Bayesian models of behavior suggest that organisms represent uncertainty associated with sensory variables. However, the neural code of uncertainty remains elusive. A central hypothesis is that uncertainty is encoded in the population activity of cortical neurons in the form of likelihood functions. We tested this hypothesis by simultaneously recording population activity from primate visual cortex during a visual categorization task in which trial-to-trial uncertainty about stimulus orientation was relevant for the decision. We decoded the likelihood function from the trial-to-trial population activity and found that it predicted decisions better than a point estimate of orientation. This remained true when we conditioned on the true orientation, suggesting that internal fluctuations in neural activity drive behaviorally meaningful variations in the likelihood function. Our results establish the role of population-encoded likelihood functions in mediating behavior and provide a neural underpinning for Bayesian models of perception.

中文翻译：

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视觉皮层概率计算的神经基础。

贝叶斯行为模型表明，生物体代表与感觉变量相关的不确定性。然而，不确定性的神经代码仍然难以捉摸。一个中心假设是，不确定性以似然函数的形式编码在皮质神经元的群体活动中。我们通过在视觉分类任务期间同时记录灵长类动物视觉皮层的群体活动来测试这一假设，其中刺激方向的试验间不确定性与决策相关。我们从试验到试验的群体活动中解码了似然函数，发现它比方向的点估计更好地预测决策。当我们以真实方向为条件时，这一点仍然成立，这表明神经活动的内部波动会驱动似然函数的行为上有意义的变化。我们的结果确立了群体编码似然函数在调节行为中的作用，并为贝叶斯感知模型提供了神经基础。