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Article details
Title
Gesturing during mental problem solving reduces eye movements, especially for individuals with lower visual working memory capacity
Published in
Cognitive Processing, March 2016
DOI 10.1007/s10339-016-0757-6
Pubmed ID
Authors
Abstract

Non-communicative hand gestures have been found to benefit problem-solving performance. These gestures seem to compensate for limited internal cognitive capacities, such as visual working memory capacity. Yet, it is not clear how gestures might perform this cognitive function. One hypothesis is that gesturing is a means to spatially index mental simulations, thereby reducing the need for visually projecting the mental simulation onto the visual presentation of the task. If that hypothesis is correct, less eye movements should be made when participants gesture during problem solving than when they do not gesture. We therefore used mobile eye tracking to investigate the effect of co-thought gesturing and visual working memory capacity on eye movements during mental solving of the Tower of Hanoi problem. Results revealed that gesturing indeed reduced the number of eye movements (lower saccade counts), especially for participants with a relatively lower visual working memory capacity. Subsequent problem-solving performance was not affected by having (not) gestured during the mental solving phase. The current findings suggest that our understanding of gestures in problem solving could be improved by taking into account eye movements during gesturing.

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X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 80 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Geographical breakdown
Country Count As %
United States 1 1%
Netherlands 1 1%
Brazil 1 1%
Unknown 77 96%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 18 23%
Researcher 10 13%
Student > Master 9 11%
Student > Doctoral Student 8 10%
Student > Bachelor 8 10%
Other 13 16%
Unknown 14 18%
Readers by discipline
Readers by discipline Count As %
Psychology 27 34%
Engineering 5 6%
Social Sciences 5 6%
Business, Management and Accounting 4 5%
Computer Science 4 5%
Other 19 24%
Unknown 16 20%
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 23 March 2016.
All research outputs
#20,315,221
of 22,856,968 outputs
Outputs from Cognitive Processing
#293
of 337 outputs
Outputs of similar age
#253,769
of 299,781 outputs
Outputs of similar age from Cognitive Processing
#8
of 9 outputs
Altmetric has tracked 22,856,968 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 337 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.3. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 299,781 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one.