| Title |
PredPsych: A toolbox for predictive machine learning-based approach in experimental psychology research
|
|---|---|
| Published in |
Behavior Research Methods, December 2017
|
| DOI | 10.3758/s13428-017-0987-2 |
| Pubmed ID | |
| Authors |
Atesh Koul, Cristina Becchio, Andrea Cavallo |
| Abstract |
Recent years have seen an increased interest in machine learning-based predictive methods for analyzing quantitative behavioral data in experimental psychology. While these methods can achieve relatively greater sensitivity compared to conventional univariate techniques, they still lack an established and accessible implementation. The aim of current work was to build an open-source R toolbox - "PredPsych" - that could make these methods readily available to all psychologists. PredPsych is a user-friendly, R toolbox based on machine-learning predictive algorithms. In this paper, we present the framework of PredPsych via the analysis of a recently published multiple-subject motion capture dataset. In addition, we discuss examples of possible research questions that can be addressed with the machine-learning algorithms implemented in PredPsych and cannot be easily addressed with univariate statistical analysis. We anticipate that PredPsych will be of use to researchers with limited programming experience not only in the field of psychology, but also in that of clinical neuroscience, enabling computational assessment of putative bio-behavioral markers for both prognosis and diagnosis. |
X Demographics
The data shown below were collected from the profiles of 33 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 Kingdom | 8 | 24% |
| Italy | 5 | 15% |
| France | 3 | 9% |
| Netherlands | 3 | 9% |
| Switzerland | 2 | 6% |
| Germany | 2 | 6% |
| Israel | 1 | 3% |
| United States | 1 | 3% |
| Denmark | 1 | 3% |
| Other | 0 | 0% |
| Unknown | 7 | 21% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 18 | 55% |
| Scientists | 11 | 33% |
| Practitioners (doctors, other healthcare professionals) | 3 | 9% |
| Science communicators (journalists, bloggers, editors) | 1 | 3% |
Mendeley readers
The data shown below were compiled from readership statistics for 139 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 1 | <1% |
| Unknown | 138 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 29 | 21% |
| Researcher | 25 | 18% |
| Student > Master | 19 | 14% |
| Student > Bachelor | 11 | 8% |
| Student > Doctoral Student | 9 | 6% |
| Other | 26 | 19% |
| Unknown | 20 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 56 | 40% |
| Computer Science | 13 | 9% |
| Neuroscience | 13 | 9% |
| Social Sciences | 4 | 3% |
| Medicine and Dentistry | 3 | 2% |
| Other | 15 | 11% |
| Unknown | 35 | 25% |
Attention Score in Context
This research output has an Altmetric Attention Score of 18. 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 18 January 2022.
All research outputs
#2,116,614
of 25,653,515 outputs
Outputs from Behavior Research Methods
#211
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Outputs of similar age
#45,769
of 445,595 outputs
Outputs of similar age from Behavior Research Methods
#4
of 31 outputs
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