Web: http://arxiv.org/abs/1808.00054

Sept. 19, 2022, 1:15 a.m. | Michael Hahn, Frank Keller

cs.CL updates on arXiv.org arxiv.org

Research on human reading has long documented that reading behavior shows
task-specific effects, but it has been challenging to build general models
predicting what reading behavior humans will show in a given task. We introduce
NEAT, a computational model of the allocation of attention in human reading,
based on the hypothesis that human reading optimizes a tradeoff between economy
of attention and success at a task. Our model is implemented using contemporary
neural network modeling techniques, and makes explicit and …

arxiv attention effects human modeling network neural network reading

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