Huizhen Tang Joann, Solomon Selina S, Kohn Adam, Sussman Elyse S
Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA.
Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA.
Brain Cogn. 2024 Dec;182:106228. doi: 10.1016/j.bandc.2024.106228. Epub 2024 Oct 25.
The current study investigated how the brain sets up expectations from stimulus regularities by evaluating the neural responses to expectations driven implicitly (by the stimuli themselves) and explicitly (by task demands). How the brain uses prior information to create expectations and what role attention plays in forming or holding predictions to efficiently respond to incoming sensory information is still debated. We presented temporal patterns of visual input while recording EEG under two different task conditions. When the patterns were task-relevant and pattern recognition was required to perform the button press task, three different event-related brain potentials (ERPs) were elicited, each reflecting a different aspect of pattern expectation. In contrast, when the patterns were task-irrelevant, none of the neural indicators of pattern recognition or pattern violation detection were observed to the same temporally structured sequences. Thus, results revealed a clear distinction between expectation and attention that was prompted by task requirements. These results provide complementary pieces of evidence that implicit exposure to a stimulus pattern may not be sufficient to drive neural effects of expectations that lead to predictive error responses. Task-driven attentional control can dissociate from stimulus-driven expectations, to effectively minimize distracting information and maximize attentional regulation.
当前的研究通过评估对由刺激本身隐含驱动和由任务要求明确驱动的期望的神经反应,来探究大脑如何根据刺激规律建立期望。大脑如何利用先验信息来创建期望,以及注意力在形成或维持预测以有效响应传入的感官信息中扮演什么角色,仍存在争议。我们在两种不同的任务条件下记录脑电图(EEG)时呈现视觉输入的时间模式。当这些模式与任务相关且执行按钮按下任务需要模式识别时,会引发三种不同的事件相关脑电位(ERP),每种电位反映模式期望的不同方面。相比之下,当这些模式与任务无关时,对于相同的时间结构序列,未观察到模式识别或模式违反检测的神经指标。因此,结果揭示了由任务要求引发的期望和注意力之间的明显区别。这些结果提供了补充证据,表明对刺激模式的隐性暴露可能不足以驱动导致预测误差反应的期望的神经效应。任务驱动的注意力控制可以与刺激驱动的期望分离,以有效减少干扰信息并最大化注意力调节。