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

Integrating large language models and active inference to understand eye  movements in reading and dyslexia

Reading, much like music listening, is an immersive experience that
transports readers while taking them on an emotional journey. Listening to
complementary music has the potential to amplify the reading experience,
especially when the music is stylistically cohesive and emotionally relevant.
In this paper, we propose the first fully automatic method to build a dense
soundtrack for books, which can play high-quality instrumental music for the
entirety of the reading duration. Our work employs a unique text processing and
music weaving pipeline that determines the context and emotional composition of
scenes in a chapter. This allows our method to identify and play relevant
excerpts from the soundtrack of the book's movie adaptation. By relying on the
movie composer's craftsmanship, our book soundtracks include expert-made motifs
and other scene-specific musical characteristics. We validate the design
decisions of our approach through a perceptual study. Our readers note that the
book soundtrack greatly enhanced their reading experience, due to high
immersiveness granted via uninterrupted and style-consistent music, and a
heightened emotional state attained via high precision emotion and scene
context recognition.

本文提出了一种全自动建立小说配乐的方法，该方法可以在阅读过程中播放高质量的配乐，通过文本处理和音乐编织管道识别章节中的情境和情感分布，并从小说的电影改编配乐中识别和播放相关片段。

Sonus Texere! 使用电影改编自动构建书籍的紧凑音轨

Sonus Texere! Automated Dense Soundtrack Construction for Books using Movie Adaptations

Over the past two decades, numerous studies have demonstrated how less
predictable (i.e. higher surprisal) words take more time to read. In general,
these previous studies implicitly assumed the reading process to be purely
responsive: readers observe a new word and allocate time to read it as
required. These results, however, are also compatible with a reading time that
is anticipatory: readers could, e.g., allocate time to a future word based on
their expectation about it. In this work, we examine the anticipatory nature of
reading by looking at how people's predictions about upcoming material
influence reading times. Specifically, we test anticipation by looking at the
effects of surprisal and contextual entropy on four reading-time datasets: two
self-paced and two eye-tracking. In three of four datasets tested, we find that
the entropy predicts reading times as well as (or better than) the surprisal.
We then hypothesise four cognitive mechanisms through which the contextual
entropy could impact RTs -- three of which we design experiments to analyse.
Overall, our results support a view of reading that is both anticipatory and
responsive.

本篇研究检验了阅读的预测性质，发现上下文熵对阅读时间的预测能力高于或等于惊奇程度，在四个阅读数据中三个能够预测阅读时间。此外，作者提出了四个认知机制来支持上下文熵对阅读时间的影响，从而支持阅读具有先见性和响应性的观点。