Dataset Information

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 followed a trajectory toward the observer and temporarily disappeared behind an occluder. In audiovisual conditions a moving white noise (congruent or non-congruent to visual motion direction) was presented concurrently. In experiment 1 the ball reappeared on a predictable or a non-predictable trajectory and participants detected when the ball reappeared. In experiment 2 the ball did not reappear after occlusion and participants judged when the ball would reach a specified position at two possible distances from the occluder (extrapolation task). Both experiments were conducted in three-dimensional space (using stereoscopic screen and polarised glasses) and also without stereoscopic presentation. Participants benefitted from visually predictable trajectories and concurrent sounds during detection. Additionally, visual facilitation was more pronounced for non-3D stimulation during detection task. In contrast, for a more complex extrapolation task group mean results indicated that auditory information impaired motion prediction. However, a post hoc cross-validation procedure (split-half) revealed that participants varied in their ability to use sounds during motion extrapolation. Most participants selectively profited from either near or far extrapolation distances but were impaired for the other one. We propose that interindividual differences in extrapolation efficiency might be the mechanism governing this effect. Together, our results indicate that both a realistic experimental environment and subject-specific differences modulate the ability of audiovisual motion prediction and need to be considered in future research.

SUBMITTER: Dittrich S

PROVIDER: S-EPMC5871701 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Temporal Audiovisual Motion Prediction in 2D- vs. 3D-Environments.

Dittrich Sandra S Noesselt Tömme T

Frontiers in psychology 20180321

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 experim ...[more]

PMID: 29618999

Similar Datasets

Project description:The accuracy of motion prediction, utilized to overcome the system latency of motion management radiotherapy systems, is hampered by irregularities present in the patients' respiratory pattern. Audiovisual (AV) biofeedback has been shown to reduce respiratory irregularities. The aim of this study was to test the hypothesis that AV biofeedback improves the accuracy of motion prediction.An AV biofeedback system combined with real-time respiratory data acquisition and MR images were implemented in this project. One-dimensional respiratory data from (1) the abdominal wall (30 Hz) and (2) the thoracic diaphragm (5 Hz) were obtained from 15 healthy human subjects across 30 studies. The subjects were required to breathe with and without the guidance of AV biofeedback during each study. The obtained respiratory signals were then implemented in a kernel density estimation prediction algorithm. For each of the 30 studies, five different prediction times ranging from 50 to 1400 ms were tested (150 predictions performed). Prediction error was quantified as the root mean square error (RMSE); the RMSE was calculated from the difference between the real and predicted respiratory data. The statistical significance of the prediction results was determined by the Student's t-test.Prediction accuracy was considerably improved by the implementation of AV biofeedback. Of the 150 respiratory predictions performed, prediction accuracy was improved 69% (103/150) of the time for abdominal wall data, and 78% (117/150) of the time for diaphragm data. The average reduction in RMSE due to AV biofeedback over unguided respiration was 26% (p < 0.001) and 29% (p < 0.001) for abdominal wall and diaphragm respiratory motion, respectively.This study was the first to demonstrate that the reduction of respiratory irregularities due to the implementation of AV biofeedback improves prediction accuracy. This would result in increased efficiency of motion management techniques affected by system latencies used in radiotherapy.

Dataset Information

Temporal Audiovisual Motion Prediction in 2D- vs. 3D-Environments.

Publications

Temporal Audiovisual Motion Prediction in 2D- vs. 3D-Environments.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets