Eye movements in reading play a crucial role in psycholinguistic research studying the cognitive mechanisms underlying human language processing. More recently, the tight coupling between eye movements and cognition has also been leveraged for language-related machine learning tasks such as the interpretability, enhancement, and pre-training of language models, as well as the inference of reader- and text-specific properties. However, scarcity of eye movement data and its unavailability at application time poses a major challenge for this line of research. Initially, this problem was tackled by resorting to cognitive models for synthesizing eye movement data. However, for the sole purpose of generating human-like scanpaths, purely data-driven machine-learning-based methods have proven to be more suitable. Following recent advances in adapting diffusion processes to discrete data, we propose ScanDL, a novel discrete sequence-to-sequence diffusion model that generates synthetic scanpaths on texts. By leveraging pre-trained word representations and jointly embedding both the stimulus text and the fixation sequence, our model captures multi-modal interactions between the two inputs. We evaluate ScanDL within- and across-dataset and demonstrate that it significantly outperforms state-of-the-art scanpath generation methods. Finally, we provide an extensive psycholinguistic analysis that underlines the model's ability to exhibit human-like reading behavior. Our implementation is made available at https://github.com/DiLi-Lab/ScanDL.

翻译：在心理语言学研究中，阅读中的眼动对于理解人类语言处理的认知机制至关重要。近年来，眼动与认知之间的紧密耦合也被应用于语言相关的机器学习任务，如语言模型的可解释性、增强和预训练，以及推断读者和文本的特定属性。然而，眼动数据的稀缺性及其在应用时的不可获取性，对该研究方向构成了重大挑战。最初，这一问题通过借助认知模型合成眼动数据来解决。但就生成类人扫描路径这一单一目标而言，纯数据驱动的机器学习方法已被证明更为适用。根据最近将扩散过程适配到离散数据的研究进展，我们提出了ScanDL——一种新型离散序列到序列扩散模型，用于生成文本上的合成扫描路径。通过利用预训练的词嵌入并联合嵌入刺激文本和注视序列，我们的模型捕捉了两类输入之间的多模态交互。我们在数据集内和跨数据集上评估了ScanDL，结果表明其显著优于现有的扫描路径生成方法。最后，我们提供了广泛的心理语言学分析，强调了模型展现类人阅读行为的能力。我们的实现代码已公开在https://github.com/DiLi-Lab/ScanDL。