2018
DOI: 10.3389/fpsyg.2018.00368
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Temporal Audiovisual Motion Prediction in 2D- vs. 3D-Environments

Abstract: Predicting motion is essential for many everyday life activities, e.g., in road traffic. Previous studies on motion prediction failed to find consistent results, which might be due to the use of very different stimulus material and behavioural tasks. Here, we directly tested the influence of task (detection, extrapolation) and stimulus features (visual vs. audiovisual and three-dimensional vs. non-three-dimensional) on temporal motion prediction in two psychophysical experiments. In both experiments a ball fol… Show more

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Cited by 4 publications
(2 citation statements)
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“…able to estimate the time when a vehicle will reappear, after being occluded by a bus, to plan our next action accordingly, indicating that we can successfully infer the reappearance of a dynamically occluded object (e.g., Coull et al, 2008;Dittrich & Noesselt, 2018). Although such inference mechanisms underpin many actions of our daily life, little is known about their exact neural representations and their informational content.…”
mentioning
confidence: 99%
“…able to estimate the time when a vehicle will reappear, after being occluded by a bus, to plan our next action accordingly, indicating that we can successfully infer the reappearance of a dynamically occluded object (e.g., Coull et al, 2008;Dittrich & Noesselt, 2018). Although such inference mechanisms underpin many actions of our daily life, little is known about their exact neural representations and their informational content.…”
mentioning
confidence: 99%
“…For instance, when driving or crossing a street, we are able to estimate the time when a vehicle will reappear, after being occluded by a bus, to plan our next action accordingly, indicating that we can successfully infer the reappearance of a dynamically occluded object (e.g. Coull et al, 2008, Dittrich & Noesselt, 2018). Although such inference mechanisms underpin many actions of our daily life, little is known about their exact neural representations and their informational content.…”
Section: Introductionmentioning
confidence: 99%