Predicting individuals' learning success from patterns of pre-learning MRI activity.
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 t...
Main Authors: | , , , , , , , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2011-01-01
|
Series: | PLoS ONE |
Online Access: | http://europepmc.org/articles/PMC3021541?pdf=render |
id |
doaj-690eb120f7ee4decb18c42b351d85868 |
---|---|
record_format |
Article |
spelling |
doaj-690eb120f7ee4decb18c42b351d858682020-11-25T01:48:34ZengPublic Library of Science (PLoS)PLoS ONE1932-62032011-01-0161e1609310.1371/journal.pone.0016093Predicting individuals' learning success from patterns of pre-learning MRI activity.Loan T K VoDirk B WaltherArthur F KramerKirk I EricksonWalter R BootMichelle W VossRuchika S PrakashHyunkyu LeeMonica FabianiGabriele GrattonDaniel J SimonsBradley P SuttonMichelle Y WangPerformance 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.http://europepmc.org/articles/PMC3021541?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Loan T K Vo Dirk B Walther Arthur F Kramer Kirk I Erickson Walter R Boot Michelle W Voss Ruchika S Prakash Hyunkyu Lee Monica Fabiani Gabriele Gratton Daniel J Simons Bradley P Sutton Michelle Y Wang |
spellingShingle |
Loan T K Vo Dirk B Walther Arthur F Kramer Kirk I Erickson Walter R Boot Michelle W Voss Ruchika S Prakash Hyunkyu Lee Monica Fabiani Gabriele Gratton Daniel J Simons Bradley P Sutton Michelle Y Wang Predicting individuals' learning success from patterns of pre-learning MRI activity. PLoS ONE |
author_facet |
Loan T K Vo Dirk B Walther Arthur F Kramer Kirk I Erickson Walter R Boot Michelle W Voss Ruchika S Prakash Hyunkyu Lee Monica Fabiani Gabriele Gratton Daniel J Simons Bradley P Sutton Michelle Y Wang |
author_sort |
Loan T K Vo |
title |
Predicting individuals' learning success from patterns of pre-learning MRI activity. |
title_short |
Predicting individuals' learning success from patterns of pre-learning MRI activity. |
title_full |
Predicting individuals' learning success from patterns of pre-learning MRI activity. |
title_fullStr |
Predicting individuals' learning success from patterns of pre-learning MRI activity. |
title_full_unstemmed |
Predicting individuals' learning success from patterns of pre-learning MRI activity. |
title_sort |
predicting individuals' learning success from patterns of pre-learning mri activity. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2011-01-01 |
description |
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. |
url |
http://europepmc.org/articles/PMC3021541?pdf=render |
work_keys_str_mv |
AT loantkvo predictingindividualslearningsuccessfrompatternsofprelearningmriactivity AT dirkbwalther predictingindividualslearningsuccessfrompatternsofprelearningmriactivity AT arthurfkramer predictingindividualslearningsuccessfrompatternsofprelearningmriactivity AT kirkierickson predictingindividualslearningsuccessfrompatternsofprelearningmriactivity AT walterrboot predictingindividualslearningsuccessfrompatternsofprelearningmriactivity AT michellewvoss predictingindividualslearningsuccessfrompatternsofprelearningmriactivity AT ruchikasprakash predictingindividualslearningsuccessfrompatternsofprelearningmriactivity AT hyunkyulee predictingindividualslearningsuccessfrompatternsofprelearningmriactivity AT monicafabiani predictingindividualslearningsuccessfrompatternsofprelearningmriactivity AT gabrielegratton predictingindividualslearningsuccessfrompatternsofprelearningmriactivity AT danieljsimons predictingindividualslearningsuccessfrompatternsofprelearningmriactivity AT bradleypsutton predictingindividualslearningsuccessfrompatternsofprelearningmriactivity AT michelleywang predictingindividualslearningsuccessfrompatternsofprelearningmriactivity |
_version_ |
1725011373572751360 |