Direct Learning for Time-to-Collision Judgments of Approaching Objects: The Role of Fractal 1/f Noise in Exploration
| dc.contributor.committeeMember | DeLucia, Patricia R | |
| dc.contributor.committeeMember | Kortum, Philip | |
| dc.contributor.committeeMember | Lane, David M | |
| dc.contributor.committeeMember | Sano, Akane | |
| dc.creator | Braly, Adam M | |
| dc.date.accessioned | 2020-04-20T14:32:51Z | |
| dc.date.available | 2020-04-20T14:32:51Z | |
| dc.date.created | 2020-05 | |
| dc.date.issued | 2020-04-14 | |
| dc.date.submitted | May 2020 | |
| dc.date.updated | 2020-04-20T14:32:51Z | |
| dc.description.abstract | The purpose of this dissertation was to determine whether direct learning can improve time-to-collision (TTC) judgments of approaching objects. Accurate information for judgments of TTC is available in the optic array, but research has shown that observers do not always use this accurate information. Even though this information exists in the optic array, observers may not be attuned to use such information. According to direct learning theory, observers must be able to flexibly combine exploration and feedback to calibrate their judgments. This could explain why prior studies found that observers relied on other less reliably accurate sources of information. Research has also shown that fractal fluctuations in exploration involve fluctuations at all time scales, which ostensibly allows perceptual systems the flexibility to detect information. Therefore, fractal fluctuations in exploration may reflect coordination among detection, calibration, and attunement of information for perception or action. This dissertation tested whether judgments of TTC were significantly better when participants were permitted to make exploratory movements with feedback compared to when they were restricted and not given feedback. In a virtual environment, participants viewed scenes of an object that approached them. After a designated time, the object disappeared and participants judged when the object would have reached them, had it continued to move. Exploration and feedback were factorially crossed to create four between-subjects conditions of Exploration- Feedback, Exploration- No Feedback, No Exploration- Feedback, and No Exploration- No Feedback. Results showed that participants in the Exploration- Feedback learned to used more accurate information for their judgments and this improvement was retained in the absence of further feedback. Participants in the No Exploration- Feedback appeared to learn how to use accurate information, but in the absence of further feedback their performance degraded, suggesting a strategy based on feedback rather than learning. Results of fractal analyses revealed that exploratory movements were fractal, and that trail-by-trial fluctuations in the fractal scaling exponent predicted perceptual error. The findings have implications for theories of TTC perception and practical implications are discussed. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.citation | Braly, Adam M. "Direct Learning for Time-to-Collision Judgments of Approaching Objects: The Role of Fractal 1/f Noise in Exploration." (2020) Diss., Rice University. <a href="https://hdl.handle.net/1911/108322">https://hdl.handle.net/1911/108322</a>. | |
| dc.identifier.uri | https://hdl.handle.net/1911/108322 | |
| dc.language.iso | eng | |
| dc.rights | Copyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder. | |
| dc.subject | time-to-collision | |
| dc.subject | direct learning | |
| dc.subject | 1/f noise | |
| dc.subject | fractal | |
| dc.title | Direct Learning for Time-to-Collision Judgments of Approaching Objects: The Role of Fractal 1/f Noise in Exploration | |
| dc.type | Thesis | |
| dc.type.material | Text | |
| thesis.degree.department | Psychology | |
| thesis.degree.discipline | Social Sciences | |
| thesis.degree.grantor | Rice University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.major | Human Factors | |
| thesis.degree.name | Doctor of Philosophy |
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