Department of Psychology, Vanderbilt University, 301 Wilson Hall, Nashville, TN 37240, United States.
Cereb Cortex. 2023 Apr 4;33(8):4280-4292. doi: 10.1093/cercor/bhac342.
People vary in their general ability to compare, identify, and remember objects. Research using latent variable modeling identifies a domain-general visual recognition ability (called o) that reflects correlations among different visual tasks and categories. We measure associations between a psychometrically-sensitive measure of o and a neurometrically-sensitive measure of visual sensitivity to shape. We report evidence for distributed neural correlates of o using functional and anatomical regions-of-interest (ROIs) as well as whole brain analyses. Neural selectivity to shape is associated with o in several regions of the ventral pathway, as well as additional foci in parietal and premotor cortex. Multivariate analyses suggest the distributed effects in ventral cortex reflect a common mechanism. The network of brain areas where neural selectivity predicts o is similar to that evoked by the most informative features for object recognition in prior work, showing convergence of 2 different approaches on identifying areas that support the best object recognition performance. Because o predicts performance across many visual tasks for both novel and familiar objects, we propose that o could predict the magnitude of neural changes in task-relevant areas following experience with specific task and object category.
人们在比较、识别和记忆物体的一般能力上存在差异。使用潜在变量建模的研究确定了一种领域通用的视觉识别能力(称为 o),它反映了不同视觉任务和类别之间的相关性。我们测量了 o 的心理测量敏感度量与对形状的视觉敏感性的神经测量敏感度量之间的关联。我们使用功能和解剖学区域兴趣(ROI)以及全脑分析报告了 o 的分布式神经相关性的证据。形状的神经选择性与腹侧通路中的几个区域的 o 相关,以及顶叶和运动前皮质中的其他焦点相关。多元分析表明,腹侧皮层中的分布式效应反映了一种共同的机制。在先前的工作中,用于对象识别的最具信息量的特征可以诱发大脑区域的网络,该网络中的神经选择性预测 o,表明两种不同方法在确定支持最佳对象识别性能的区域方面的趋同。由于 o 可以预测许多视觉任务和新的和熟悉的物体的性能,我们提出 o 可以预测特定任务和对象类别经验后与任务相关区域的神经变化的幅度。