Computational Learning Phenotypes and Resting-State fMRI Connectivity Data

A behavioral paradigm and modeling framework tested in a large cohort of human participants. The dataset, authored by Evan Dastin-van Rijn and last updated in May 2026, contains results from a study investigating individual differences in learning and their relationship to brain connectivity. It includes 4.5 MB of data in PDF format.

Use Cases

• Classifying individuals into behavioral phenotypes based on how they carve temporal contexts into latent states.
• Studying the impact of behavioral phenotypes on continual learning, specifically interference avoidance and information reuse.
• Testing for relationships between computational learning phenotypes and underlying brain connectivity measured by resting-state fMRI.

Strengths

• Data is based on a large cohort of human participants.
• The dataset is associated with a specific behavioral paradigm and modeling framework developed for the study.
• It is published under a CC-BY-4.0 license, allowing for reuse and modification.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The primary data format is PDF, which may require extraction for computational analysis.

Provenance

Source

Evan Dastin-van Rijn via figshare

Collection Method

Likely collected via a behavioral experiment and resting-state fMRI scans on a human cohort.

Freshness

Last updated 2026-05-01 05:27:29; freshness should be verified.