Maze Made Easy: Better and easier measurement of incremental processing difficulty
Author(s)
Boyce, Veronica; Futrell, Richard; Levy, Roger P
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© 2019 Elsevier Inc. Behavioral measures of incremental language comprehension difficulty form a crucial part of the empirical basis of psycholinguistics. The two most common methods for obtaining these measures have significant limitations: eye tracking studies are resource-intensive, and self-paced reading can yield noisy data with poor localization. These limitations are even more severe for web-based crowdsourcing studies, where eye tracking is infeasible and self-paced reading is vulnerable to inattentive participants. Here we make a case for broader adoption of the Maze task, involving sequential forced choice between each successive word in a sentence and a contextually inappropriate distractor. We leverage natural language processing technology to automate the most researcher-laborious part of Maze – generating distractor materials – and show that the resulting A(uto)-Maze method has dramatically superior statistical power and localization for well-established syntactic ambiguity resolution phenomena. We make our code freely available online for widespread adoption of A-maze by the psycholinguistics community.
Date issued
2020Department
Massachusetts Institute of Technology. Department of Brain and Cognitive SciencesJournal
Journal of Memory and Language
Publisher
Elsevier BV
Citation
Boyce, Veronica, Futrell, Richard and Levy, Roger P. 2020. "Maze Made Easy: Better and easier measurement of incremental processing difficulty." Journal of Memory and Language, 111.
Version: Original manuscript