Coordinating sentence composition with error correction: A multilevel analysis
Keywords:cognitive effort, error analysis, multilevel analysis, technology of writing, text produced so far, working memory
AbstractError analysis involves detecting and correcting discrepancies between the 'text produced so far' (TPSF) and the writer's mental representation of what the text should be. While many factors determine the choice of strategy, cognitive effort is a major contributor to this choice. This research shows how cognitive effort during error analysis affects strategy choice and success as measured by a series of online text production measures. We hypothesize that error correction with speech recognition software differs from error correction with keyboard for two reasons. Speech produces auditory commands and, consequently, different error types. The study reported on here measured the effects of (1) mode of presentation (auditory or visual-tactile), (2) error span, whether the error spans more or less than two characters, and (3) lexicality, whether the text error comprises an existing word. A multilevel analysis was conducted to take into account the hierarchical nature of these data. For each variable (interference reaction time, preparation time, production time, immediacy of error correction, and accuracy of error correction), multilevel regression models are presented. As such, we take into account possible disturbing person characteristics while testing the effect of the different conditions and error types at the sentence level. The results show that writers delay error correction more often when the TPSF is read out aloud first. The auditory property of speech seems to free resources for the primary task of writing, i.e. text production. Moreover, the results show that large errors in the TPSF require more cognitive effort, and are solved with a higher accuracy than small errors. The latter also holds for the correction of small errors that result in non-existing words.
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Copyright (c) 2011 Mariëlle Leijten, Sven De Maeyer, Luuk Van Waes
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 Unported License.