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A Machine Learning Approach for Detecting Cognitive Interference Based on Eye-Tracking Data

Overview of attention for article published in Frontiers in Human Neuroscience, April 2022
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2 X users

Citations

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5 Dimensions

Readers on

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16 Mendeley
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Title
A Machine Learning Approach for Detecting Cognitive Interference Based on Eye-Tracking Data
Published in
Frontiers in Human Neuroscience, April 2022
DOI 10.3389/fnhum.2022.806330
Pubmed ID
Authors

Antonio Rizzo, Sara Ermini, Dario Zanca, Dario Bernabini, Alessandro Rossi

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users 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 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 2 13%
Student > Master 2 13%
Lecturer 1 6%
Researcher 1 6%
Lecturer > Senior Lecturer 1 6%
Other 1 6%
Unknown 8 50%
Readers by discipline Count As %
Engineering 3 19%
Unspecified 2 13%
Computer Science 2 13%
Psychology 1 6%
Unknown 8 50%
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 01 May 2022.
All research outputs
#19,416,201
of 23,885,338 outputs
Outputs from Frontiers in Human Neuroscience
#6,246
of 7,389 outputs
Outputs of similar age
#319,712
of 429,504 outputs
Outputs of similar age from Frontiers in Human Neuroscience
#138
of 178 outputs
Altmetric has tracked 23,885,338 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,389 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.8. This one is in the 8th percentile – i.e., 8% 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 429,504 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 178 others from the same source and published within six weeks on either side of this one. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.