Data from: Emerging representational geometries in the visual system predict reaction times for object categorization

Recognizing an object takes just a fraction of a second, less than the blink of an eye. Applying multivariate pattern analysis, or “brain decoding”, methods to magnetoencephalography (MEG) data has allowed researchers to characterize, in high temporal resolution, the emerging representation of object categories that underlie our capacity for rapid recognition. Shortly after stimulus onset, object exemplars cluster by category in a high-dimensional activation space in the brain. In this emerging activation space, the decodability of exemplar category varies over time, reflecting the brain’s transformation of visual inputs into coherent category representations. How do these emerging representations relate to categorization behavior? Recently it has been proposed that the distance of an exemplar representation from a categorical boundary in an activation space is critical for perceptual decision-making, and that reaction times should therefore correlate with distance from the boundary. The predictions of this distance hypothesis have been born out in human inferior temporal cortex (IT), an area of the brain crucial for the representation of object categories. When viewed in the context of a time varying neural signal, the optimal time to “read out” category information is when category representations in the brain are most decodable. Here, we show that the distance from a decision boundary through activation space, as measured using MEG decoding methods, correlates with reaction times for visual categorization during the period of peak decodability. Our results suggest that the brain begins to read out information about exemplar category at the optimal time for use in choice behaviour, and support the hypothesis that the structure of the representation for objects in the visual system is partially constitutive of the decision process in recognition.识别单个物体仅需短短一瞬，甚至不及一次眨眼的时长。将多变量模式分析（multivariate pattern analysis，又称“脑解码”（brain decoding））方法应用于脑磁图（magnetoencephalography, MEG）数据，使研究者能够以极高的时间分辨率，刻画支撑人类快速识别能力的物体类别表征的动态形成过程。刺激呈现后不久，物体样例便会在大脑的高维激活空间中按类别聚类。在这一动态形成的激活空间中，样例类别的可解码性随时间动态变化，反映了大脑将视觉输入转化为连贯类别表征的过程。这些动态形成的表征与分类行为之间存在何种关联？近期有研究提出，激活空间中样例表征与类别边界的距离对知觉决策至关重要，因此反应时应与该边界距离呈相关性。该距离假说的预测已在人类下颞叶皮层（inferior temporal cortex, IT）中得到验证——这一脑区正是物体类别表征的关键区域。若结合随时间变化的神经信号来看，“读取”类别信息的最佳时机，正是大脑中类别表征可解码性达到峰值的时刻。本研究表明，通过MEG解码方法测得的、激活空间中与决策边界的距离，在可解码性峰值时段内与视觉分类任务的反应时呈显著相关。研究结果显示，大脑会在适配决策行为的最佳时机开始读取物体样例的类别信息，同时验证了如下假说：视觉系统中物体表征的结构，是识别过程中决策机制的部分构成要素。