Search in real-world routes
Search in real-world routes
The structural nature of the world often provides a clear guide to where sought objects are likely to appear, as well as the kind of objects they may repeatedly appear in the presence of. The relationship between the targets, distractors and the landscape provides context, which ensures efficient search. This thesis will explore the dynamics of how knowledge of the environment ahead will inform search on future presentation of those scenes, as well as explore how several factors between individuals (such as cognitive resources, or tendencies towards anxiety) may influence search and learning processes. This thesis reports three studies using a new eye movement experimental paradigm termed the repeated scenes search task (RSST). This task presented scenes taken on a route around a suburban neighbourhood as search arrays, while participants searched for target superimposed in naturalistic locations. The scenes were presented on 8 occasions in each experiment, and performance improved with number of repeats. In the experimental chapters the influence of scene order on search was examined with targets appearing in several contingencies with relation to scene identity and compared between the scenes appearing in a consistent or randomised order. Subtle benefits to search were found when scenes were presented in a consistent order. The influence of boosting WM and inducing a state of anxiety upon participant responses (via more efficient eye movements) were also examined. The impact of these findings upon the general literature and with regard to individuals searching in dangerous environments are discussed, with the key finding that attentional networks, working memory and a state of anxiety are important factors to consider in search through familiar environments.
University of Southampton
Tew, Oliver David
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September 2017
Tew, Oliver David
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Donnelly, Nicholas
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Garner, Matthew
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Hadwin, Julie
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Godwin, Hayward
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Liversedge, Simon P
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Tew, Oliver David
(2017)
Search in real-world routes.
University of Southampton, Doctoral Thesis, 158pp.
Record type:
Thesis
(Doctoral)
Abstract
The structural nature of the world often provides a clear guide to where sought objects are likely to appear, as well as the kind of objects they may repeatedly appear in the presence of. The relationship between the targets, distractors and the landscape provides context, which ensures efficient search. This thesis will explore the dynamics of how knowledge of the environment ahead will inform search on future presentation of those scenes, as well as explore how several factors between individuals (such as cognitive resources, or tendencies towards anxiety) may influence search and learning processes. This thesis reports three studies using a new eye movement experimental paradigm termed the repeated scenes search task (RSST). This task presented scenes taken on a route around a suburban neighbourhood as search arrays, while participants searched for target superimposed in naturalistic locations. The scenes were presented on 8 occasions in each experiment, and performance improved with number of repeats. In the experimental chapters the influence of scene order on search was examined with targets appearing in several contingencies with relation to scene identity and compared between the scenes appearing in a consistent or randomised order. Subtle benefits to search were found when scenes were presented in a consistent order. The influence of boosting WM and inducing a state of anxiety upon participant responses (via more efficient eye movements) were also examined. The impact of these findings upon the general literature and with regard to individuals searching in dangerous environments are discussed, with the key finding that attentional networks, working memory and a state of anxiety are important factors to consider in search through familiar environments.
Text
Search in real-world routes
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Published date: September 2017
Identifiers
Local EPrints ID: 420030
URI: http://eprints.soton.ac.uk/id/eprint/420030
PURE UUID: eb17ac27-af2c-420a-810a-aced11b4902f
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Date deposited: 25 Apr 2018 16:31
Last modified: 16 Mar 2024 04:01
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Contributors
Author:
Oliver David Tew
Thesis advisor:
Nicholas Donnelly
Thesis advisor:
Simon P Liversedge
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