  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.
