The medial temporal lobe (MTL) supports a constellation of memory-related behaviors. Its involvement in perceptual processing, however, has been subject to enduring debate. This debate centers on perirhinal cortex (PRC), an MTL structure at the apex of the ventral visual stream (VVS). Here we leverage a deep learning framework that approximates visual behaviors supported by the VVS (i.e., lacking PRC). We first apply this approach retroactively, modeling 30 published visual discrimination experiments: excluding non-diagnostic stimulus sets, there is a striking correspondence between VVS-modeled and PRC-lesioned behavior, while each is outperformed by PRC-intact participants. We corroborate and extend these results with a novel experiment, directly comparing PRC-intact human performance to electrophysiological recordings from the macaque VVS: PRC-intact participants outperform a linear readout of high-level visual cortex. By situating lesion, electrophysiological, and behavioral results within a shared computational framework, this work resolves decades of seemingly inconsistent findings surrounding PRC involvement in perception.

内侧颞叶 (MTL) 支持一系列与记忆相关的行为。然而，它对知觉处理的参与一直受到持久的争论。这场争论的焦点是鼻周皮层 (PRC)，这是位于腹侧视觉流 (VVS) 顶端的 MTL 结构。在这里，我们利用深度学习框架来近似 VVS 支持的视觉行为（即缺乏 PRC）。我们首先回顾性地应用这种方法，对 30 个已发表的视觉辨别实验进行建模：排除非诊断性刺激集，VVS 建模的行为与 PRC 受损的行为之间存在惊人的对应关系，而每个行为均优于 PRC 完好的参与者。我们通过一项新颖的实验证实并扩展了这些结果，直接将 PRC 完整的人类表现与猕猴 VVS 的电生理记录进行比较：PRC 完整的参与者优于高级视觉皮层的线性读数。通过将病变、电生理学和行为结果置于共享的计算框架内，这项工作解决了数十年来围绕 PRC 参与感知的看似不一致的发现。