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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Modeling behavior dynamics using computational psychometrics within virtual worlds
|
|---|---|
| Published in |
Frontiers in Psychology, November 2015
|
| DOI | 10.3389/fpsyg.2015.01725 |
| Pubmed ID | |
| Authors |
Pietro Cipresso |
| Abstract |
In case of fire in a building, how will people behave in the crowd? The behavior of each individual affects the behavior of others and, conversely, each one behaves considering the crowd as a whole and the individual others. In this article, I propose a three-step method to explore a brand new way to study behavior dynamics. The first step relies on the creation of specific situations with standard techniques (such as mental imagery, text, video, and audio) and an advanced technique [Virtual Reality (VR)] to manipulate experimental settings. The second step concerns the measurement of behavior in one, two, or many individuals focusing on parameters extractions to provide information about the behavior dynamics. Finally, the third step, which uses the parameters collected and measured in the previous two steps in order to simulate possible scenarios to forecast through computational models, understand, and explain behavior dynamics at the social level. An experimental study was also included to demonstrate the three-step method and a possible scenario. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 83 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 1% |
| Spain | 1 | 1% |
| Italy | 1 | 1% |
| Unknown | 80 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 15 | 18% |
| Researcher | 9 | 11% |
| Student > Master | 9 | 11% |
| Other | 8 | 10% |
| Student > Bachelor | 6 | 7% |
| Other | 19 | 23% |
| Unknown | 17 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 20 | 24% |
| Computer Science | 11 | 13% |
| Neuroscience | 5 | 6% |
| Engineering | 5 | 6% |
| Design | 3 | 4% |
| Other | 17 | 20% |
| Unknown | 22 | 27% |
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 06 November 2015.
All research outputs
#23,445,860
of 26,122,087 outputs
Outputs from Frontiers in Psychology
#28,001
of 35,003 outputs
Outputs of similar age
#256,036
of 298,627 outputs
Outputs of similar age from Frontiers in Psychology
#449
of 497 outputs
Altmetric has tracked 26,122,087 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 35,003 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.6. 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 298,627 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 497 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.