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Hyuksu Lee, Do-Joon Yi; Visual short-term memory load does not enhance attentional selection. Journal of Vision 2018;18(10):1181. doi: 10.1167/18.10.1181.
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Lavie's load theory posits that high cognitive load impairs attentional selection since the distinction between targets and distractors cannot to be actively maintained (Lavie et al., 2004). Thus, it has been assumed that taxing any types of working memory (WM) might increase distractor processing in a concurrent selection task. Two of recent studies disproved this assumption: holding multiple items in visual short-term memory (VSTM) enhances visual selection (Konstantinou, Beal, King, & Lavie, 2014; Roper & Vecera, 2014). The authors suggested that the encoding and maintenance in VSTM might deplete perceptual resources for filtering distractors. Notwithstanding their plausible arguments, however, here we report a series of four experiments, in which we consistently failed to reproduce such effects. Participants performed a canonical flanker task while maintaining colors of squares in VSTM. We contrasted the low load versus high load conditions (1 vs. 4) in an intermixed design or in a blocked design (Experiment 1 and 2, respectively), or the no load versus high load conditions (0 vs. 3) in a blocked design (Experiment 3). In all three experiments, flanker interference did not decrease with VSTM load. This failure of replication cannot be ascribed to a floor effect; flanker interference did decrease with perceptual demand (Experiment 4). We further tested any potential effects of the spatial overlap between the flanker and the memory items. VSTM load exacerbated, but not alleviate, flanker interference only when the two locations were not overlapped (Experiment 5). Finally, we submitted all four VSTM experiments to the equaivalence test (Lakens, 2017), and found that the effects of VSTM load on flanker interference were statistically equivalent to zero. Overall, our results call for new models, within which the dynamic relationships between VSTM and selective attention are fully described and tested.
Meeting abstract presented at VSS 2018
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