New AI Model Recreates How Humans Read—and Why Our Eyes Decide to Go Back

Reading may feel effortless, but every line of text triggers a complex series of decisions. The eyes jump between words, pause on difficult passages, skip familiar material and sometimes return to earlier sentences when meaning becomes unclear. Researchers at Aalto University and international partner institutions have now developed an artificial intelligence model that reproduces these decisions with unprecedented accuracy, offering a detailed account of how people allocate attention while reading.

The model is designed not merely to imitate eye movements, but to explain the reasoning behind them. It uses reinforcement learning, an approach widely used in robotics and game-playing systems, to learn how to direct its gaze through words, sentences and paragraphs. Rather than copying patterns from a database of human eye-tracking recordings, the system is trained to decide which piece of text to examine next in order to build the most useful understanding within a limited amount of time.

The research, published in Nature Human Behaviour, is based on a principle known as resource rationality. This theory proposes that the human mind constantly balances the benefits of gathering more information against the cost of spending time and mental energy. During reading, that calculation may determine whether a person carefully studies an unfamiliar word, moves quickly across a predictable sentence or backtracks to resolve a confusing idea.

“Our brain is constantly deciding where to look, what to skip, and when to backtrack—spending attention like a budget to maximize understanding,” says Professor Shengdong Zhao of City University of Hong Kong, one of the researchers involved in the work. The new model represents this process at several interacting levels. It can decide how to handle an individual word, how to navigate a sentence and how to move through the wider structure of a document.

This hierarchy is central to the model’s technical design. At the word level, the system evaluates whether a term is familiar, informative or essential to the meaning of the passage. At the sentence level, it assesses how the current sentence fits with what has already been read. At the text level, it maintains a condensed representation of the document’s overall content. If an important word or clause is missing from that internal representation, the model can direct its simulated gaze back toward the relevant section to recover the information.

The researchers also introduced adjustable reader characteristics into the system. These parameters include language knowledge, memory capacity, visual abilities and eye-movement speed. The result is not a single rigid reader, but a flexible model that can represent different reading profiles. A fast reader with strong memory may move rapidly through a passage, while someone with limited working memory may revisit earlier sentences more frequently to maintain a coherent understanding.

Earlier computational approaches typically learned from large collections that paired text with eye-tracking data. Such systems could reproduce common patterns, including where readers tended to pause, but they often struggled to generalize beyond the language, text type or reading conditions represented in their training data. According to Professor Antti Oulasvirta of Aalto University, those models primarily mimicked behavior. The new approach instead gives the system an objective—understand as much as possible under specific time and cognitive constraints—and allows reinforcement learning to discover effective strategies.

To train the model, the researchers placed it in an environment containing millions of texts. The system received feedback based on how well it retained and represented the meaning of what it read. Over time, it learned to optimize its simulated eye movements, choosing when to move forward, when to slow down and when to return to previously viewed material. When the researchers compared its decisions with real human eye-tracking data, the model closely mirrored the behavior of readers and could be adapted to resemble different kinds of readers.

The achievement could have consequences far beyond laboratory models of cognition. A system that can predict how people allocate attention may help create augmented-reality displays that automatically adjust the pace, layout and density of text. Smart glasses, for example, might present information in a way that suits a user’s reading speed, memory limitations or immediate environment. Digital documents could also be redesigned dynamically, highlighting essential information or restructuring difficult passages without changing their underlying meaning.

The researchers envision applications in education, accessibility and professional communication. A complex legal document could be transformed into versions suited to readers with different levels of language proficiency, while real-time systems could present essential information to drivers without creating dangerous distractions. The team plans to investigate whether the model can support people with dyslexia or low language proficiency. If successful, the technology could mark a shift from mass-produced text toward reading experiences tailored to individual minds, situations and goals.

Subject of Research: Not applicable

Article Title: Hierarchical Resource Rationality Explains Human Reading Behavior

News Publication Date: 10-Aug-2026

Web References: https://doi.org/10.1038/s41562-026-02534-0

References: Nature Human Behaviour, DOI: 10.1038/s41562-026-02534-0

Image Credits: Aalto University

Keywords: artificial intelligence, reading, eye tracking, reinforcement learning, human cognition, resource rationality, augmented reality, personalized text, dyslexia, Aalto University