# More accurate behavioral predictions with hybrid Bayesian-connectionist models

arXiv: https://arxiv.org/abs/2608.22154 · HTML: https://mlsanity.com/paper/2608.22154/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 70 references that are in the map.
- First posted on arXiv 23 Aug 2026.

## Builds on (most cited first, 10 of 10)

- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Automatically Interpreting Millions of Features in Large Language Models](https://mlsanity.com/paper/2410.13928/) (2024)
- [Modeling rapid language learning by distilling Bayesian priors into artificial neural networks](https://mlsanity.com/paper/2305.14701/) (2023)
- [The BabyView dataset: High-resolution egocentric videos of infants' and young children's everyday experiences](https://mlsanity.com/paper/2406.10447/) (2024)
- [Closing the Loop to Discover Psychological Theories with an Automated Cognitive Scientist](https://mlsanity.com/paper/2606.26448/) (2026)
- [Whither symbols in the era of advanced neural networks?](https://mlsanity.com/paper/2508.05776/) (2025)
- [Human-like Category Learning by Injecting Ecological Priors from Large Language Models into Neural Networks](https://mlsanity.com/paper/2402.01821/) (2024)
- [Meta-learning ecological priors from large language models explains human learning and decision making](https://mlsanity.com/paper/2509.00116/) (2025)
- [Catalytic Priors: Using Synthetic Data to Specify Prior Distributions in Bayesian Analysis](https://mlsanity.com/paper/2208.14123/) (2022)
- [LLMs model how humans induce logically structured rules](https://mlsanity.com/paper/2507.03876/) (2025)

## Abstract (arXiv)

Researchers must often choose between Bayesian or neural network models of behavior, two paradigms with complementary strengths and weaknesses. An ideal paradigm would facilitate testing many kinds of representations and inductive biases; Bayesian models make this easy, while neural networks do not. Similarly, an ideal paradigm would avoid over-simplifications; neural networks make this easy, while Bayesian models do not. Here, we introduce Bayesian distillation with Behavioral Tuning (BBT) as an approach to getting the best of both traditions. BBT offers a simple recipe for model building: first, a neural network is trained to mimic a Bayesian model through synthetic data, and second, the network is fine-tuned on human behavior to capture additional structure and nuance. Across four case studies in human concept learning, we find that BBT outperforms traditional approaches at predicting human behavior while also revealing psychological insights, resulting in models that can both mimic Bayesian priors and capture heuristics and biases that violate simple modeling assumptions.