  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 testable predictions
about how the allocation of attention varies across different tasks. We test
this in an eyetracking study comparing two versions of a reading comprehension
task, finding that our model successfully accounts for reading behavior across
the tasks. Our work thus provides evidence that task effects can be modeled as
optimal adaptation to task demands.

Computation and Language