fMRI Classification learning
Aron, A.R. and Poldrack, R.A. and Gluck, M.A.

Type: Dataset
Tags: fMRI

title = {fMRI Classification learning},
journal = {},
author = {Aron, A.R. and Poldrack, R.A. and Gluck, M.A.},
year = {2011},
url = {},
license = {ODC Public Domain Dedication and Licence (PDDL)},
abstract = {Submitted by picchetti on Thu, 10/06/2011 - 11:36

Subjects performed a classification learning task with two different problems (across different runs), using a "weather prediction" task.  In one (probabilistic) problem, the labels were probabilistically related to each set of cards.  In another (deterministic) problem, the labels were deterministically related to each set of cards.  After learning, subjects participated in an event-related block of judgment only (no feedback) in which they were presented with stimuli from both of the training problems.

Tasks and Conditions: 
001 Probabilistic classification task

001 Probabilistic classification trials
002 feedback
002 deterministic classification

001 Deterministic classification trials
002 feedback
003 classification probe without feedback

001 Classification trials: Probabilistic
002 Classification trials: Deterministic

Investigator Info
Aron, A.R.
Poldrack, R.A.
Gluck, M.A.
Acknowledgements and Funding: 
Whitehall Foundation and NSF grant BCS-0223843 to R.A.P. The authors thank Allan J. Tobin and Robert Bilder for helpful discussion and encouragement, Sabrina Tom for scanning and Catherine Myers and Daphna Shohamy for help with task design.

Pubmed Link: 
Long-term test-retest reliability of fMRI
Digital Document: 

Study Metadata
Sample Size: 
Scanner Type: 
3 T Siemens Allegra MRI scanner

Accession Number: 
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