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Setting things straight: A comparison of measures of saccade trajectory deviation

Overview of attention for article published in Behavior Research Methods, January 2017
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Title
Setting things straight: A comparison of measures of saccade trajectory deviation
Published in
Behavior Research Methods, January 2017
DOI 10.3758/s13428-016-0846-6
Pubmed ID
Authors

Luke Tudge, Eugene McSorley, Stephan A. Brandt, Torsten Schubert

Abstract

In eye movements, saccade trajectory deviation has often been used as a physiological operationalization of visual attention, distraction, or the visual system's prioritization of different sources of information. However, there are many ways to measure saccade trajectories and to quantify their deviation. This may lead to noncomparable results and poses the problem of choosing a method that will maximize statistical power. Using data from existing studies and from our own experiments, we used principal components analysis to carry out a systematic quantification of the relationships among eight different measures of saccade trajectory deviation and their power to detect the effects of experimental manipulations, as measured by standardized effect size. We concluded that (1) the saccade deviation measure is a good default measure of saccade trajectory deviation, because it is somewhat correlated with all other measures and shows relatively high effect sizes for two well-known experimental effects; (2) more generally, measures made relative to the position of the saccade target are more powerful; and (3) measures of deviation based on the early part of the saccade are made more stable when they are based on data from an eyetracker with a high sampling rate. Our recommendations may be of use to future eye movement researchers seeking to optimize the designs of their studies.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 34 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 18%
Researcher 6 18%
Student > Master 4 12%
Other 3 9%
Student > Doctoral Student 3 9%
Other 5 15%
Unknown 7 21%
Readers by discipline Count As %
Psychology 13 38%
Neuroscience 3 9%
Computer Science 2 6%
Engineering 2 6%
Social Sciences 2 6%
Other 4 12%
Unknown 8 24%
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 24 January 2017.
All research outputs
#17,283,763
of 25,371,288 outputs
Outputs from Behavior Research Methods
#1,634
of 2,524 outputs
Outputs of similar age
#268,958
of 423,586 outputs
Outputs of similar age from Behavior Research Methods
#19
of 31 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,524 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.1. This one is in the 25th percentile – i.e., 25% of its peers scored the same or lower than it.
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We're also able to compare this research output to 31 others from the same source and published within six weeks on either side of this one. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.