Padova Neuroscience Center, University of Padova, Padova, Italy.
Fondazione Bruno Kessler, Povo, Italy.
PLoS Comput Biol. 2024 Oct 10;20(10):e1012521. doi: 10.1371/journal.pcbi.1012521. eCollection 2024 Oct.
The ability to solve complex tasks relies on the adaptive changes occurring in the spatio-temporal organization of brain activity under different conditions. Altered flexibility in these dynamics can lead to impaired cognitive performance, manifesting for instance as difficulties in attention regulation, distraction inhibition, and behavioral adaptation. Such impairments result in decreased efficiency and increased effort in accomplishing goal-directed tasks. Therefore, developing quantitative measures that can directly assess the effort involved in these transitions using neural data is of paramount importance. In this study, we propose a framework to associate cognitive effort during the performance of tasks with electroencephalography (EEG) activation patterns. The methodology relies on the identification of discrete dynamical states (EEG microstates) and optimal transport theory. To validate the effectiveness of this framework, we apply it to a dataset collected during a spatial version of the Stroop task, a cognitive test in which participants respond to one aspect of a stimulus while ignoring another, often conflicting, aspect. The Stroop task is a cognitive test where participants must respond to one aspect of a stimulus while ignoring another, often conflicting, aspect. Our findings reveal an increased cost linked to cognitive effort, thus confirming the framework's effectiveness in capturing and quantifying cognitive transitions. By utilizing a fully data-driven method, this research opens up fresh perspectives for physiologically describing cognitive effort within the brain.
解决复杂任务的能力依赖于大脑活动在不同条件下的时空组织的适应性变化。这些动态的灵活性改变可能导致认知表现受损，例如在注意力调节、分心抑制和行为适应方面出现困难。这些损伤导致在完成目标导向任务时效率降低，努力增加。因此，开发使用神经数据直接评估这些转变所涉及的认知努力的定量测量方法至关重要。在这项研究中，我们提出了一个将任务执行过程中的认知努力与脑电图（EEG）激活模式相关联的框架。该方法依赖于离散动力学状态（EEG 微状态）的识别和最优传输理论。为了验证该框架的有效性，我们将其应用于在空间版 Stroop 任务中收集的数据集，这是一种认知测试，参与者需要对刺激的一个方面做出反应，同时忽略另一个经常与之冲突的方面。Stroop 任务是一种认知测试，参与者必须对刺激的一个方面做出反应，同时忽略另一个经常与之冲突的方面。我们的研究结果揭示了与认知努力相关的成本增加，从而证实了该框架在捕捉和量化认知转变方面的有效性。通过利用完全数据驱动的方法，这项研究为在大脑中生理描述认知努力开辟了新的视角。
