Direction specific biases in human visual and vestibular heading perception.
Heading direction is determined from visual and vestibular cues. Both sensory modalities have been shown to have better direction discrimination for headings near straight ahead. Previous studies of visual heading estimation have not used the full range of stimuli, and vestibular heading estimation...
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| Formato: | article |
| Lenguaje: | EN |
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Public Library of Science (PLoS)
2012
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| Acceso en línea: | https://doaj.org/article/c8669af691d64dc0b63d005a0f2bb1e4 |
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| Sumario: | Heading direction is determined from visual and vestibular cues. Both sensory modalities have been shown to have better direction discrimination for headings near straight ahead. Previous studies of visual heading estimation have not used the full range of stimuli, and vestibular heading estimation has not previously been reported. The current experiments measure human heading estimation in the horizontal plane to vestibular, visual, and spoken stimuli. The vestibular and visual tasks involved 16 cm of platform or visual motion. The spoken stimulus was a voice command speaking a heading angle. All conditions demonstrated direction dependent biases in perceived headings such that biases increased with headings further from the fore-aft axis. The bias was larger with the visual stimulus when compared with the vestibular stimulus in all 10 subjects. For the visual and vestibular tasks precision was best for headings near fore-aft. The spoken headings had the least bias, and the variation in precision was less dependent on direction. In a separate experiment when headings were limited to ± 45°, the biases were much less, demonstrating the range of headings influences perception. There was a strong and highly significant correlation between the bias curves for visual and spoken stimuli in every subject. The correlation between visual-vestibular and vestibular-spoken biases were weaker but remained significant. The observed biases in both visual and vestibular heading perception qualitatively resembled predictions of a recent population vector decoder model (Gu et al., 2010) based on the known distribution of neuronal sensitivities. |
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