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  A simple solution for model comparison in bold imaging: the special case of reward prediction error and reward outcomes

Erdeniz, B., Rohe, T., Done, J., & Seidler, R. (2013). A simple solution for model comparison in bold imaging: the special case of reward prediction error and reward outcomes. Frontiers in Neuroscience, 7: 116, pp. 1-6. doi:10.3389/fnins.2013.00116.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-001A-13F2-2 Version Permalink: http://hdl.handle.net/21.11116/0000-0001-8CD1-5
Genre: Journal Article

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 Creators:
Erdeniz, B, Author
Rohe, T1, 2, 3, Author              
Done, J, Author
Seidler, RD, Author
Affiliations:
1Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497797              
2Research Group Cognitive Neuroimaging, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497804              
3Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Conventional neuroimaging techniques provide information about condition-related changes of the BOLD (blood-oxygen-level dependent) signal, indicating only where and when the underlying cognitive processes occur. Recently, with the help of a new approach called “model-based” functional neuroimaging (fMRI), researchers are able to visualize changes in the internal variables of a time varying learning process, such as the reward prediction error or the predicted reward value of a conditional stimulus. However, despite being extremely beneficial to the imaging community in understanding the neural correlates of decision variables, a model-based approach to brain imaging data is also methodologically challenging due to the multicollinearity problem in statistical analysis. There are multiple sources of multicollinearity in functional neuroimaging including investigations of closely related variables and/or experimental designs that do not account for this. The source of multicollinearity discussed in this paper occurs due to correlation between different subjective variables that are calculated very close in time. Here, we review methodological approaches to analyzing such data by discussing the special case of separating the reward prediction error signal from reward outcomes.

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 Dates: 2013-07
 Publication Status: Published online
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 Identifiers: DOI: 10.3389/fnins.2013.00116
BibTex Citekey: ErdenizRDS2013
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Title: Frontiers in Neuroscience
Source Genre: Journal
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Pages: - Volume / Issue: 7 Sequence Number: 116 Start / End Page: 1 - 6 Identifier: -