 Testing Whether Humans Have an Accurate Model of Their Own Motor Uncertainty in a Speeded Reaching TaskHang Zhang, Nathaniel D Daw and Laurence T Maloney PLOS Computational Biology, 2013, vol. 9, issue 5, 1-11 Abstract: In many motor tasks, optimal performance presupposes that human movement planning is based on an accurate internal model of the subject's own motor error. We developed a motor choice task that allowed us to test whether the internal model implicit in a subject's choices differed from the actual in isotropy (elongation) and variance. Subjects were first trained to hit a circular target on a touch screen within a time limit. After training, subjects were repeatedly shown pairs of targets differing in size and shape and asked to choose the target that was easier to hit. On each trial they simply chose a target – they did not attempt to hit the chosen target. For each subject, we tested whether the internal model implicit in her target choices was consistent with her true error distribution in isotropy and variance. For all subjects, movement end points were anisotropic, distributed as vertically elongated bivariate Gaussians. However, in choosing targets, almost all subjects effectively assumed an isotropic distribution rather than their actual anisotropic distribution. Roughly half of the subjects chose as though they correctly estimated their own variance and the other half effectively assumed a variance that was more than four times larger than the actual, essentially basing their choices merely on the areas of the targets. The task and analyses we developed allowed us to characterize the internal model of motor error implicit in how humans plan reaching movements. In this task, human movement planning – even after extensive training – is based on an internal model of human motor error that includes substantial and qualitative inaccuracies.Author Summary: When you play darts, which part of the dartboard do you aim at? The tiny bull's eye is worth 50 points. The 20-point section is much larger. If you're not very good at dart throwing you may want to take that into account. Your choice of target depends not on how good you are but on how good you think you are – your internal model of your own motor error distribution. If you think you hit exactly where you aim, you should aim at the bull's eye. If you assume you have less error in the horizontal direction, you would tend to choose vertically elongated targets over horizontally elongated ones. Previous work in movement planning hints that people have accurate models of their own motor error; we test this hypothesis. People first practiced speeded reaching to touch targets. We then asked them to choose between targets of varying shapes and sizes. Their pattern of choices allows us to estimate their internal models of their own motor error and compare them to their actual motor error distributions. We found – in contrast to previous work – that people's models of their own motor ability were markedly inaccurate. Date: 2013 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1003080 DOI: 10.1371/journal.pcbi.1003080 Access Statistics for this article More articles in PLOS Computational Biology from Public Library of Science |
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