Rangelov, D. and Muller, Hermann J. and Zehetleitner, M. (2017) Failure to pop out: feature singletons do not capture attention under low signal-to-noise ratio conditions. Journal of Experimental Psychology: General 146 (5), pp. 651-671. ISSN 0096-3445.
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Failure to pop out feature singletons do not capture attention under low signal to noise ratio conditions_Dragan.pdf - Author's Accepted Manuscript Download (5MB) | Preview |
Abstract
Pop-out search implies that the target is always the first item selected, no matter how many distractors are presented. However, increasing evidence indicates that search is not entirely independent of display density even for pop-out targets: search is slower with sparse (few distractors) than with dense displays (many distractors). Despite its significance, the cause of this anomaly remains unclear. We investigated several mechanisms that could slow down search for pop-out targets. Consistent with the assumption that pop-out targets frequently fail to pop out in sparse displays, we observed greater variability of search duration for sparse displays relative to dense. Computational modeling of the response time distributions also supported the view that pop-out targets fail to pop out in sparse displays. Our findings strongly question the classical assumption that early processing of pop-out targets is independent of the distrac- tors. Rather, the density of distractors critically influences whether or not a stimulus pops out. These results call for new, more reliable measures of pop-out search and potentially a reinterpretation of studies that used relatively sparse displays.
Metadata
| Item Type: | Article |
|---|---|
| Additional Information: | ©American Psychological Association 2017. This paper is not the copy of record and may not exactly replicate the authoritative document published in the APA journal. Please do not copy or cite without author's permission. The final article is available, upon publication, at the DOI cited above. |
| Keyword(s) / Subject(s): | Attentional capture, Distribution analyses, Efficient visual search, Mixture models |
| Depositing User: | Hermann Muller |
| Date Deposited: | 16 Mar 2018 08:44 |
| Last Modified: | 04 Jul 2022 12:26 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/21565 |
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