We present a novel computational model employing hierarchical active inference to simulate reading and eye movements. The model characterizes linguistic processing as inference over a hierarchical generative model, facilitating predictions and inferences at various levels of granularity, from syllables to sentences. Our approach combines the strengths of large language models for realistic textual predictions and active inference for guiding eye movements to informative textual information, enabling the testing of predictions. The model exhibits proficiency in reading both known and unknown words and sentences, adhering to the distinction between lexical and nonlexical routes in dual-route theories of reading. Notably, our model permits the exploration of maladaptive inference effects on eye movements during reading, such as in dyslexia. To simulate this condition, we attenuate the contribution of priors during the reading process, leading to incorrect inferences and a more fragmented reading style, characterized by a greater number of shorter saccades. This alignment with empirical findings regarding eye movements in dyslexic individuals highlights the model's potential to aid in understanding the cognitive processes underlying reading and eye movements, as well as how reading deficits associated with dyslexia may emerge from maladaptive predictive processing. In summary, our model represents a significant advancement in comprehending the intricate cognitive processes involved in reading and eye movements, with potential implications for understanding and addressing dyslexia through the simulation of maladaptive inference. It may offer valuable insights into this condition and contribute to the development of more effective interventions for treatment.

我们提出了一种新颖的计算模型，采用分层主动推理来模拟阅读和眼动。该模型将语言处理视为对层次生成模型的推理，从音节到句子的多个粒度级别上进行预测和推理。我们的方法结合了大规模语言模型的文本预测能力和主动推理的眼动引导信息，实现了对预测的测试。我们的模型表现出在阅读已知和未知词汇和句子时的熟练程度，并符合阅读双重路径理论中词汇和非词汇路径的区分。总之，我们的模型在理解阅读和眼动中涉及的复杂认知过程方面取得了显著进展，并具有通过模拟适应性推理来理解和解决阅读障碍与诵读障碍相关的潜在启示。它可能为该病症提供有价值的见解，并有助于制定更有效的治疗措施的发展。

融合大型语言模型和主动推断以理解阅读和失读中的眼动