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The computational principles underlying attention allocation in complexgoal-directed tasks remain elusive. Goal-directed reading, i.e., reading apassage to answer a question in mind, is a common real-world task that stronglyengages attention. Here, we investigate what computational models can explainattention distribution in this complex task. We show that the reading time oneach word is predicted by the attention weights in transformer-based deepneural networks (DNNs) optimized to perform the same reading task. Eye-trackingfurther reveals that readers separately attend to basic text features andquestion-relevant information during first-pass reading and rereading,respectively. Similarly, text features and question relevance separatelymodulate attention weights in shallow and deep DNN layers. Furthermore, whenreaders scan a passage without a question in mind, their reading time ispredicted by DNNs optimized for a word prediction task. Therefore, attentionduring real-world reading can be interpreted as the consequence of taskoptimization.

Human Attention during Goal-directed Reading Comprehension Relies on Task Optimization