The Bayesian brain hypothesis has been a leading theory in understanding perceptual decision-making under uncertainty. While extensive psychophysical evidence supports the notion of the brain performing Bayesian computations, how uncertainty information is encoded in sensory neural populations remains elusive. Specifically, two competing hypotheses propose that early sensory populations encode either the likelihood function (exemplified by probabilistic population codes) or the posterior distribution (exemplified by neural sampling codes) over the stimulus, with the key distinction lying in whether stimulus priors would modulate the neural responses. However, experimentally differentiating these two hypotheses has remained challenging, as it is unclear what task design would effectively distinguish the two. In this work, we present an information-theoretic framework for optimizing the task stimulus distribution that would maximally differentiate competing probabilistic neural codes. To quantify how distinguishable the two probabilistic coding hypotheses are under a given task design, we derive the information gap--the expected performance difference when likelihood versus posterior decoders are applied to neural populations--by evaluating the Kullback-Leibler divergence between the true posterior and a task-marginalized surrogate posterior. Through extensive simulations, we demonstrate that the information gap accurately predicts decoder performance differences across diverse task settings. Critically, maximizing the information gap yields stimulus distributions that optimally differentiate likelihood and posterior coding hypotheses. Our framework enables principled, theory-driven experimental designs with maximal discriminative power to differentiate probabilistic neural codes, advancing our understanding of how neural populations represent and process sensory uncertainty.

翻译：贝叶斯大脑假说一直是理解不确定性下感知决策的主导理论。尽管大量心理物理学证据支持大脑执行贝叶斯计算的观点，但不确定性信息如何在感觉神经群体中编码仍然难以捉摸。具体而言，两种竞争性假说提出早期感觉群体编码的要么是刺激的似然函数（以概率群体编码为例），要么是后验分布（以神经采样编码为例），其关键区别在于刺激先验是否会调节神经响应。然而，实验上区分这两种假说仍然具有挑战性，因为尚不清楚何种任务设计能有效区分二者。本研究提出了一种信息论框架，用于优化任务刺激分布，以最大程度地区分竞争性的概率神经编码。为了量化在给定任务设计下两种概率编码假说的可区分度，我们推导了信息间隙——即对神经群体应用似然解码器与后验解码器时的预期性能差异——通过评估真实后验与任务边缘化替代后验之间的Kullback-Leibler散度。通过大量模拟，我们证明信息间隙能准确预测不同任务设置下解码器的性能差异。关键在于，最大化信息间隙可产生能最优区分似然编码与后验编码假说的刺激分布。我们的框架实现了具有最大区分能力的原理性、理论驱动的实验设计，以区分概率神经编码，从而推进我们对神经群体如何表征和处理感觉不确定性的理解。