Institut des Sciences Cognitives Marc Jeannerod, CNRS, UMR5229, 69675 Bron Cedex, France.
Department of Physiology and Pharmacology, Sapienza University of Rome, 00185 Rome, Italy.
Cereb Cortex. 2022 Jun 16;32(13):2745-2761. doi: 10.1093/cercor/bhab378.
In everyday life, we are continuously struggling at focusing on our current goals while at the same time avoiding distractions. Attention is the neuro-cognitive process devoted to the selection of behaviorally relevant sensory information while at the same time preventing distraction by irrelevant information. Distraction can be prevented proactively, by strategically prioritizing task-relevant information at the expense of irrelevant information, or reactively, by suppressing the ongoing processing of distractors. The distinctive neuronal signature of these suppressive mechanisms is still largely unknown. Thanks to machine-learning decoding methods applied to prefrontal cortical activity, we monitor the dynamic spatial attention with an unprecedented spatial and temporal resolution. We first identify independent behavioral and neuronal signatures for long-term (learning-based spatial prioritization) and short-term (dynamic spatial attention) mechanisms. We then identify distinct behavioral and neuronal signatures for proactive and reactive suppression mechanisms. We find that while distracting task-relevant information is suppressed proactively, task-irrelevant information is suppressed reactively. Critically, we show that distractor suppression, whether proactive or reactive, strongly depends on the implementation of both long-term and short-term mechanisms of selection. Overall, we provide a unified neuro-cognitive framework describing how the prefrontal cortex deals with distractors in order to flexibly optimize behavior in dynamic environments.
在日常生活中,我们不断地努力专注于当前的目标,同时避免分心。注意力是一种神经认知过程,用于选择行为相关的感觉信息,同时防止无关信息的干扰。分心可以通过主动策略来预防,即通过有策略地优先处理与任务相关的信息,而牺牲与任务无关的信息;也可以通过抑制正在进行的干扰物处理来被动预防。这些抑制机制的独特神经元特征在很大程度上仍然未知。借助应用于前额叶皮层活动的机器学习解码方法,我们以前所未有的空间和时间分辨率监测动态空间注意力。我们首先确定了长期(基于学习的空间优先化)和短期(动态空间注意力)机制的独立行为和神经元特征。然后,我们确定了主动和被动抑制机制的独特行为和神经元特征。我们发现,虽然有干扰的任务相关信息是主动抑制的,但任务无关的信息是被动抑制的。关键的是,我们表明,无论是主动还是被动的干扰抑制,都强烈依赖于选择的长期和短期机制的实施。总的来说,我们提供了一个统一的神经认知框架,描述了前额叶皮层如何处理干扰物,以便在动态环境中灵活地优化行为。