| Popis: |
It is well-established that pre-cues, including those observed in an implicit manner, can affect motor skills and reaction times. However, little research currently exists on how pre-cues influence complex motor skills such as driving a car at high speed. This pilot study investigates the effect of implicit pre-cues on collision avoidance under a repeat trial experiment design using a car driving simulator. Seventeen par- ticipants (aged 23.8 ± 4.2 years) were included in this investigation, which consisted of four different one-kilometre driving scenarios. This investigation considers two of the four scenarios. Two scenarios had the stimulus of a child crossing the road, however only one of these scenarios had an implicit pre-cue appear before the stimulus. The remaining two scenarios had no stimulus or pre-cue and were included to reduce any learning effect by participants. The proportion of participants who had a collision differed significantly between scenarios with and without a pre-cue. The primary effect size of the pre-cue is modelled using a logis- tic regression and distributions for point estimators are obtained from bootstrapping results. A power analysis exploring different primary effect sizes is performed to inform sample size considerations for repeat studies. Implications for motor control, such as experiment design and statistical modelling methods, are discussed to inform future large scale trials. References J. A. Barela, A. A. Rocha, A. R. Novak, J. Fransen, and G. A. Figueiredo. Age differences in the use of implicit visual cues in a response time task. Braz. J. Motor Behav. 13.2 (2019), pp. 86–93. doi: 10.20338/bjmb.v13i2.139 J. Cohen. Statistical power analysis for the behavioral sciences. Routledge, 1988. doi: 10.4324/9780203771587 U. Eversheim and O. Bock. The role of precues in the preparation of motor responses in humans. J. Mot. Behav. 34.3 (2002), pp. 271–276. doi: 10.1080/00222890209601945 D. G. Jenkins and P. F. Quintana-Ascencio. A solution to minimum sample size for regressions. PLOS One 15.2 (2020), e0229345. doi: 10.1371/journal.pone.0229345 J. Jiang. Linear and generalized linear mixed models and their applications. Springer Series in Statistics. Springer, 2007. doi: 10.1007/978-0-387-47946-0 C. Kistin and M. Silverstein. Pilot studies: A critical but potentially misused component of interventional research. JAMA 314.15 (2015), pp. 1561–1562. doi: 10.1001/jama.2015.10962 H. C. Kraemer, J. Mintz, A. Noda, J. Tinklenberg, and J. A. Yesavage. Caution regarding the use of pilot studies to guide power calculations for study proposals. Arch. Gen. Psych. 63.5 (2006), pp. 484–489. doi: 10.1001/archpsyc.63.5.484 J. A. Nelder and R. W. M. Wedderburn. Generalized linear models. J. Roy. Stat. Soc. 135.3 (1972), pp. 370–384. doi: 10.2307/2344614 R. Stine. An introduction to bootstrap methods: Examples and ideas. Soc. Meth. Res. 18.2–3 (1989), pp. 243–291. doi: 10.1177/0049124189018002003 |
