Most current models assume that the perceptual and cognitive processes of visual word recognition and reading operate upon neuronally coded domain-general low-level visual representations – typically oriented line representations. We here demonstrate, consistent with neurophysiological theories of Bayesian-like predictive neural computations, that prior visual knowledge of words may be utilized to ‘explain away’ redundant and highly expected parts of the visual percept. Subsequent processing stages, accordingly, operate upon an optimized representation of the visual input, the orthographic prediction error, highlighting only the visual information relevant for word identification. We show that this optimized representation is related to orthographic word characteristics, accounts for word recognition behavior, and is processed early in the visual processing stream, i.e., in V4 and before 200 ms after word-onset. Based on these findings, we propose that prior visual-orthographic knowledge is used to optimize the representation of visually presented words, which in turn allows for highly efficient reading processes.

中文翻译：

---

拼写预测误差作为高效视觉单词识别的基础


大多数当前模型假设视觉单词识别和阅读的感知和认知过程是在神经元编码的域通用低级视觉表示（通常是定向线表示）上进行的。我们在这里证明，与类贝叶斯预测神经计算的神经生理学理论一致，可以利用单词的先验视觉知识来“解释”视觉感知中冗余和高度期望的部分。因此，后续处理阶段对视觉输入的优化表示、拼写预测误差进行操作，仅突出显示与单词识别相关的视觉信息。我们表明，这种优化的表示与拼写单词特征相关，解释了单词识别行为，并且在视觉处理流的早期进行处理，即在 V4 中以及单词出现后 200 毫秒之前。基于这些发现，我们建议使用先前的视觉正字法知识来优化视觉呈现的单词的表示，从而实现高效的阅读过程。