Raat, Emma Marie ORCID: https://orcid.org/0000-0001-6748-5186 (2023) Getting the gist of it: An investigation of gist processing and the learning of novel gist categories. PhD thesis, University of York.
Abstract
Gist extraction rapidly processes global structural regularities to provide access to the general meaning and global categorizations of our visual environment – the gist. Medical experts can also extract gist information from mammograms to categorize them as normal or abnormal. However, the visual properties influencing the gist of medical abnormality are largely unknown. It is also not known how medical experts, or any observer for that matter, learned to recognise the gist of new categories. This thesis investigated the processing and acquisition of the gist of abnormality. Chapter 2 observed no significant differences in performance between 500 ms and unlimited viewing time, suggesting that the gist of abnormality is fully accessible after 500 ms and remains available during further visual processing. Next, chapter 3 demonstrated that certain high-pass filters enhanced gist signals in mammograms at risk of future cancer, without affecting overall performance. These filters could be used to enhance mammograms for gist risk-factor scoring. Chapter 4’s multi-session training showed that perceptual exposure with global feedback is sufficient to induce learning of a new gist categorisation. However, learning was affected by individual differences and was not significantly retained after 7-10 days, suggesting that prolonged perceptual exposure might be needed for consolidation. Chapter 5 observed evidence for the neural signature of gist extraction in medical experts across a network of regions, where neural activity patterns showed clear individual differences. Overall, the findings of this thesis confirm the gist extraction of medical abnormality as a rapid, global process that is sensitive to spatial structural regularities. Additionally, it was shown that a gist category can be learned via global feedback, but this learning is hard to retain and is affected by individual differences. Similarly, individual differences were observed in the neural signature of gist extraction by medical experts.
Metadata
Supervisors: | Evans, Karla |
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Related URLs: | |
Keywords: | gist; gist of abnormality; expertise; medical expertise; radiology; mammography; rapid visual processing; EEG; statistical learning; DNN |
Awarding institution: | University of York |
Academic Units: | The University of York > Psychology (York) |
Identification Number/EthosID: | uk.bl.ethos.883561 |
Depositing User: | Ms. Emma Raat |
Date Deposited: | 21 Jun 2023 10:28 |
Last Modified: | 21 Jul 2023 09:53 |
Open Archives Initiative ID (OAI ID): | oai:etheses.whiterose.ac.uk:33057 |
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