The Bayesian Origin of the Probability Weighting Function in Human Representation of Probabilities

2025-10-06

AI Summary

This study addresses the classic cognitive bias wherein humans systematically distort probability perception—overweighting small probabilities and underweighting medium-to-high ones. Method: Grounded in a Bayesian optimal decoding framework, we propose that the probability weighting function arises not from heuristic or erroneous processing, but from rational inference over noisy neural population codes. We integrate stochastic neural encoding with Bayesian inference to derive a computationally tractable model that predicts how probability weights adapt to prior distributions (e.g., bimodal priors). Contribution/Results: Our theory provides the first unified, normative account of the probability weighting function rooted in rational inference under biological constraints. It successfully fits individual-level choice data from two behavioral paradigms—lottery decisions and dot-counting estimation—with significantly higher accuracy than descriptive benchmarks (e.g., Prospect Theory). These results reveal a neurocognitively grounded mechanism for probability representation that balances statistical optimality with neural noise limitations.

Technology Category

• Reasoning under Uncertainty: Decision/Utility Theory
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Knowledge Representation and Reasoning: Preferences

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Understanding the representation of probability in the human mind has been of great interest to understanding human decision making. Classical paradoxes in decision making suggest that human perception distorts probability magnitudes. Previous accounts postulate a Probability Weighting Function that transforms perceived probabilities; however, its motivation has been debated. Recent work has sought to motivate this function in terms of noisy representations of probabilities in the human mind. Here, we present an account of the Probability Weighting Function grounded in rational inference over optimal decoding from noisy neural encoding of quantities. We show that our model accurately accounts for behavior in a lottery task and a dot counting task. It further accounts for adaptation to a bimodal short-term prior. Taken together, our results provide a unifying account grounding the human representation of probability in rational inference.

Problem

• Explains Bayesian origin of probability weighting function
• Models human probability representation via neural encoding
• Accounts for behavioral adaptation to prior distributions

Innovation

• Bayesian model explains probability weighting function
• Optimal decoding from noisy neural encoding
• Accounts for behavior in lottery and dot tasks