Department of Psychology, New York University, New York, NY 10003, USA.
Department of Psychology, New York University, New York, NY 10003, USA; Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA 93106, USA.
Neuron. 2021 Nov 17;109(22):3699-3712.e6. doi: 10.1016/j.neuron.2021.08.022. Epub 2021 Sep 14.
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) 的神经表示是嘈杂的，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，大脑同时表示记忆内容和不确定性的机制在很大程度上仍然未知。在这里，受概率群体编码理论的启发，我们检验了这样一个假设，即大脑将 VWM 中保持的项目表示为刺激特征空间上的概率分布，从而同时捕获其内容和不确定性。我们使用神经生成模型从 fMRI 激活模式中解码记忆位置的概率分布。我们发现，从视网膜区域解码的概率分布的平均值可以逐次预测记忆报告。此外，在几个相同的中背侧流区域中，分布的扩展预测了主观的逐次不确定性判断。这些结果提供了证据，表明 VWM 内容和不确定性由概率神经编码共同表示。
