In recent years, computational neuroscientists have suggested that human behaviour, including perception, occurs in a manner consistent with Bayesian inference. According to the Bayesian ideal observer model, the observer combines cues from multiple sensory streams as a weighted average based on each cue’s reliability. Most cue-combination research has focused on integration of cues between sensory modalities or within the visual modality. Cue combination within the tactile modality has been relatively rarely studied, and it is still not known whether cues from individual digits combine optimally. In this thesis, we use the ideal observer model to determine whether cues from three different digits are combined optimally. We predicted that cues from multiple digits would be combined according to the optimal cue combination model. To test our hypothesis, we devised a two-interval forced choice (2IFC) task where participants had to discriminate the distal/proximal location of a 1-mm thick edge across the fingerpad(s) of the index (D2), middle (D3), and ring (D4) fingers. We used a Bayesian adaptive method, the ψ method, to compute participants’ psychometric functions for single-digit (D2, D3, and D4) and multiple-digit (D23, D24, D34, and D234) conditions. We determined the stimulus level ∆x, the distance (mm) between the distal and proximal stimuli locations, at 76% correct probability. This distance corresponds to a sensitivity index d'=1 and is the σ value of the participant’s stimulus measurement distribution. We then used the single-digit σ values to predict optimal cue combination for the multiple-digits combinations. We did not observer optimal cue-combination between the digits in this study. We outline potential implications the results of this experimental have on determining how the nervous system combines cues between digits, focusing on theoretical and experimental updates to the experiment that might result in the observation of optimal cue combination between digits. / Thesis / Master of Science (MSc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/23761 |
| Date | January 2018 |
| Creators | Prodribaba, Nina |
| Contributors | Goldreich, Daniel, Neuroscience |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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