Department of Psychology, University of Cambridge, Downing Street, Cambridge CB2 3EB, UK; Queensland Brain Institute, The University of Queensland, QBI Building 79, St Lucia, QLD 4072, Australia; School of Psychology, The University of Queensland, McElwain Building 24a, St Lucia, QLD 4072, Australia.
Department of Psychology, University of Cambridge, Downing Street, Cambridge CB2 3EB, UK.
Cognition. 2021 Sep;214:104763. doi: 10.1016/j.cognition.2021.104763. Epub 2021 May 29.
Accounts of working memory based on independent item representations may overlook a possible contribution of ensemble statistics, higher-order regularities of a scene such as the mean or variance of a visual attribute. Here we used change detection tasks to investigate the hypothesis that observers store ensemble statistics in working memory and use them to detect changes in the visual environment. We controlled changes to the ensemble mean or variance between memory and test displays across six experiments. We made specific predictions of observers' sensitivity using an optimal summation model that integrates evidence across separate items but does not detect changes in ensemble statistics. We found strong evidence that observers outperformed this model, but only when task difficulty was high, and only for changes in stimulus variance. Under these conditions, we estimated that the variance of items contributed to change detection sensitivity more strongly than any individual item in this case. In contrast, however, we found strong evidence against the hypothesis that the average feature value is stored in working memory: when the mean of memoranda changed, sensitivity did not differ from the optimal summation model, which was blind to the ensemble mean, in five out of six experiments. Our results reveal that change detection is primarily limited by uncertainty in the memory of individual features, but that memory for the variance of items can facilitate detection under a limited set of conditions that involve relatively high working memory demands.
基于独立项目表示的工作记忆的描述可能忽略了集合统计的可能贡献，例如场景的平均值或方差等更高阶的规则。在这里，我们使用变化检测任务来研究以下假设：观察者在工作记忆中存储集合统计信息，并利用它们来检测视觉环境中的变化。我们在六个实验中控制了记忆和测试显示之间的集合平均值或方差的变化。我们使用一种最优求和模型对观察者的敏感性进行了具体预测，该模型整合了独立项目的证据，但无法检测到集合统计数据的变化。我们发现有强有力的证据表明，观察者的表现优于该模型，但只有在任务难度较高且仅针对刺激方差变化的情况下。在这些条件下，我们估计，在这种情况下，项目的方差比任何单个项目对变化检测敏感性的贡献都更强。然而，相比之下，我们有强有力的证据表明，工作记忆中没有存储平均特征值的假设：当记忆的平均值发生变化时，敏感性与最优求和模型没有差异，该模型对集合平均值不敏感，在六个实验中的五个实验中均如此。我们的结果表明，变化检测主要受到对单个特征记忆的不确定性的限制，但在涉及相对较高工作记忆需求的有限条件下，项目方差的记忆可以促进检测。
