Computational Psychiatry for Computers
iScience, ISSN: 2589-0042, Vol: 23, Issue: 12, Page: 101772
2020
- 12Citations
- 49Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations12
- Citation Indexes12
- 12
- CrossRef3
- Captures49
- Readers49
- 49
Review Description
Computational psychiatry is a nascent field that attempts to use multi-level analyses of the underlying computational problems that we face in navigating a complex, uncertain and changing world to illuminate mental dysfunction and disease. Two particular foci of the field are the costs and benefits of environmental adaptivity and the danger and necessity of heuristics. Here, we examine the extent to which these foci and others can be used to study the actual and potential flaws of the artificial computational devices that we are increasingly inventing and empowering to navigate this very same environment on our behalf.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S258900422030969X; http://dx.doi.org/10.1016/j.isci.2020.101772; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85097041299&origin=inward; http://www.ncbi.nlm.nih.gov/pubmed/33294781; https://linkinghub.elsevier.com/retrieve/pii/S258900422030969X; https://dx.doi.org/10.1016/j.isci.2020.101772
Elsevier BV
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