混合贝叶斯-联结主义模型实现更准确的行为预测
More accurate behavioral predictions with hybrid Bayesian-connectionist models
Brenden M. Lake, Akshay K. Jagadish, Guangyuan Jiang
AI 速读 基于摘要
作者提出贝叶斯蒸馏与行为微调（BBT）方法，将贝叶斯模型的可解释先验与神经网络对复杂行为的拟合能力结合，并在人类概念学习的四个案例中报告了更优的行为预测表现。
cs.LG
查看原文摘要
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.