Learning to suppress salient distractors in the target dimension: Region-based inhibition is persistent and transfers to distractors in a nontarget dimension.

Abstract

It was shown previously that observers can learn to exploit an uneven spatial distribution of singleton distractors to better shield visual search from distractors in the frequent versus the rare region (i.e., distractor location probability cueing; Sauter, Liesefeld, Zehetleitner, & Müller, 2018). However, with distractors defined in the same dimension as the search target, this comes at the cost of impaired detection of targets in the frequent region. In 3 experiments, the present study investigated the learning and unlearning of distractor location probability cueing and the carry-over of cueing effects from same- to different-dimension distractors. All experiments involved a visual search for an orientation-defined singleton target in the presence of either a more salient color-defined (different-dimension) or orientation-defined (same-dimension) distractor singleton, and all were divided into a learning session and a subsequent test session. The present study showed that with same-dimension (but not with different-dimension) distractors, the acquired cueing effect persists over a 24-h break between the training and test session and takes several hundred trials to be unlearned when the distribution is changed to even (50%/50%) in the test session. Furthermore, the target location effect as well as (somewhat less marked) the cueing effect carries over from learning with same-dimension distractors to test with different-dimension distractors. These carry-over effects are in line with the assumption that the learned distractor suppression effects are implemented at different levels in the hierarchical architecture of search guidance: the saliency map with same-dimension distractors versus a dimension-based level below the saliency map with different-dimension distractors.

Predictability of salient distractor increases top-down control in healthy younger and older adults

“…Moreover, the present findings are in line with previous reports that younger adults are able to reduce distractor interference and improve top-down control when conditions afford valid predictions about the location of an upcoming distractor (Awh et al, 2003;Chao, 2010;Havlíček et al, 2018;Ruff & Driver, 2006;Sauter et al, 2019Sauter et al, , 2018Wang & Theeuwes, 2018;Watson & Humphreys, 1997). Note, however, that Noonan et al (2016) have argued that top-down controlled distractor suppression is possible only when observers can make stable predictions, which is not the case with foreknowledge of the distractor location provided by trial-wise presented (i.e., with regard to the indicated location: flexible) cues.…”

“…However, younger adults can mitigate the detrimental effects of strong bottom-up signals by utilising prior information ('expectancies') about where an upcoming distractor is likely to occur. That is, they can improve top-down controlled weighting of stimuli, and thus reduce distractor interference, when they can make valid predictions about the (likely) location of an upcoming distractor based on spatial pre-cues (Awh, Matsukura, & Serences, 2003;Chao, 2010;Havlíček, Müller, & Wykowska, 2018;Noonan et al, 2016;Ruff & Driver, 2006;Watson & Humphreys, 1997) or based on statistical learning of the spatial distribution of distractors in within a search display (Goschy, Bakos, Müller, & Zehetleitner, 2014;Sauter, Liesefeld, & Müller, 2019;Sauter, Liesefeld, Zehetleitner, & Müller, 2018). For older adults who are affected by agerelated reductions in attentional capacity, a preserved ability to use predictive information about where distracting stimuli might appear would be of particular importance.…”

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

“…Recently, there has been a growing interest in statistical, location-probability learning in visual search. While most of this research has focused on the learning of target locations (e.g., Druker & Anderson, 2010;Geng & Behrmann, 2002Jiang, Swallow, & Rosenbaum, 2013;Walthew & Gilchrist, 2006; see also Miller, 1988;Müller & Findlay, 1987;Shaw & Shaw, 1977), more recently, there have been various attempts to extend this to the learning of distractor locations (e.g., Ferrante, Patacca, Di Caro, Della Libera, Santandrea, & Chelazzi, 2018;Goschy, Bakos, Müller, & Zehetleitner, 2014;Leber, Gwinn, Hong, & O'Toole, 2016;Sauter, Liesefeld, Zehetleitner, & Müller, 2018;Sauter, Liesefeld, & Müller, 2019;Wang & Theeuwes, 2018a).…”

Probing the Neural Mechanisms for Distractor Filtering and Their History-Contingent Modulation by Means of TMS

“…Different putative mechanisms for distractor filtering have been proposed depending on the adopted experimental paradigm and behavioral context (Chelazzi et al, 2019). These mechanisms include proactive control (Marini et al, 2013(Marini et al, , 2016Geng, 2014;Cosman et al, 2018), habituation of capture (Neo and Chua, 2006;Pascucci and Turatto, 2015;Turatto et al, 2018a,b), intertrial priming (Geyer et al, 2008;Müller et al, 2010), and implicit distractor probability learning (Goschy et al, 2014;Ferrante et al, 2018;Wang and Theeuwes, 2018a,b;Sauter et al, 2018Sauter et al, , 2019Di Caro et al, 2019). Despite the abundance of behavioral evidence about specific task conditions and behavioral contexts wherein distractor suppression occurs, much less is known about the underlying neural mechanisms (Egeth et al, 2010;Folk and Remington, 2010;Seidl et al, 2012;Gaspar and McDonald, 2014;Geng, 2014;Liesefeld et al, 2014Liesefeld et al, , 2017Marini et al, 2016;Donohue et al, 2018).…”