Human attention during goal-directed reading comprehension relies on task optimization

Author:

Zou Jiajie12ORCID,Zhang Yuran1,Li Jialu3,Tian Xing3ORCID,Ding Nai12ORCID

Affiliation:

1. Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Sciences, Zhejiang University

2. Nanhu Brain-computer Interface Institute

3. Division of Arts and Sciences, New York University Shanghai

Abstract

The computational principles underlying attention allocation in complex goal-directed tasks remain elusive. Goal-directed reading, that is, reading a passage to answer a question in mind, is a common real-world task that strongly engages attention. Here, we investigate what computational models can explain attention distribution in this complex task. We show that the reading time on each word is predicted by the attention weights in transformer-based deep neural networks (DNNs) optimized to perform the same reading task. Eye tracking further reveals that readers separately attend to basic text features and question-relevant information during first-pass reading and rereading, respectively. Similarly, text features and question relevance separately modulate attention weights in shallow and deep DNN layers. Furthermore, when readers scan a passage without a question in mind, their reading time is predicted by DNNs optimized for a word prediction task. Therefore, we offer a computational account of how task optimization modulates attention distribution during real-world reading.

Funder

STI2030-Major Project

National Natural Science Foundation of China

Major Scientific Project of Zhejiang Laboratory

Fundamental Research Funds for the Central Universities

Publisher

eLife Sciences Publications, Ltd

Subject

General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine,General Neuroscience

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