Figural properties are prioritized for search under conditions of uncertainty: Setting boundary conditions on claims that figures automatically attract attention
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PetersonEtAlAP&Pinpress2016.pdf
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Final Accepted Manuscript
Affiliation
Psychology Department, University of ArizonaCognitive Science Program, University of Arizona
Issue Date
2016-10-28
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SPRINGERCitation
Figural properties are prioritized for search under conditions of uncertainty: Setting boundary conditions on claims that figures automatically attract attention 2016, 79 (1):180 Attention, Perception, & PsychophysicsRights
© The Psychonomic Society, Inc. 2016.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
Nelson and Palmer (2007) concluded that figures/figural properties automatically attract attention, after they found that participants were faster to detect/discriminate targets appearing where a portion of a familiar object was suggested in an otherwise ambiguous display. We investigated whether these effects are truly automatic and whether they generalize to another figural property-convexity. We found that Nelson and Palmer's results do generalize to convexity, but only when participants are uncertain regarding when and where the target will appear. Dependence on uncertainty regarding target location/timing was also observed for familiarity. Thus, although we could replicate and extend Nelson and Palmer's results, our experiments showed that figures do not automatically draw attention. In addition, our research went beyond Nelson and Palmer's, in that we were able to separate figural properties from perceived figures. Because figural properties are regularities that predict where objects lie in the visual field, our results join other evidence that regularities in the environment can attract attention. More generally, our results are consistent with Bayesian theories in which priors are given more weight under conditions of uncertainty.Note
12 month embargo; First Online: 28 October 2016ISSN
1943-39211943-393X
Version
Final accepted manuscriptSponsors
NSF BCS [0960529]; ONR [N00014-14-1-067]Additional Links
http://link.springer.com/10.3758/s13414-016-1223-3ae974a485f413a2113503eed53cd6c53
10.3758/s13414-016-1223-3