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X Demographics
Mendeley readers
Attention Score in Context
| Title |
On the Origins of Suboptimality in Human Probabilistic Inference
|
|---|---|
| Published in |
PLoS Computational Biology, June 2014
|
| DOI | 10.1371/journal.pcbi.1003661 |
| Pubmed ID | |
| Authors |
Luigi Acerbi, Sethu Vijayakumar, Daniel M. Wolpert |
X Demographics
The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 43% |
| Turkey | 1 | 14% |
| Finland | 1 | 14% |
| Unknown | 2 | 29% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 4 | 57% |
| Members of the public | 2 | 29% |
| Science communicators (journalists, bloggers, editors) | 1 | 14% |
Mendeley readers
The data shown below were compiled from readership statistics for 203 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 7 | 3% |
| Switzerland | 2 | <1% |
| Germany | 2 | <1% |
| Japan | 2 | <1% |
| United Kingdom | 2 | <1% |
| Netherlands | 1 | <1% |
| France | 1 | <1% |
| Austria | 1 | <1% |
| Iceland | 1 | <1% |
| Other | 0 | 0% |
| Unknown | 184 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 60 | 30% |
| Researcher | 41 | 20% |
| Student > Master | 21 | 10% |
| Student > Bachelor | 18 | 9% |
| Professor > Associate Professor | 12 | 6% |
| Other | 33 | 16% |
| Unknown | 18 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 53 | 26% |
| Neuroscience | 29 | 14% |
| Agricultural and Biological Sciences | 27 | 13% |
| Computer Science | 21 | 10% |
| Engineering | 21 | 10% |
| Other | 22 | 11% |
| Unknown | 30 | 15% |
Attention Score in Context
This research output has an Altmetric Attention Score of 12. 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 21 April 2021.
All research outputs
#3,079,389
of 26,017,215 outputs
Outputs from PLoS Computational Biology
#2,683
of 9,035 outputs
Outputs of similar age
#29,457
of 246,695 outputs
Outputs of similar age from PLoS Computational Biology
#34
of 151 outputs
Altmetric has tracked 26,017,215 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,035 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has gotten more attention than average, scoring higher than 70% of its peers.
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 246,695 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 151 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.