Taylor Jack E, Sinn Rasmus, Iaia Cosimo, Fiebach Christian J
Department of Psychology, Goethe University Frankfurt, Frankfurt, Germany.
School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK.
Neurobiol Lang (Camb). 2025 Sep 29;6. doi: 10.1162/NOL.a.19. eCollection 2025.
Letter processing plays a key role in visual word recognition. However, word recognition models typically overlook or greatly simplify early perceptual processes of letter recognition. We suggest that optimal transport theory may provide a computational framework for describing letter shape processing. We use representational similarity analysis to show that optimal transport cost (Wasserstein distance) between pairs of letters aligns with neural activity elicited by visually presented letters <225 ms after stimulus onset, outperforming an existing approach based on shape overlap. We additionally show that optimal transport can capture the emergence of geometric invariances (e.g., to position or size) observed in letter perception. Finally, we demonstrate that Wasserstein distance predicts neural activity similarly well to features from artificial networks trained to classify images and letters. However, whereas representations in artificial neural networks emerge in a computationally unconstrained manner, our proposal provides a computationally explicit route to modeling the earliest orthographic processes.
字母处理在视觉单词识别中起着关键作用。然而，单词识别模型通常会忽略或极大地简化字母识别的早期感知过程。我们认为最优传输理论可能为描述字母形状处理提供一个计算框架。我们使用表征相似性分析表明，字母对之间的最优传输成本（瓦瑟斯坦距离）与刺激开始后<225毫秒视觉呈现字母引发的神经活动相一致，优于基于形状重叠的现有方法。我们还表明，最优传输可以捕捉在字母感知中观察到的几何不变性（例如对位置或大小的不变性）的出现。最后，我们证明瓦瑟斯坦距离预测神经活动的效果与训练用于对图像和字母进行分类的人工网络的特征相似。然而，虽然人工神经网络中的表征以计算无约束的方式出现，但我们的提议提供了一条计算明确的途径来对最早的正字法过程进行建模。
