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.

翻译：理解人类心智中的概率表征对于理解人类决策具有重要意义。决策中的经典悖论表明，人类感知会扭曲概率量值。先前研究假设存在一个概率权重函数来转换感知概率，但其动机一直存在争议。近期研究试图从人类心智中概率的噪声表征角度解释该函数的动机。本文提出一种基于理性推断的概率权重函数解释框架，该框架源于对数量噪声神经编码的最优解码。我们证明，该模型能够准确解释彩票任务和点数计数任务中的行为表现，并进一步解释了针对双峰短期先验的适应机制。综合来看，我们的研究结果提供了一个统一的理论框架，将人类的概率表征建立在理性推断的基础之上。