These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
Pubmed for Handhelds
PUBMED FOR HANDHELDS
Search MEDLINE/PubMed
Title: Hidden from view: Statistical learning exposes latent attentional capture. Author: Hilchey MD, Pratt J. Journal: Psychon Bull Rev; 2019 Oct; 26(5):1633-1640. PubMed ID: 31152432. Abstract: Contingent-capture cueing paradigms have long shown that salient visual stimuli-both abrupt onsets and color singleton cues-fail to reliably capture attention if they do not resemble the search target. There may, however, be latent attentional capture in these situations, based on recent evidence that abrupt-onset cues can capture attention in difficult, but not easy, search displays (Gaspelin, Ruthruff, & Lien in Journal of Experimental Psychology: Human Perception and Performance, 42, 1104-1120, 2016). To test this notion, we hypothesized that it should be possible to expose any latent capture generated by cues by means of statistical learning. In two versions of the classic four-location contingent-capture paradigm with easy search displays, cues either matched or mismatched (Exp. 1, color singleton; Experiment 2, abrupt-onset singleton) a target defined by a unique color in an array of distractors. Unbeknownst to participants, in both experiments the mismatch cue predicted the upcoming target location (81.5%), whereas the match cue did not (25%). Replicating typical findings, capture was robust and stable over time for the match cues. Mismatch color cues consistently failed to produce capture throughout the experiment. Importantly, mismatch abrupt-onset cues did produce capture after the first block of trials (i.e., after statistical learning). This dissociation exposes latent capture by abrupt-onset cues. Together, the findings suggest that attentional control sets are not so powerful that all information is filtered out, while also showing that statistical learning is not so powerful that it undermines all top-down control.[Abstract] [Full Text] [Related] [New Search]