Designing software to estimate the relative interference between multiple tasks and determine its validity

Mousavi Sadati, K

doi:10.29252/jsmt.17.18.45

Volume 17, Issue 18 (12-2019) RSMT 2019, 17(18): 45-55 | Back to browse issues page

‎ 10.29252/jsmt.17.18.45

‎ 20.1001.1.22520708.1398.17.18.5.7

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Mousavi Sadati K. Designing software to estimate the relative interference between multiple tasks and determine its validity. RSMT 2019; 17 (18) :45-55
URL: http://jsmt.khu.ac.ir/article-1-400-en.html

Designing software to estimate the relative interference between multiple tasks and determine its validity

K Mousavi Sadati

, drmousavisadati@gmail.com

Abstract: (3594 Views)

The most commonly used empirical approach to address issues of attention constraint is evaluate the degree of interference in dual or multiple tasks. The purpose of this study was designing software to estimate the relative interference between multiple tasks and determine its validity in performance prediction of simultaneous multiple tasks. in order to assess the validity of software, 46 students with a driving certificate from East Tehran branch of Islamic Azad University were selected by available sampling method, the response time to the visual and auditory stimulus of participants during driving in different conditions was measured by using software and hardware installed on their cars, and then relative interference in the above mentioned conditions was estimated by using relative interference software. The result of pearson correlation analysis showed that there is a high, meaningful and direct correlation between the amount of estimated relative interference with the software and measured participants' response time in different driving conditions (r=0.95, p≤0.01). Therefore, the designed software can be used to predict the amount of interference between two or more simultaneous tasks.

Keywords: Design, Software, Relative Interference, Multiple Tasks, Validity.

Full-Text [PDF 1250 kb] (1098 Downloads)

Type of Study: Research |
Received: 2020/02/5 | Accepted: 2020/02/5 | Published: 2020/02/5

References

1. 1. McGarry, T., O'Donoghue, P.,de Eira Sampaio, A.J. (2013). Handbook of Sports Performance Analysis, Publisher: New York : Routledge. [DOI:10.4324/9780203806913]

2. 2. Koedijker, J.M., Poolton, J.M., Maxwell, J.P., Oudejans, R.R., Beek, P.J., Masters, R.S. (2011). Attention and time constraints in perceptual-motor learning and performance: Instruction, analogy, and skill level. Consciousness and Cognition. 20(2):245-56. [DOI:10.1016/j.concog.2010.08.002]

3. 3. Shaw, M.L, Mulligan, R.M., Stone, L.D. (1983). Two-state versus continuous-state stimulus representations: A test based on attentional constraints. Perception & Psychophysics. 33(4):338-54. [DOI:10.3758/BF03205881]

4. 4. Edwards, W. (2010). Learning and Motor Control from Theory to Practice - Volume II, Mousavi Sadati, S.K. (2014). Publisher: Hatmi. First Edition. 165-7. (Persian)

5. 5. Driver, J. (2001). A selective review of selective attention research from the past century. British Journal of Psychology. 92:53-78. [DOI:10.1348/000712601162103]

6. 6. Welford, A.T. (1952). The 'psychological refractory period' and the timing of high‐speed performance-a review and a theory. British Journal of Psychology. General Section. 43(1):2-19. [DOI:10.1111/j.2044-8295.1952.tb00322.x]

7. 7. Norman, D.A. (1968). Toward a theory of memory and attention. Psychological Review. 75(6):522-36. [DOI:10.1037/h0026699]

8. 8. Schmidt, R., Lee, T. (2014). Motor learning and performance: From principles to applications. Human Kinetics. 205.

9. 9. Magill, R.A. (2016). Motor learning: Concepts and aplications. Vaez Mousavi, M.K., Shojaie, M. Publisher: Bamdad Ketab. 196-9. (Persian)

10. 10. Kahneman, D. (1973). Attention and effort: Prentice-Hall Inc., Englewood Cliffs, New Jersey. 145-8.

11. 11. Navon, D., Gopher, D. (1979). On the economy of the human-processing system. Psychological Review. 86(3):214-55. [DOI:10.1037/0033-295X.86.3.214]

12. 12. Wickens, C.D., Kessel, C. (1980). Processing resource demands of failure detection in dynamic systems. Journal of Experimental Psychology: Human Perception and Performance. 6(3): 564-77. [DOI:10.1037/0096-1523.6.3.564]

13. 13. Pashler H, Johnston JC, Ruthruff E. (2001). Attention and performance. Annual review of psychology. 52(1):629-51. [DOI:10.1146/annurev.psych.52.1.629]

14. 14. Wickens, C.D. (2008). Multiple resources and mental workload. Human Factors. 50(3):449-55. [DOI:10.1518/001872008X288394]

15. 15. Wickens, C.D. (2002). Multiple resources and performance prediction. Theoretical Issues in Ergonomics Science. 3(2):159-77. [DOI:10.1080/14639220210123806]

16. 16. van Engelen, D. (2011). Attention Drivers: RWTH Aachen University; 11-22.