Université de Liège.
Fund for Scientific Research FNRS, Brussels, Belgium.
J Cogn Neurosci. 2018 Feb;30(2):144-159. doi: 10.1162/jocn_a_01195. Epub 2017 Oct 6.
The dorsal attention network is consistently involved in verbal and visual working memory (WM) tasks and has been associated with task-related, top-down control of attention. At the same time, WM capacity has been shown to depend on the amount of information that can be encoded in the focus of attention independently of top-down strategic control. We examined the role of the dorsal attention network in encoding load and top-down memory control during WM by manipulating encoding load and memory control requirements during a short-term probe recognition task for sequences of auditory (digits, letters) or visual (lines, unfamiliar faces) stimuli. Encoding load was manipulated by presenting sequences with small or large sets of memoranda while maintaining the amount of sensory stimuli constant. Top-down control was manipulated by instructing participants to passively maintain all stimuli or to selectively maintain stimuli from a predefined category. By using ROI and searchlight multivariate analysis strategies, we observed that the dorsal attention network encoded information for both load and control conditions in verbal and visuospatial modalities. Decoding of load conditions was in addition observed in modality-specific sensory cortices. These results highlight the complexity of the role of the dorsal attention network in WM by showing that this network supports both quantitative and qualitative aspects of attention during WM encoding, and this is in a partially modality-specific manner.
背侧注意网络一直参与言语和视觉工作记忆(WM)任务,并且与与任务相关的、自上而下的注意力控制有关。同时,WM 能力已被证明取决于可以在注意力焦点中独立于自上而下的策略控制进行编码的信息量。我们通过在短期探针识别任务中操纵编码负荷和记忆控制要求,来检查背侧注意网络在 WM 中的编码负荷和自上而下的记忆控制中的作用,该任务用于对听觉(数字、字母)或视觉(线、不熟悉的面孔)刺激序列进行编码。通过呈现具有小或大的记忆集的序列来操纵编码负荷,同时保持恒定的感觉刺激量。通过指令参与者被动地保持所有刺激或选择性地保持来自预定义类别的刺激来操纵自上而下的控制。通过使用 ROI 和搜索灯多元分析策略,我们观察到背侧注意网络在言语和视空间模态中都为负荷和控制条件编码信息。在特定于模态的感觉皮层中还观察到了对负荷条件的解码。这些结果通过表明该网络在 WM 编码期间支持注意力的定量和定性方面,并且以部分模态特异性的方式,突出了背侧注意网络在 WM 中的作用的复杂性。