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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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Behavioral results for the multitasking dataset.Left four graphs show the data, the right four graphs the model predictions. Error bars indicate standard error.
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pone.0119673.g007: Behavioral results for the multitasking dataset.Left four graphs show the data, the right four graphs the model predictions. Error bars indicate standard error.

Mentions: Fig. 7 shows the behavioral results: on the left the data, on the right the model predictions. The top left graph shows the mismatch between high tones presented and counted in the tone-counting task. Subjects performed well in all conditions, but made more errors when tone counting was combined with n-back, while tracking only had a minimal impact. The second graph shows the proportion of error time in the tracking task, which is the proportion of time the cursor was outside the vertical lines flanking the target dot. Subjects also performed very well on this task; only the combination with n-back led to a clear decrease in performance. The model predicted these results fairly accurately, although it predicted a much higher tracking error than displayed by the human subjects. In general, doing either of the tasks in combination with n-back led to the largest performance decrements. The model attributed those decrements to competition for the problem state module in the case of tone counting and to competition for the visual module in the case of the tracking task.


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)

Behavioral results for the multitasking dataset.Left four graphs show the data, the right four graphs the model predictions. Error bars indicate standard error.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0119673.g007: Behavioral results for the multitasking dataset.Left four graphs show the data, the right four graphs the model predictions. Error bars indicate standard error.
Mentions: Fig. 7 shows the behavioral results: on the left the data, on the right the model predictions. The top left graph shows the mismatch between high tones presented and counted in the tone-counting task. Subjects performed well in all conditions, but made more errors when tone counting was combined with n-back, while tracking only had a minimal impact. The second graph shows the proportion of error time in the tracking task, which is the proportion of time the cursor was outside the vertical lines flanking the target dot. Subjects also performed very well on this task; only the combination with n-back led to a clear decrease in performance. The model predicted these results fairly accurately, although it predicted a much higher tracking error than displayed by the human subjects. In general, doing either of the tasks in combination with n-back led to the largest performance decrements. The model attributed those decrements to competition for the problem state module in the case of tone counting and to competition for the visual module in the case of the tracking task.

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