ACT-R Model-Based fMRI Data for Neural Correlate Mapping

A dataset from a study aiming to precisely map five ACT-R cognitive architecture modules onto brain regions. The data was generated by Jelmer P. Borst of Carnegie Mellon University using model-based fMRI analysis on two separate datasets, which were combined to create an overall mapping. The study seeks to refine a previous, coarser mapping of these modules.

Use Cases

• Validate and refine brain region mappings for ACT-R modules based on the combined fMRI analysis results.
• Compare the spatial precision of model-based fMRI mappings against the coarser, predefined cube-based mappings mentioned in the description.
• Investigate the neural correlates of specific cognitive processes as defined by the ACT-R framework.
• Replicate or extend the conjunction analysis methodology described for other cognitive models.

Strengths

• Data is derived from a conjunction of two separate model-based fMRI analyses, potentially increasing reliability.
• Focuses on a specific, established cognitive architecture (ACT-R), providing a clear theoretical framework.
• Aims to provide more detailed whole-brain mappings than the previously defined, coarser 12×12×12mm cubes.

Limitations

• Row count, column definitions, and sample data are unknown, limiting suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

Jelmer P. Borst, Carnegie Mellon University, via paperswithcode.

Collection Method

Model-based fMRI analysis applied to two datasets, with results combined.