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Using data-driven model-brain mappings to constrain formal models of cognition.

Borst JP, Nijboer M, Taatgen NA, van Rijn H, Anderson JR - PLoS ONE (2015)

Bottom Line: Although such mappings can be based on the experience of the modeler or on a reading of the literature, a formal method is preferred to prevent researcher-based biases.We then validated this mapping by applying it to two new datasets with associated models.The new mapping was at least as powerful as an existing mapping that was based on the literature, and indicated where the models were supported by the data and where they have to be improved.

View Article: PubMed Central - PubMed

Affiliation: Carnegie Mellon University, Dept. of Psychology, Pittsburgh, United States of America; University of Groningen, Dept. of Artificial Intelligence, Groningen, the Netherlands.

ABSTRACT
In this paper we propose a method to create data-driven mappings from components of cognitive models to brain regions. Cognitive models are notoriously hard to evaluate, especially based on behavioral measures alone. Neuroimaging data can provide additional constraints, but this requires a mapping from model components to brain regions. Although such mappings can be based on the experience of the modeler or on a reading of the literature, a formal method is preferred to prevent researcher-based biases. In this paper we used model-based fMRI analysis to create a data-driven model-brain mapping for five modules of the ACT-R cognitive architecture. We then validated this mapping by applying it to two new datasets with associated models. The new mapping was at least as powerful as an existing mapping that was based on the literature, and indicated where the models were supported by the data and where they have to be improved. We conclude that data-driven model-brain mappings can provide strong constraints on cognitive models, and that model-based fMRI is a suitable way to create such mappings.

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Imaging results of the multitasking dataset for the left and right manual modules.The top row shows model predictions, the middle row the BOLD responses in the new, data-driven ROIs, and the bottom row the BOLD responses in the original ROIs.
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pone.0119673.g008: Imaging results of the multitasking dataset for the left and right manual modules.The top row shows model predictions, the middle row the BOLD responses in the new, data-driven ROIs, and the bottom row the BOLD responses in the original ROIs.

Mentions: Fig. 8 (left and right manual module) and Fig. 9 (problem state, declarative memory, aural, and visual modules) show the fMRI results. The model predictions are shown in the top rows of the figures, the results of the new data-driven ROIs in the middle rows, and the results of the original ROIs in the bottom rows. The colors of the conditions correspond to the colors in Fig. 7. Table 3 reports fit measures.


Using data-driven model-brain mappings to constrain formal models of cognition.

Borst JP, Nijboer M, Taatgen NA, van Rijn H, Anderson JR - PLoS ONE (2015)

Imaging results of the multitasking dataset for the left and right manual modules.The top row shows model predictions, the middle row the BOLD responses in the new, data-driven ROIs, and the bottom row the BOLD responses in the original ROIs.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4352055&req=5

pone.0119673.g008: Imaging results of the multitasking dataset for the left and right manual modules.The top row shows model predictions, the middle row the BOLD responses in the new, data-driven ROIs, and the bottom row the BOLD responses in the original ROIs.
Mentions: Fig. 8 (left and right manual module) and Fig. 9 (problem state, declarative memory, aural, and visual modules) show the fMRI results. The model predictions are shown in the top rows of the figures, the results of the new data-driven ROIs in the middle rows, and the results of the original ROIs in the bottom rows. The colors of the conditions correspond to the colors in Fig. 7. Table 3 reports fit measures.

Bottom Line: Although such mappings can be based on the experience of the modeler or on a reading of the literature, a formal method is preferred to prevent researcher-based biases.We then validated this mapping by applying it to two new datasets with associated models.The new mapping was at least as powerful as an existing mapping that was based on the literature, and indicated where the models were supported by the data and where they have to be improved.

View Article: PubMed Central - PubMed

Affiliation: Carnegie Mellon University, Dept. of Psychology, Pittsburgh, United States of America; University of Groningen, Dept. of Artificial Intelligence, Groningen, the Netherlands.

ABSTRACT
In this paper we propose a method to create data-driven mappings from components of cognitive models to brain regions. Cognitive models are notoriously hard to evaluate, especially based on behavioral measures alone. Neuroimaging data can provide additional constraints, but this requires a mapping from model components to brain regions. Although such mappings can be based on the experience of the modeler or on a reading of the literature, a formal method is preferred to prevent researcher-based biases. In this paper we used model-based fMRI analysis to create a data-driven model-brain mapping for five modules of the ACT-R cognitive architecture. We then validated this mapping by applying it to two new datasets with associated models. The new mapping was at least as powerful as an existing mapping that was based on the literature, and indicated where the models were supported by the data and where they have to be improved. We conclude that data-driven model-brain mappings can provide strong constraints on cognitive models, and that model-based fMRI is a suitable way to create such mappings.

Show MeSH