↓ Skip to main content

Decision landscapes: visualizing mouse-tracking data

Overview of attention for article published in Royal Society Open Science, November 2017
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
10 tweeters

Citations

dimensions_citation
11 Dimensions

Readers on

mendeley
56 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Decision landscapes: visualizing mouse-tracking data
Published in
Royal Society Open Science, November 2017
DOI 10.1098/rsos.170482
Pubmed ID
Authors

A. Zgonnikov, A. Aleni, P. T. Piiroinen, D. O'Hora, M. di Bernardo

Abstract

Computerized paradigms have enabled gathering rich data on human behaviour, including information on motor execution of a decision, e.g. by tracking mouse cursor trajectories. These trajectories can reveal novel information about ongoing decision processes. As the number and complexity of mouse-tracking studies increase, more sophisticated methods are needed to analyse the decision trajectories. Here, we present a new computational approach to generating decision landscape visualizations based on mouse-tracking data. A decision landscape is an analogue of an energy potential field mathematically derived from the velocity of mouse movement during a decision. Visualized as a three-dimensional surface, it provides a comprehensive overview of decision dynamics. Employing the dynamical systems theory framework, we develop a new method for generating decision landscapes based on arbitrary number of trajectories. This approach not only generates three-dimensional illustration of decision landscapes, but also describes mouse trajectories by a number of interpretable parameters. These parameters characterize dynamics of decisions in more detail compared with conventional measures, and can be compared across experimental conditions, and even across individuals. The decision landscape visualization approach is a novel tool for analysing mouse trajectories during decision execution, which can provide new insights into individual differences in the dynamics of decision making.

Twitter Demographics

The data shown below were collected from the profiles of 10 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 56 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 32%
Researcher 8 14%
Student > Master 7 13%
Student > Bachelor 5 9%
Student > Doctoral Student 3 5%
Other 7 13%
Unknown 8 14%
Readers by discipline Count As %
Psychology 19 34%
Neuroscience 7 13%
Agricultural and Biological Sciences 5 9%
Engineering 3 5%
Environmental Science 2 4%
Other 8 14%
Unknown 12 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 17 April 2019.
All research outputs
#4,090,623
of 17,024,042 outputs
Outputs from Royal Society Open Science
#1,592
of 3,096 outputs
Outputs of similar age
#88,982
of 329,090 outputs
Outputs of similar age from Royal Society Open Science
#82
of 142 outputs
Altmetric has tracked 17,024,042 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,096 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 47.3. This one is in the 48th percentile – i.e., 48% 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 329,090 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 142 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.