Attribute Construction of Individual Differences in the “Speed - Accuracy Compromise” Task Using the Periodograms
The research is devoted to finding the attributes that would allow detailing the factors breaking Fitts's law. Fitts's law ties together the distance to a target area with the accuracy and movement time. Frequency analysis of two time rows that depict, respectively, the accuracy and time needed to solve the task, is used. To analyze the results we used the periodogram method. It allows us to evaluate how the period of time, given to a person under test, appears in his solutions of the task. The features of periodicity suppression are interpreted as specific characteristics of people under test. The research deals with the data received from 1023 test takers, each of those solved 120 tasks. It is not only the fact of interrupting the given periodicity revealed in the results, but also a set of typical cases of such interruption is demonstrated from the points of view of two traditionally used variables - accuracy and time - and a new, third, variable, that is the accurate and fast solution mindset. This mindset is given as a special kind of instruction during the test. As a result, the authors revealed that Fitts's law manifestation on the level of periods that subordinate distance changes is distinctive for the majority of test takers. However, the character of periodicity changes easily. For example, it was found out that about a half of examinees demonstrate the periodicity consisting of 30 tasks, this periodicity appears stable both from the points of speed and accuracy, and on the level of dependent variables. At the same time, the periodicity of 10 tasks depends a lot on the speed. A small amount of results characterized by the local absence of periodicity was also found. The authors remark successful application of the attribute construction method, it allows us to describe in detail the character of individual differences manifestation, being short and compact in giving new descriptive attributes at the same time. The ways of improving and perfecting the methods of interactive spatial choice task solving are planned in the long view.
Keywords
Fitts's law,
task,
time,
accuracy,
periodogram,
frequency analysis,
attribute construction,
individual differencesAuthors
Balanev Dmitry Yu. | Tomsk State University | balanevd@gmail.com |
Kulikov Ivan A. | Tomsk State University | kulikov.ivan.tsu@gmail.com |
Всего: 2
References
Baek J., Park H.J. Bayesian adaptive model estimation to solve the speed accuracy tradeoff problem in psychophysical experiments // Sci Rep. 2021. Vol. 11. Art. 18264. DOI: 10.1038/s41598-021-97772-9
Berkay D., Eser H.Y., Sack A.T., Qakmak Y.O., Balci F. The modulatory role of pre-SMA in speed-accuracy tradeoff: A bi-directional TMS study // Neuropsychologia. 2018. Vol. 109. Р. 255-261. DOI: 10.1016/j.neuropsychologia.2017.12.031
Ducatez S., Audet J.N., Lefebvre L. Speed-accuracy trade-off, detour reaching and response to PHA in Carib grackles // Anim Cogn. 2019. Vol. 22. Р. 625-633. DOI: 10.1007/s10071-019-01258-1
Ratcliff R., Kang I. Qualitative speed-accuracy tradeoff effects can be explained by a diffusion / fast-guess mixture model // Sci Rep. 2021. Vol. 11. Art. 15169. DOI: 10.1038/s41598-021-94451-7
Liesefeld H.R., Janczyk M.Combining speed and accuracy to control for speed-accuracy trade-offs(?) // Behav Res. 2019. Vol. 51. Р. 40-60. DOI: 10.3758/s13428-018-1076-x
Reid Turner C., Fuggetta A., Lavazza L., Wolf A.L. A conceptual basis for feature engineering // Journal of Systems and Software. 1999. Vol. 49 (1). Р. 3-15. DOI: 10.1016/s0164-1212(99)0006
Nguyen K.T.P. Feature Engineering and Health Indicator Construction for Fault Detection and Diagnostic // Control Charts and Machine Learning for Anomaly Detection in Manufacturing / K.P. Tran (ed.). Springer, Cham., 2022. (Springer Series in Reliability Engineering). DOI: 10.1007/978-3-030-83819-5_10
Garla V.N., Brandt C. Ontology-guided feature engineering for clinical text classification // Journal of Biomedical Informatics. 2012. Vol. 45 (5). Р. 992-998. DOI: 10.1016/j.jbi.2012.04.010
Das S., Subba Rao S., Yang J. Spectral methods for small sample time series: A complete periodogram approach // Journal of Time Series Analysis. 2021. Vol. 42 (5-6). P. 597621. DOI: 10.1111/jtsa.12584
Caiado J., Crato N., Poncela P. A fragmented-periodogram approach for clustering big data time series // Adv Data Anal Classif. 2020. Vol. 14. Р. 117-146. DOI: 10.1007/s11634-019-00365-8
Fajardo F.A., Reisen V.A., Levy-Leduc C., Taqqu M.S. M-periodogram for the analysis of long-range-dependent time series // Statistics. 2018. Vol. 52 (3). Р. 665-683. DOI: 10.1080/02331888.2018.14277
Баланёв Д.Ю. Возможности визуализации результатов экспериментального исследования компромисса скорость-точность // Экспериментальная психология в России: традиции и перспективы / под ред. В.А. Барабанщикова. М. : Ин-т психологии РАН, 2010. С. 80-86.
Баланев Д.Ю., Бредун Е.В. Компромисс скорость-точность как предмет психологического анализа // Вестник Кемеровского государственного университета. 2021. Т. 23, № 1. С. 123-132.
Fitts P.M. The information capacity of the human motor system in controlling the amplitude of movement // Journal of Experimental Psychology. 1954. Vol. 47 (6). Р. 381-391. DOI: 10.1037/h0055392
Muntashir-Al-Arefin, Md. Ayub Al. sleekts: 4253H, Twice Smoothing. R package version 1.0.2. 2015. URL: https://CRAN.R-project.org/package=sleekts
The R Project for Statistical Computing. 2021. URL: https://www.R-project.org/
Bischl B., Lang M., Bossek J., Horn D., Richter J., Surmann D. BBmisc: Miscellaneous Helper Functions for B. Bischl. R package version 1.11. 2017. URL: https://CRAN.R-project.org/package=BBmisc
Borchers H.W. pracma: Practical Numerical Math Functions. R package version 2.3.3. 2021. URL: https://CRAN.R-project.org/package=pracma
Wickham H. ggplot2: Elegant Graphics for Data Analysis. New York : Springer-Verlag, 2016. 268 р.
Wilke C.O. cowplot: Streamlined Plot Theme and Plot Annotations for 'ggplot2'. R package version 1.1.1. 2020. URL: https://CRAN.R-project.org/package=cowplot
Бернштейн Н.А. Физиология движений и активность / под ред. О.Г. Газенко; АН СССР. М. : Наука, 1990. 494 с.