The study of tactile illusions like visual illusions can reveal the brain's processing strategies. A famous tactile illusion is the cutaneous rabbit illusion. Fundamental to this illusion is the perceptual length contraction phenomenon: two taps that occur in rapid succession on the forearm are perceived as occurring closer together than they were physically placed. Our lab previously proposed a Bayesian probabilistic model that views perception as a compromise between expectation (prior experience) and sensation (likelihood of sensorineural data given hypothesized tap locations). The model proposes a low-speed prior, an expectation based on experience that objects tend to be stationary or to move slowly on the skin. When the sensation of space is unclear (e.g., taps are weak), the model predicts that expectation will strongly influence perception. Consistent with this prediction, our lab previously showed that the use of weaker taps causes more pronounced perceptual length contraction. Here we report psychophysical tests on 64 participants, which confirmed this finding. Our study also used stimulus sequences consisting of a weak and a strong tap, for which the Bayesian model predicts an asymmetric perceptual length contraction, such that the weaker tap location will be perceived to shift more than the stronger tap. The experimental results confirmed this prediction, providing further support for our Bayesian probabilistic model as an explanation for perceptual length contraction. However, our results revealed a discrepancy in the data at the smaller SOAs, which showed less length contraction than predicted. We hypothesized that participants might overestimate the smaller SOAs, an effect our lab defines as time dilation. Accordingly, in a second study we investigated the effects of varying SOA and lengths on perceived SOA. The model predicts more pronounced time dilation at smaller SOAs and larger lengths. The psychophysical data from 37 participants confirmed the trends predicted by the model. / Thesis / Master of Science (MSc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/24277 |
| Date | 23 November 2018 |
| Creators | Deep, Akash |
| Contributors | Goldreich, Daniel, Neuroscience |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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