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Using reinforcement learning to examine dynamic attention allocation during reading.

Yanping Liu¹, Erik D Reichle, Ding-Guo Gao

¹Department of Psychology, Sun Yat-Sen University.

Cognitive Science

|February 26, 2013

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Artificial reading agents show a strong preference for serial word processing, reading one word at a time. This finding offers insights into how human attention is allocated during reading.

Area of Science:

Cognitive Psychology
Computational Neuroscience
Human-Computer Interaction

Background:

The debate in reading research centers on whether attention is allocated serially (one word at a time) or in parallel (multiple words concurrently).
Understanding attentional allocation is crucial for explaining reading mechanisms and developing reading aids.

Purpose of the Study:

To investigate attentional allocation during reading using computational models.
To determine whether artificial reading agents favor serial or parallel word processing.

Main Methods:

Simulations were conducted using artificial reading agents designed to learn efficient attention allocation.
Agents were programmed to dynamically allocate attention to 1-4 words during simulated reading tasks.

Keywords:

Attention Eye movements Neural networks Reading Reinforcement learning

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Existing models of artificial reading agents were utilized and adapted for this study.

Main Results:

Simulation results demonstrated a strong preference for serial word processing among the artificial agents.
Concurrent processing of more than one word was observed occasionally, but serial processing was dominant.
The agents' learning algorithms favored strategies that prioritized single-word attention.

Conclusions:

The findings suggest that serial word processing is an efficient strategy for reading.
The study provides computational evidence supporting the prevalence of serial attention allocation in reading.
Implications for the ongoing debate on human attentional mechanisms during reading are discussed.