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The role of vision in detecting and correcting fingertip force errors during object lifting
Author(s) -
Gavin Buckingham,
Nathalie S. Ranger,
Melvyn A. Goodale
Publication year - 2011
Publication title -
journal of vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.126
H-Index - 113
ISSN - 1534-7362
DOI - 10.1167/11.1.4
Subject(s) - computer vision , lift (data mining) , computer science , haptic technology , artificial intelligence , illusion , task (project management) , set (abstract data type) , object (grammar) , cognitive psychology , psychology , engineering , machine learning , systems engineering , programming language
Vision provides many reliable cues about the likely weight of an object, allowing individuals to predict how heavy it will be. The forces used to lift an object for the first time reflect these predictions. This, however, leads to inevitable errors during lifts of objects that weigh unexpected amounts. Fortunately, these errors are rarely made twice in a row-lifters have the impressive ability to detect and correct large or small misapplications of fingertip forces, even while experiencing weight illusions. Although it has been assumed that we detect and correct these errors exclusively with our sense of touch, recent evidence has demonstrated a role for vision in this fingertip force scaling. Here, we demonstrate that even when stimulus set size, delay, and modality are controlled for, individuals are unable to skillfully scale their grip and load force rates over repeated lifts without vision. However, eliminating only the task-relevant visual information, while maintaining the rest of the visual world, shifts participants back into the normal, skilled mode of control. These findings clarify the role of visual information in the ostensibly haptic task of lifting objects, suggesting individuals use priors under conditions where uncertainty is high.

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