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Zhu , Jian-Qiao (2018) The sampling brain. PhD thesis, University of Warwick.
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WRAP_Theses_Zhu_2018.pdf - Submitted Version - Requires a PDF viewer. Download (15Mb) | Preview |
Official URL: http://webcat.warwick.ac.uk/record=b3437828~S15
Abstract
Understanding the algorithmic nature of mental processes is of vital importance to psychology, neuroscience, and artificial intelligence. In response to a rapidly changing world and computational demanding cognitive tasks, evolution may have endowed us with brains that are approximating rational solutions, such that our performance is close to optimal. This thesis suggests one instance of the approximation algorithms, sample-based approximation, to be implemented by the brain to tackle complex cognitive tasks. Knowing that certain types of sampling is used to generate mental samples, the brain could also actively correct for the uncertainty comes along with the sampling process. This correction process for samples left traces in human probability estimates, suggesting a more rational account of sample-based estimations. In addition, these mental samples can come from both observed experiences (memory) and synthesised experiences (imagination). Each source of mental samples has unique role in learning tasks and the classical error-correction principle of learning can be generalised when mental-sampling processes are considered.
| Item Type: | Thesis or Dissertation (PhD) | ||||
|---|---|---|---|---|---|
| Subjects: | B Philosophy. Psychology. Religion > BF Psychology | ||||
| Library of Congress Subject Headings (LCSH): | Cognition -- Mathematical models, Sampling (Statistics), Algorithms, Bayesian statistical decision theory | ||||
| Official Date: | December 2018 | ||||
| Dates: |
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| Institution: | University of Warwick | ||||
| Theses Department: | Department of Psychology | ||||
| Thesis Type: | PhD | ||||
| Publication Status: | Unpublished | ||||
| Supervisor(s)/Advisor: | Ludvig, Elliot Andrew, 1975- ; Sanborn, Adam N. | ||||
| Format of File: | |||||
| Extent: | xv, 147 leaves : illustrations, charts | ||||
| Language: | eng |
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