Abstract
Experimental psychology and neuroscience reveal that decision-behavior plays a dominant role in human-selective-attention when it comes to reading, object and scene detection. Difficulties in reading are easily reflected by eye-movement patterns. Hence, modelling eye-gaze behaviour for normal readers and people with reading impairments can greatly help in contrasting reading strategies used, which can in turn help in early identification and diagnosis of impairments such as dyslexia. This paper introduces a novel method of formulating a reinforcement learning model that is explainable, and can obtain the sequence of gaze targets based on recorded observations of dyslexic and non-dyslexic children. Results reveal that despite being a less sophisticated model, it is able to obtain the optimal reading policy of the ideal reader, from a set of good and poor readers with the help of a strong reward system and Q-Learning agent.
Published Version
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