Work

Modelling Guided Attention using Learned Target-category Associations

Year: 2020
Type: dissertation
Abstract: In addition to saliency and goal-based factors, a scene’s semantic content has been shown to guide attention in visual search tasks. Here, we ask if this rapidly available guidance signal can be leveraged to learn new attentional strategies. In two variants of the scene preview paradigm (Castelhano Heaven, 2010), participants searched for targets embedded in real-world scenes with target locations linked to scene gist. In one experiment, we found that activating gist with scene previews significantly increased search efficiency in a manner consistent with formal theories of skill acquisition. In the second experiment, search efficiency significantly increased across the experiment; however, this learning effect did not differ between preview condition. Results from a computational model suggest that, when preview information is useful, stimulus features may amplify the similarities and differences between exemplars.
Language: English
Open Access status: green
OpenAlex ID: W3154636660
Cites: 43
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Related to: 10