Caponi Camilla, Maldonado Moscoso Paula A, Castaldi Elisa, Arrighi Roberto, Grasso Paolo A
Department of Neuroscience, Psychology, Pharmacology and Child Health, University of Florence, Florence, Tuscany, Italy.
Centre for Mind/Brain Sciences - CIMeC, University of Trento, Rovereto, Italy.
Front Neurosci. 2023 May 24;17:1190317. doi: 10.3389/fnins.2023.1190317. eCollection 2023.
The moment we see a group of objects, we can appreciate its numerosity. Our numerical estimates can be imprecise for large sets (>4 items), but they become much faster and more accurate if items are clustered into groups compared to when they are randomly displaced. This phenomenon, termed groupitizing, is thought to leverage on the capacity to quickly identify groups from 1 to 4 items (subitizing) within larger sets, however evidence in support for this hypothesis is scarce. The present study searched for an electrophysiological signature of subitizing while participants estimated grouped numerosities exceeding this range by measuring event-related potential (ERP) responses to visual arrays of different numerosities and spatial configurations. The EEG signal was recorded while 22 participants performed a numerosity estimation task on arrays with numerosities in the subitizing (3 or 4) or estimation (6 or 8) ranges. In the latter case, items could be spatially arranged into subgroups (3 or 4) or randomly scattered. In both ranges, we observed a decrease in N1 peak latency as the number of items increased. Importantly, when items were arranged to form subgroups, we showed that the N1 peak latency reflected both changes in total numerosity and changes in the number of subgroups. However, this result was mainly driven by the number of subgroups to suggest that clustered elements might trigger the recruitment of the subitizing system at a relatively early stage. At a later stage, we found that P2p was mostly modulated by the total numerosity in the set, with much less sensitivity for the number of subgroups these might be segregated in. Overall, this experiment suggests that the N1 component is sensitive to both local and global parcelling of elements in a scene suggesting that it could be crucially involved in the emergence of the groupitizing advantage. On the other hand, the later P2p component seems to be much more bounded to the global aspects of the scene coding the total number of elements while being mostly blind to the number of subgroups in which elements are parsed.
当我们看到一组物体时，就能感知其数量。对于较大的集合（>4个物体），我们的数字估计可能不准确，但与物体随机分布时相比，如果将物体聚集成组，我们的估计会变得更快且更准确。这种现象被称为“分组化”，人们认为它利用了在较大集合中快速识别1到4个物体（即“一眼识数”）的能力，然而支持这一假设的证据很少。本研究在参与者估计超过此范围的分组数量时，通过测量对不同数量和空间配置的视觉阵列的事件相关电位（ERP）反应，寻找“一眼识数”的电生理特征。在22名参与者对数量处于“一眼识数”范围（3或4）或估计范围（6或8）的阵列执行数量估计任务时，记录了脑电图信号。在后一种情况下，物体可以在空间上排列成子组（3或4个）或随机散布。在这两个范围内，我们观察到随着物体数量的增加，N1峰值潜伏期缩短。重要的是，当物体被排列成子组时，我们发现N1峰值潜伏期既反映了总数的变化，也反映了子组数量的变化。然而，这一结果主要由子组数量驱动，表明聚集的元素可能在相对早期阶段就触发了“一眼识数”系统的启用。在后期阶段，我们发现P2p主要受集合中总数的调节，对这些元素可能被分成的子组数量的敏感性要低得多。总体而言，本实验表明N1成分对场景中元素的局部和全局划分都很敏感，这表明它可能在“分组化”优势的产生中起关键作用。另一方面，较晚出现的P2p成分似乎更受场景全局方面的限制，编码元素的总数，而对元素被解析成的子组数量基本不敏感。
