Here, BHM is used to analyse the speed of participants’ binary goal side selections in a soccer related penalty shot task. Participants viewed realistic images of a soccer goal and goalkeeper, and chose which side of the goal to best score. Analogous to the visual line bisection task, we (Pereira & Patching, 2021, Perceptual and Motor Skills, 128, 2279-2303)...
Traditionally, psychophysical data is modelled by fitting individual curves to each participant’s data and then by statistical analysis of the extracted parameters. A now viable alternative to this traditional two-stage approach is Bayesian hierarchical modelling (BHM). BHM allows for modelling all of the data and all of the parameters in one-step rather than two.
Here, BHM is used to analyse the speed of participants’ binary goal side selections in a soccer related penalty shot task. Participants viewed realistic images of a soccer goal and goalkeeper, and chose which side of the goal to best score. Analogous to the visual line bisection task, we (Pereira & Patching, 2021, Perceptual and Motor Skills, 128, 2279-2303) systematically repositioned the goalkeeper from left to right along the goal line and, to simulate changes in the viewing position of the kicker, lateral position of the goalmouth in each image. Scaling response times in terms of signed response speed shows a close linear correspondence with log odds ratios (logit) of left goal side selection. Overall, participants tended to choose the left- over right-goal side, but both the speed and direction of this tendency depended on the goalkeeper’s position and lateral position of the goalmouth relative to participants’ body midline. Current analysis of signed response speed shows a similar pattern of results as binary response probability complementing our earlier analysis of binary goal side selection in the present penalty shot task.
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@misc{b4ef9323-c4ca-4bf5-8f3a-c0d28153a3e8,
abstract = {{<div>Traditionally, psychophysical data is modelled by fitting individual curves to each participant’s data and then by statistical analysis of the extracted parameters. A now viable alternative to this traditional two-stage approach is Bayesian hierarchical modelling (BHM). BHM allows for modelling all of the data and all of the parameters in one-step rather than two. </div><div>Here, BHM is used to analyse the speed of participants’ binary goal side selections in a soccer related penalty shot task. Participants viewed realistic images of a soccer goal and goalkeeper, and chose which side of the goal to best score. Analogous to the visual line bisection task, we (Pereira & Patching, 2021, Perceptual and Motor Skills, 128, 2279-2303) systematically repositioned the goalkeeper from left to right along the goal line and, to simulate changes in the viewing position of the kicker, lateral position of the goalmouth in each image. Scaling response times in terms of signed response speed shows a close linear correspondence with log odds ratios (logit) of left goal side selection. Overall, participants tended to choose the left- over right-goal side, but both the speed and direction of this tendency depended on the goalkeeper’s position and lateral position of the goalmouth relative to participants’ body midline. Current analysis of signed response speed shows a similar pattern of results as binary response probability complementing our earlier analysis of binary goal side selection in the present penalty shot task.</div>}},
author = {{Patching, Geoff}},
language = {{eng}},
month = {{04}},
title = {{Applying Bayesian hierarchical modelling to assess the speed of goal side selection in a soccer related penalty shot task}},
year = {{2023}},
}
