Neural representations of visual working memory (VWM) are noisy, and thus, decisions based on VWM are inevitably subject to uncertainty. However, the mechanisms by which the brain simultaneously represents the content and uncertainty of memory remain largely unknown. Here, inspired by the theory of probabilistic population codes, we test the hypothesis that the human brain represents an item maintained in VWM as a probability distribution over stimulus feature space, thereby capturing both its content and uncertainty. We used a neural generative model to decode probability distributions over memorized locations from fMRI activation patterns. We found that the mean of the probability distribution decoded from retinotopic cortical areas predicted memory reports on a trial-by-trial basis. Moreover, in several of the same mid-dorsal stream areas, the spread of the distribution predicted subjective trial-by-trial uncertainty judgments. These results provide evidence that VWM content and uncertainty are jointly represented by probabilistic neural codes.

视觉工作记忆 (VWM) 的神经表征存在噪声，因此基于 VWM 的决策不可避免地会受到不确定性的影响。然而，大脑同时代表记忆内容和不确定性的机制仍然很大程度上未知。在这里，受概率群体代码理论的启发，我们测试了这样的假设：人脑将 VWM 中维护的项目表示为刺激特征空间上的概率分布，从而捕获其内容和不确定性。我们使用神经生成模型来解码功能磁共振成像激活模式中记忆位置的概率分布。我们发现，从视网膜专题皮层区域解码的概率分布平均值可以在逐次试验的基础上预测记忆报告。此外，在几个相同的中背流区域，分布的扩展预测了主观的逐次试验的不确定性判断。这些结果证明 VWM 内容和不确定性是由概率神经代码共同表示的。