标题： 吸引和排斥的感知偏差在生成对抗推理中自然出现 显示英文标题

标题： Attractive and Repulsive Perceptual Biases Naturally Emerge in Generative Adversarial Inference

摘要： 人类的感知估计在不确定性条件下表现出显著的偏差反转：在感觉不确定性较高时，它们会向先验期望靠拢，但当内部噪声占主导时，它们会远离这些期望。 虽然贝叶斯推断结合高效编码可以解释这种双重偏差，但现有模型依赖于手工设计的先验或固定编码器，无法说明这些表示和推断如何通过学习产生。 我们引入了一个生成对抗推理（GAI）网络，该网络可以直接从数据中同时学习感觉表示和推理策略，而无需假设显式的似然函数或先验。 通过联合重建和对抗训练，模型学习到一种近似于与信息论预测一致的高效编码表示。 在具有不同信噪比的Gabor刺激上进行训练，GAI自发地再现了从先验吸引到排斥的完整过渡，并恢复了高效编码理论预测的费舍尔信息轮廓。 它还比监督或变分方法更稳健地捕捉到人类感知中观察到的特征偏差反转。 这些结果表明，一个通过对抗训练的网络可以共同获得高效的感官编码并支持贝叶斯一致的行为，提供了一种神经上合理的端到端感知偏差解释，将规范理论和深度学习统一起来。 显示英文摘要

摘要： Human perceptual estimates exhibit a striking reversal in bias depending on uncertainty: they shift toward prior expectations under high sensory uncertainty, but away from them when internal noise is dominant. While Bayesian inference combined with efficient coding can explain this dual bias, existing models rely on handcrafted priors or fixed encoders, offering no account of how such representations and inferences could emerge through learning. We introduce a Generative Adversarial Inference (GAI) network that simultaneously learns sensory representations and inference strategies directly from data, without assuming explicit likelihoods or priors. Through joint reconstruction and adversarial training, the model learns a representation that approximates an efficient code consistent with information-theoretic predictions. Trained on Gabor stimuli with varying signal-to-noise ratios, GAI spontaneously reproduces the full transition from prior attraction to repulsion, and recovers the Fisher information profile predicted by efficient coding theory. It also captures the characteristic bias reversal observed in human perception more robustly than supervised or variational alternatives. These results show that a single adversarially trained network can jointly acquire an efficient sensory code and support Bayesian-consistent behavior, providing a neurally plausible, end-to-end account of perceptual bias that unifies normative theory and deep learning.