Postle Bradley R
Curr Opin Behav Sci. 2015 Feb;1:40-46. doi: 10.1016/j.cobeha.2014.08.004.
Our understanding of the neural bases of visual short-term memory (STM), the ability to mentally retain information over short periods of time, is being reshaped by two important developments: the application of methods from statistical machine learning, often a variant of multivariate pattern analysis (MVPA), to functional magnetic resonance imaging (fMRI) and electroencephalographic (EEG) data sets; and advances in our understanding of the physiology and functions of neuronal oscillations. One consequence is that many commonly observed physiological "signatures" that have previously been interpreted as directly related to the retention of information in visual STM may require reinterpretation as more general, state-related changes that can accompany cognitive-task performance. Another is important refinements of theoretical models of visual STM.
我们对视觉短期记忆(STM)神经基础的理解正在被两个重要进展所重塑,视觉短期记忆即大脑在短时间内心理保留信息的能力:将统计机器学习方法(通常是多变量模式分析(MVPA)的一种变体)应用于功能磁共振成像(fMRI)和脑电图(EEG)数据集;以及我们对神经元振荡的生理机制和功能理解的进步。一个结果是,许多先前被解释为与视觉STM中信息保留直接相关的常见生理“特征”,可能需要重新解释为伴随认知任务表现的更普遍的、与状态相关的变化。另一个结果是对视觉STM理论模型的重要改进。