Probability cueing of singleton-distractor locations in visual search: Priority-map- versus dimension-based inhibition?

Abstract

Observers can learn the likely locations of salient distractors in visual search, reducing their potential to cause interference. While there is agreement that this involves positional suppression of the likely distractor location(s), it is contentious at which stage the suppression operates: the search-guiding priority map, which integrates feature-contrast signals (e.g., generated by a red amongst green or a diamond amongst circular items) across dimensions, or the distractor-defining dimension. On the latter, dimension-based account (Sauter et al., 2018), processing of, say, a shape-defined target should be unaffected by distractor suppression when the distractor is defined by color, because in this case only color signals would be suppressed. At odds with this, Wang & Theeuwes (2018a) found slowed processing of the target when it appeared at the likely (vs. an unlikely) distractor location, consistent with priority-map-based suppression. Adopting their paradigm, the present study replicated this target location effect. Crucially, however, changing the paradigm by making the target appear as likely at the frequent as at any of the rare distractor locations and making the distractor/non-distractor color assignment consistent abolished the target location effect, without impacting the reduced interference for distractors at the frequent location. These findings support a flexible locus of spatial distractor suppressionpriority-map-or dimension-based-depending on the prominence of distractor 'cues' provided by the paradigm.

Distractor suppression leads to reduced flanker interference

“…We replicated the main findings of Wang and Theeuwes (2018a, b): reduced capture for when the color distractor was presented at the high-probability location relative to the low-probability location, and less efficient selection of the target when it was presented at the high-probability location (for studies that do not show a similar target suppression, see Sauter, Liesefeld, Zehetleitner, & Müller, 2018;van Moorselaar, Daneshtalab, and Slagter, 2020;Zhang, Allenmark, Liesefeld, Shi, & Müller, 2019). This demonstrates that the results are robust and easily reproduced using an online version of this experiment.…”

Acquisition and Use of ‘Priors’ in Autism: Typical in Deciding Where to Look, Atypical in Deciding What Is There

“…In our variant of this paradigm, the to-be-ignored distractor was more likely to appear in one specific display region (e.g., the upper region), while the target appeared with equal likelihood at all possible locations-that is, the likely distractor region was probabilistically 'cued'. Prior work has shown that TD participants can acquire this cue by statistical learning and use it to 'proactively' (e.g., Geng 2014) and tonically suppress salient distractors occurring at the likely, or 'frequent', positions, as evidenced by such distractors generating less reaction-time (RT) interference-indicative of less 'involuntary attentional capture'-compared to distractors occurring at unlikely, or 'rare', locations (Allenmark et al 2019;Sauter et al 2018;Wang and Theeuwes 2018a;Zhang et al 2019).…”

“…The sample size needed for 90% power was calculated based on the effect sizes of the distractor-location effects in two previous probability-cueing studies using a similar paradigm: the study of (Wang and Theeuwes 2018a) and our replication of this study (Zhang et al 2019). Since our previous study had two sessions, we calculated the effect size based on the first session only: our effect size was comparable to that reported by Wang and Theeuwes but slightly smaller (d z = 1.8 compared to d z = 2.0), and so we conservatively based our power calculation on that, smaller effect size.…”

Changes in visual cortical processing attenuate singleton distraction during visual search

“…Likewise, a spatially unpredictable color or luminance singleton distractor that initially captures attention, no longer does so when it recurs (Gaspelin, Gaspar, & Luck, 2019;Geng & Diquattro, 2010;Geyer, Muller, & Krummenacher, 2006;Vatterott & Vecera, 2012). Interestingly, the color of the singleton does not have to be the same as long as the color singleton is predictably a distractor (Gaspelin, Leonard, & Luck, 2017;Stilwell & Vecera, 2019;Vatterott, Mozer, & Vecera, 2018;Won, Kosoyan, & Geng, 2019;Yantis & Egeth, 1999;Zhang et al, 2019). Statistical learning of distractor properties, regardless of whether it is spatial or feature-based, appears to occur relatively fast.…”

“…For example, a number of studies have shown that spatial locations with a high probability of containing a salient distractor are de-prioritized and this reduces distractor interference. Evidence of spatial de-prioritization comes from the finding that it takes longer to find targets that (infrequently) appear in that same location (Failing & Theeuwes, 2018;Ferrante et al, 2018;Wang & Theeuwes, 2018a, 2018bWang, van Driel, Ort, & Theeuwes, 2019;Zhang, Allenmark, Liesefeld, Shi, & Müller, 2019). Likewise, a spatially unpredictable color or luminance singleton distractor that initially captures attention, no longer does so when it recurs (Gaspelin, Gaspar, & Luck, 2019;Geng & Diquattro, 2010;Geyer, Muller, & Krummenacher, 2006;Vatterott & Vecera, 2012).…”