Seeing virtual while acting real: Visual display and strategy effects on the time and precision of eye-hand coordination
March 30, 2018 Β· Declared Dead Β· π PLoS ONE
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
A. U. Batmaz, M. de Mathelin, Birgitta Dresp-Langley
arXiv ID
1804.00974
Category
cs.HC: Human-Computer Interaction
Citations
49
Venue
PLoS ONE
Last Checked
3 months ago
Abstract
Effects of computer generated 2D and 3D views on the time and precision of bare-handed or tool-mediated eye-hand coordination were investigated in a pick-and-place-task with complete novices. All of them scored well above average in spatial perspective taking ability and performed the task with their dominant hand. Two groups of novices, four men and four women in each group, had to place a small object in a precise order on the centre of five targets on a Real-world Action Field (RAF), as swiftly as possible and as precisely as possible, using a tool or not (control). Each individual session consisted of four visual display conditions. The order of conditions was counterbalanced between individuals and sessions. Subjects looked at what their hands were doing 1) directly in front of them (natural top-down view) 2) in topdown 2D fisheye camera view 3) in top-down undistorted 2D view or 4) in 3D stereoscopic top-down view (head-mounted OCULUS DK 2). It was made sure that object movements in all image conditions matched the real-world movements in time and space. One group was looking at the 2D images with the monitor positioned sideways (sub-optimal); the other group was looking at the monitor placed straight ahead of them (near-optimal). All image viewing conditions had significantly detrimental effects on time (seconds) and precision (pixels) of task execution when compared with natural direct viewing.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted