alternative title
- MRI Patterns Predict Learning
- Language
- English
- Date published
- 2011
- Academic Unit
- Psychological and Brain Sciences; Iowa Neuroscience Institute
- Record Identifier
- 9984002471102771
Metrics
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