A key challenge in understanding the functional organisation of visual cortex stems from the fact that only a small proportion of the objects experienced during natural viewing can be presented in a typical experiment. This constraint often leads to experimental designs that compare responses to objects from experimenter-defined stimulus conditions, potentially limiting the interpretation of the data. To overcome this issue, we used images from the THINGS initiative, which provides a systematic sampling of natural objects. A data-driven analysis was then applied to reveal the functional organisation of the visual brain, incorporating both perceptual and neural responses to these objects. Perceptual properties of the objects were taken from an analysis of similarity judgements, and neural properties were taken from whole brain fMRI responses to the same objects. Partial least squares regression (PLSR) was then used to predict neural responses across the brain from the perceptual properties while simultaneously applying dimensionality reduction. The PLSR model accurately predicted neural responses across visual cortex using only a small number of components. These components revealed smooth, graded neural topographies, which were similar in both hemispheres, and captured a variety of object properties including animacy, real-world size, and object category. However, they did not accord in any simple way with previous theoretical perspectives on object perception. Instead, our findings suggest that visual cortex encodes information in a statistically efficient manner, reflecting natural variability among objects.Significance statement The ability to recognise objects is fundamental to how we interact with our environment, yet the organising principles underlying neural representations of visual objects remain contentious. In this study, we sought to address this question by analysing perceptual and neural responses to a large, unbiased sample of objects. Using a data-driven approach, we leveraged perceptual properties of objects to predict neural responses using a small number of components. This model predicted neural responses with a high degree of accuracy across visual cortex. The components did not directly align with previous explanations of object perception. Instead, our findings suggest the organisation of the visual brain is based on the statistical properties of objects in the natural world.

中文翻译：

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对自然物体的感知和神经反应的数据驱动分析揭示了人类视觉认知的组织原则。


理解视觉皮层功能组织的一个关键挑战源于这样一个事实，即在自然观看过程中体验到的物体中只有一小部分可以在典型的实验中呈现。这种约束通常会导致实验设计比较实验者定义的刺激条件对物体的响应，这可能会限制数据的解释。为了解决这个问题，我们使用了来自 THINGS 计划的图像，该计划提供了自然对象的系统采样。然后应用数据驱动的分析来揭示视觉大脑的功能组织，包括对这些对象的感知和神经反应。物体的感知特性取自相似性判断分析，神经特性取自全脑 fMRI 对相同物体的反应。然后使用偏最小二乘回归 （PLSR） 从感知特性预测整个大脑的神经反应，同时应用降维。PLSR 模型仅使用少量组件就准确预测了整个视觉皮层的神经反应。这些组件揭示了平滑、渐变的神经地形，这些拓扑在两个半球中相似，并捕获了各种对象属性，包括动画、实际大小和对象类别。然而，它们与以前关于物体感知的理论观点并不简单一致。相反，我们的研究结果表明，视觉皮层以统计上有效的方式编码信息，反映了物体之间的自然变化。意义陈述 识别物体的能力是我们与环境互动的基础，但视觉对象神经表征背后的组织原则仍然存在争议。在这项研究中，我们试图通过分析对大量无偏物体样本的感知和神经反应来解决这个问题。使用数据驱动的方法，我们利用对象的感知特性来预测使用少量组件的神经反应。该模型在整个视觉皮层中以高度的准确性预测了神经反应。这些组件与之前对物体感知的解释并不直接一致。相反，我们的研究结果表明，视觉大脑的组织是基于自然界中物体的统计特性。