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AI-Discovered Cognitive Models Reveal Novel Insights into Human and Animal Learning
Daniel Kasenberg1, Pablo Samuel Castro1, Maria K Eckstein1
1Google DeepMind.
Biorxiv : the Preprint Server for Biology
|June 4, 2026
Summary
DataDIVER automatically discovers simple computer programs as scientific models from data. This approach surfaces novel mechanistic insights into human and animal learning, accelerating scientific discovery.
Area of Science:
- Computational Neuroscience
- Cognitive Science
- Machine Learning
Background:
- Scientific models bridge theories and data in natural sciences, crucial for understanding learning.
- Traditionally, models are handcrafted, but they often fail to capture behavioral complexity.
- Data-driven approaches exist but require significant human effort for insight discovery.
Purpose of the Study:
- To present DataDIVER, a general method for automatically discovering computational models from data.
- To demonstrate DataDIVER's ability to surface novel mechanistic insights into learning.
- To provide a tool for efficient idea generation from large datasets.
Main Methods:
- Developed DataDIVER, an approach optimizing computer programs for data fit and simplicity.
- Models generated are understandable, connect to theory, and make predictions.
- Validated through re-analysis of existing data, confirming model predictions.
Main Results:
- DataDIVER generates simple computer programs as scientific models.
- These models reveal novel mechanistic insights into human and animal learning.
- Generated models successfully predict outcomes and align with theoretical frameworks.
Conclusions:
- DataDIVER offers an efficient way to discover new scientific ideas from data.
- Automated model discovery can accelerate scientific progress, especially with big data.
- The approach enhances understanding of learning mechanisms in psychology and neuroscience.
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