[Paper Review] Hierarchical Resource Rationality Explains Human Reading Behavior

Yunpeng Bai, Xiaofu Jin+2 more|arXiv (Cornell University)|Mar 6, 2026

Reading and Literacy Development0 citations

TL;DR

The paper presents a resource-rational optimization framework for reading that balances expected comprehension against cognitive and temporal costs. It uses a hierarchical model to link eye movements, word recognition, memory, and rereading.

ABSTRACT

Reading is a pervasive and cognitively demanding activity that underpins modern human culture. It is a prime instance of a class of tasks where eye movements are coordinated for the purpose of comprehension. Existing theories explain either eye movements or comprehension during reading, but the critical link between the two remains unclear. Here, we propose resource-rational optimization as a unifying principle governing adaptive reading behavior. Eye movements are selected to maximize expected comprehension while minimizing cognitive and temporal costs, organized hierarchically across nested time scales: fixation decisions support word recognition; sentence-level integration guides skipping and regression; and text-level comprehension goals shape memory construction and rereading. A computational implementation successfully replicates an unprecedented range of findings in human reading, from lexical effects to comprehension outcomes. Together, these results suggest that resource rationality provides a general mechanism for coordinating perception, memory, and action in knowledge-intensive human behaviors, offering a principled account of how complex cognitive skills adapt to limited resources.

Motivation & Objective

• Motivate and quantify the cognitive demands of reading and the need for a unifying theory.
• Propose a resource-rational optimization framework that explains how reading behavior adapts to limited resources.
• Show that hierarchical time-scale organization guides fixation, skipping, regression, and memory processes to optimize comprehension.

Proposed method

• Formulate reading as a resource-rational optimization problem that maximizes expected comprehension while minimizing cognitive and temporal costs.
• Implement a computational model with hierarchical decision levels: fixation decisions for word recognition; sentence-level integration guiding skipping and regression; text-level goals shaping memory construction and rereading.
• Demonstrate the model reproduces a wide range of empirical reading phenomena from lexical effects to comprehension outcomes.
• Bridge perception, memory, and action by treating reading as an adaptive behavior under limited attentional and temporal resources.

Experimental results

Research questions

• RQ1 Can resource-rational optimization explain the link between eye movements and comprehension in reading?
• RQ2 Does a hierarchical, time-scale–nested model replicate known reading phenomena from word processing to memory and rereading?
• RQ3 How do fixation, skipping, regressive saccades, and memory strategies cohere under resource constraints to optimize comprehension?

Key findings

• A computational implementation reproduces a broad spectrum of reading findings from lexical effects to comprehension outcomes.
• Eye movements and memory strategies emerge as adaptive solutions under limited resources, aligned with hierarchical structure across time scales.
• The resource-rational framework provides a general mechanism coordinating perception, memory, and action in knowledge-intensive tasks like reading.