Unsupervised Machine Learning for Social Cognition

Using unsupervised machine learning to reveal hidden patterns in human beliefs and attitudes.

A tutorial and applied methods paper showing how K-means, DBSCAN, PCA, and market basket analysis can help social cognition researchers discover structure in large-scale data.

Open public source Back to work

doi.org

From Data to Discovery: Unsupervised Machine Learning’s Role in Social Cognition

Social Cognition2025 | Published

From Data to Discovery: Unsupervised Machine Learning’s Role in Social Cognition

Social Cognition methods article with DOI and public PDF link.

A tutorial showing how unsupervised machine learning can help social cognition researchers discover hidden patterns in large-scale attitude and belief data.

Open public source

PublishedUnsupervised structure discovery

Evidence panel

Source, method, and review point.

Raw material

large-scale attitude data, Project Implicit Race IAT worked examples

Method operation

compare possible clusters, dimensions, and co-occurring patterns

Structured artifact

Possible clusters, dimensions, and co-occurring response patterns that researchers can interpret with theory.

Checkpoint

Theory check: clusters and dimensions are candidates for interpretation, not answers by themselves.

Open source Publications

Case study

From question to public source.

Each row separates the human question, the evidence, the method, Jonathan's role, the contribution, the public source, and the limits of the claim.

Question: How can social-cognition researchers find hidden structure in attitude data without deciding the answer in advance?
Evidence / design: Methods tutorial using Project Implicit Race IAT data and worked examples.
My role: Lead author.
Method / approach: K-means, DBSCAN, PCA, and market basket analysis, used as discovery tools that still need theory checks.
What it contributes: A tutorial and applied methods paper showing how K-means, DBSCAN, PCA, and market basket analysis can help social cognition researchers discover structure in large-scale data.
Public source: Published, Social Cognition, 2025. Open public source
Limits / what not to overclaim: The project treats clusters and dimensions as patterns to interpret with theory, not answers by themselves.

Concrete public details

Project Implicit Race IAT data
K-means, DBSCAN, PCA, and market basket analysis
Theory check between model output and social-cognition constructs
Published in Social Cognition