Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham B15 2TT, United Kingdom
Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham B15 2TT, United Kingdom.
J Neurosci. 2023 Sep 13;43(37):6415-6429. doi: 10.1523/JNEUROSCI.0766-23.2023. Epub 2023 Aug 10.
Reward-related activity in the dopaminergic midbrain is thought to guide animal behavior, in part by boosting the perceptual and attentional processing of reward-predictive environmental stimuli. In line with this incentive salience hypothesis, studies of human visual search have shown that simple synthetic stimuli, such as lines, shapes, or Gabor patches, capture attention to their location when they are characterized by reward-associated visual features, such as color. In the real world, however, we commonly search for members of a category of visually heterogeneous objects, such as people, cars, or trees, where category examples do not share low-level features. Is attention captured to examples of a reward-associated real-world object category? Here, we have human participants search for targets in photographs of city and landscapes that contain task-irrelevant examples of a reward-associated category. We use the temporal precision of EEG machine learning and ERPs to show that these distractors acquire incentive salience and draw attention, but do not capture it. Instead, we find evidence of rapid, stimulus-triggered attentional suppression, such that the neural encoding of these objects is degraded relative to neutral objects. Humans appear able to suppress the incentive salience of reward-associated objects when they know these objects will be irrelevant, supporting the rapid deployment of attention to other objects that might be more useful. Incentive salience is thought to underlie key behaviors in eating disorders and addiction, among other conditions, and the kind of suppression identified here likely plays a role in mediating the attentional biases that emerge in these circumstances. Like other animals, humans are prone to notice and interact with environmental objects that have proven rewarding in earlier experience. However, it is common that such objects have no immediate strategic use and are therefore distracting. Do these reward-associated real-world objects capture our attention, despite our strategic efforts otherwise? Or are we able to strategically control the impulse to notice them? Here we use machine learning classification of human electrical brain activity to show that we can establish strategic control over the salience of naturalistic reward-associated objects. These objects draw our attention, but do not necessarily capture it, and this kind of control may play an important role in mediating conditions like eating disorder and addiction.
中脑的多巴胺能奖赏相关活动被认为可以指导动物行为，部分原因是增强了对奖赏预测环境刺激的感知和注意力处理。与这一激励显著性假说一致，人类视觉搜索研究表明，当简单的合成刺激（如线条、形状或 Gabor 补丁）具有与奖赏相关的视觉特征（如颜色）时，它们会捕获到其位置的注意力。然而，在现实世界中，我们通常会搜索视觉上异构的对象类别，如人、汽车或树木，而这些类别示例并不共享低级特征。那么，与奖赏相关的现实世界对象类别的示例会吸引注意力吗？在这里，我们让人类参与者在包含与任务无关的奖赏相关类别的示例的城市和景观照片中搜索目标。我们使用 EEG 机器学习和 ERP 的时间精度来表明这些分心物具有激励显著性并吸引注意力，但不会捕获它。相反，我们发现了快速、刺激触发的注意力抑制的证据，使得这些对象的神经编码相对于中性对象退化。当人类知道这些对象将是无关紧要的时，他们似乎能够抑制与奖赏相关对象的激励显著性，支持快速将注意力转移到其他可能更有用的对象上。激励显著性被认为是饮食失调和成瘾等其他条件下关键行为的基础，而这里确定的那种抑制可能在调节这些情况下出现的注意力偏差方面发挥作用。像其他动物一样，人类很容易注意到并与以前经验中证明有奖励的环境对象互动。然而，常见的情况是，这些对象没有直接的战略用途，因此很分散注意力。尽管我们有其他策略，但这些与奖赏相关的现实世界对象是否会吸引我们的注意力？或者我们是否能够从策略上控制注意它们的冲动？在这里，我们使用人类电脑活动的机器学习分类来表明，我们可以对自然奖赏相关对象的显著性进行策略控制。这些对象吸引了我们的注意力，但不一定能吸引注意力，这种控制可能在调节饮食失调和成瘾等情况方面发挥重要作用。
