Patterns of brain activity contain meaningful information about the perceived world. Recent decades have welcomed a new era in neural analyses, with computational techniques from machine learning applied to neural data to decode information represented in the brain. In this article, we review how decoding approaches have advanced our understanding of visual representations and discuss efforts to characterize both the complexity and the behavioral relevance of these representations. We outline the current consensus regarding the spatiotemporal structure of visual representations and review recent findings that suggest that visual representations are at once robust to perturbations, yet sensitive to different mental states. Beyond representations of the physical world, recent decoding work has shone a light on how the brain instantiates internally generated states, for example, during imagery and prediction. Going forward, decoding has remarkable potential to assess the functional relevance of visual representations for human behavior, reveal how representations change across development and during aging, and uncover their presentation in various mental disorders.

中文翻译：

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视觉表示：神经解码的见解

大脑活动模式包含有关感知世界的有意义的信息。近几十年来，神经分析迎来了一个新时代，机器学习的计算技术应用于神经数据，以解码大脑中表示的信息。在本文中，我们回顾了解码方法如何增进我们对视觉表示的理解，并讨论描述这些表示的复杂性和行为相关性的努力。我们概述了当前关于视觉表征时空结构的共识，并回顾了最近的发现，这些发现表明视觉表征对扰动具有鲁棒性，但对不同的心理状态敏感。除了物理世界的表征之外，最近的解码工作还揭示了大脑如何实例化内部生成的状态，例如在图像和预测过程中。展望未来，解码在评估视觉表征与人类行为的功能相关性、揭示表征在发育过程中和衰老过程中如何变化以及揭示它们在各种精神障碍中的表现方面具有巨大的潜力。