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Does complexity matter? Meta-analysis of learner performance in artificial grammar tasks

Overview of attention for article published in Frontiers in Psychology, September 2014
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Title
Does complexity matter? Meta-analysis of learner performance in artificial grammar tasks
Published in
Frontiers in Psychology, September 2014
DOI 10.3389/fpsyg.2014.01084
Pubmed ID
Authors

Rachel Schiff, Pesia Katan

Abstract

Complexity has been shown to affect performance on artificial grammar learning (AGL) tasks (categorization of test items as grammatical/ungrammatical according to the implicitly trained grammar rules). However, previously published AGL experiments did not utilize consistent measures to investigate the comprehensive effect of grammar complexity on task performance. The present study focused on computerizing Bollt and Jones's (2000) technique of calculating topological entropy (TE), a quantitative measure of AGL charts' complexity, with the aim of examining associations between grammar systems' TE and learners' AGL task performance. We surveyed the literature and identified 56 previous AGL experiments based on 10 different grammars that met the sampling criteria. Using the automated matrix-lift-action method, we assigned a TE value for each of these 10 previously used AGL systems and examined its correlation with learners' task performance. The meta-regression analysis showed a significant correlation, demonstrating that the complexity effect transcended the different settings and conditions in which the categorization task was performed. The results reinforced the importance of using this new automated tool to uniformly measure grammar systems' complexity when experimenting with and evaluating the findings of AGL studies.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 72 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
Russia 2 3%
Unknown 68 94%

Demographic breakdown

Readers by professional status Count As %
Student > Master 12 17%
Student > Bachelor 12 17%
Student > Ph. D. Student 10 14%
Researcher 6 8%
Professor 5 7%
Other 19 26%
Unknown 8 11%
Readers by discipline Count As %
Psychology 18 25%
Medicine and Dentistry 9 13%
Linguistics 8 11%
Neuroscience 5 7%
Agricultural and Biological Sciences 3 4%
Other 18 25%
Unknown 11 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 July 2018.
All research outputs
#15,306,466
of 22,764,165 outputs
Outputs from Frontiers in Psychology
#18,599
of 29,677 outputs
Outputs of similar age
#146,178
of 252,140 outputs
Outputs of similar age from Frontiers in Psychology
#290
of 367 outputs
Altmetric has tracked 22,764,165 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 29,677 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.5. This one is in the 31st percentile – i.e., 31% of its peers scored the same or lower than it.
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We're also able to compare this research output to 367 others from the same source and published within six weeks on either side of this one. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.