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Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
Published on: June 25, 2019
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Participants shift response deadlines based on list difficulty during reading-aloud megastudies
Michael J Cortese1, Maya M Khanna2, Robert Kopp3
1University of Nebraska, Omaha, Nebraska, USA. mcortese@unomaha.edu.
Memory & Cognition
|February 18, 2017
Summary
Blocking words by difficulty in reading aloud studies increases reaction time (RT) variability and predictor influence. This suggests response deadlines affect megastudies, potentially underestimating predictor effects in randomized designs.
Area of Science:
- Cognitive Psychology
- Psycholinguistics
- Computational Linguistics
Background:
- The list homogeneity effect describes how item presentation influences cognitive task performance.
- Previous research suggests item blocking may impact reading aloud metrics.
- Megastudies, large-scale data collection efforts, are increasingly used in cognitive science.
Purpose of the Study:
- To investigate the list homogeneity effect in reading aloud using a megastudy paradigm.
- To determine if blocking words by difficulty influences the predictability of reaction times (RTs) by standard variables.
- To assess the impact of item presentation order on computational models of word processing.
Main Methods:
- A megastudy paradigm comparing random versus blocked (by difficulty) word presentation in reading aloud.
- Two conditions with 25 blocks of 100 trials each.
- Regression analyses using sublexical, lexical, and semantic variables to predict RTs.
Main Results:
- Reaction times (RTs) exhibited larger standard deviations and ranges in the blocked condition compared to the random condition.
- A response bias was observed at the extremes of item difficulty.
- Predictor variables accounted for more RT variance in the blocked condition (2.8% more) than in the random condition.
Conclusions:
- Response deadlines are relevant in megastudies of reading aloud, influencing the observed effects of predictors.
- Randomized item presentation in megastudies may lead to an underestimation of predictor influence on RTs.
- Computational models, like CDP++, may show greater predictive power when data is blocked by difficulty, suggesting the need to account for response criterion shifts.
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