The Picower Institute for Learning & Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
The Wellcome Trust Centre for Neuroimaging, University College London, London, UK.
Cereb Cortex. 2019 Apr 1;29(4):1670-1681. doi: 10.1093/cercor/bhy065.
There is a severe limitation in the number of items that can be held in working memory. However, the neurophysiological limits remain unknown. We asked whether the capacity limit might be explained by differences in neuronal coupling. We developed a theoretical model based on Predictive Coding and used it to analyze Cross Spectral Density data from the prefrontal cortex (PFC), frontal eye fields (FEF), and lateral intraparietal area (LIP). Monkeys performed a change detection task. The number of objects that had to be remembered (memory load) was varied (1-3 objects in the same visual hemifield). Changes in memory load changed the connectivity in the PFC-FEF-LIP network. Feedback (top-down) coupling broke down when the number of objects exceeded cognitive capacity. Thus, impaired behavioral performance coincided with a break-down of Prediction signals. This provides new insights into the neuronal underpinnings of cognitive capacity and how coupling in a distributed working memory network is affected by memory load.
工作记忆的容量非常有限。然而,其神经生理学限制仍然未知。我们想知道,这种容量限制是否可以通过神经元耦合的差异来解释。我们基于预测编码开发了一个理论模型,并使用它来分析来自前额叶皮层(PFC)、额眼区(FEF)和外侧顶内区(LIP)的交叉谱密度数据。猴子执行了一个变化检测任务。需要记住的物体数量(记忆负荷)是变化的(同一边视野中有 1-3 个物体)。记忆负荷的变化改变了 PFC-FEF-LIP 网络的连接。当物体数量超过认知能力时,反馈(自上而下)耦合就会中断。因此,行为表现的损伤与预测信号的中断恰好一致。这为认知能力的神经基础以及分布式工作记忆网络中的耦合如何受记忆负荷影响提供了新的见解。