Implicitly learned suppression of irrelevant spatial locations

Abstract

How do we ignore a salient, irrelevant stimulus whose location is predictable? A variety of studies using instructional manipulations have shown that participants possess the capacity to exert location-based suppression. However, for the visual search challenges we face in daily life, we are not often provided explicit instructions and are unlikely to consciously deliberate on what our best strategy might be. Instead, we might rely on our past experience-in the form of implicit learning-to exert strategic control. In this paper, we tested whether implicit learning could drive spatial suppression. In Experiment 1, participants searched displays in which one location contained a target, while another contained a salient distractor. An arrow cue pointed to the target location with 70 % validity. Also, unbeknownst to the participants, the same arrow cue predicted the distractor location with 70 % validity. Results showed facilitated RTs to the predicted target location, confirming target enhancement. Critically, distractor interference was reduced at the predicted distractor location, revealing that participants used spatial suppression. Further, we found that participants had no explicit knowledge of the cue-distractor contingencies, confirming that the learning was implicit. In Experiment 2, to seek further evidence for suppression, we modified the task to include occasional masked probes following the arrow cue; we found worse probe identification accuracy at the predicted distractor location than control locations, providing converging evidence that observers spatially suppressed the predicted distractor locations. These results reveal an ecologically desirable mechanism of suppression, which functions without the need for conscious knowledge or externally guided instructions.

Region-based shielding of visual search from salient distractors: Target detection is impaired with same- but not different-dimension distractors

“…It is well-established that observers can learn to exploit uneven distributions of target locations in order to facilitate search: Targets are detected faster at locations where they appear more frequently (e.g., Anderson & Druker, 2010;Fecteau, Korjoukov, & Roelfsema, 2009;Geng & Behrmann, 2002, 2005, which Geng and Behrmann (2002) termed a target location probabilitycueing effect. Similarly, observers can learn to exploit the statistical distribution of task-irrelevant distractors to improve performance: Over time, they become better at suppressing locations where distractors appear frequently (e.g., Kelley & Yantis, 2009;Leber, Gwinn, Hong, & O'Toole, 2016;Reder, Weber, Shang, & Vanyukov, 2003). Note, though, that the relevant demonstrations have been limited to sparse visual displays that contained only a few target and distractor stimuli with a very limited number of possible distractor locations.…”

“…In Kelley and Yantis (2009), the taskrelevant red-green dot pattern consistently appeared in the display center, and a distractor (composed of the same colors) could appear at one of two, equally likely peripheral locations. Leber et al (2016) used a variation of the contingent-capture paradigm (e.g., Folk, Remington, & Johnston, 1992): There were four display locations/items (arranged in the form of a square), with the distractor display preceding the target display; distractors (which were singled out from the background stimuli by the same feature as the target: the color red) were most likely to appear at one location, defined by a fixed relationship with the likely target location that was indicated by a central arrow at the start of a trial. entails that if the frequent-distractor region was inhibited on the master saliency map, the processing of search targets appearing in this spatially suppressed region should be impaired, too.…”

“…In previous studies of distractor location probability cueing (Goschy et al, 2014;Leber et al, 2016), participants were typically unable, at the end of a lengthy experiment, to tell at above-chance level at which locations distractors were likely to appear. This was taken to suggest that the distractor probability cueing effect is essentially implicit in nature (Reder et al, 2003, too, assume that their negative location priming effect operates outside conscious awareness, though without having examined for this).…”

Suppression history of distractor location biases attentional and oculomotor control

“…However, recent evidence has suggested that similar, symmetrical effects, can be observed also with respect to visual information that instead has been associated with an history of suppression. Considering in particular the deployment of selective attention across the visual space, the degree of interference determined by salient but irrelevant visual stimuli that appear at locations that in the past have been often associated with distracting events is reduced (Ferrante et al, 2018;Goschy, Bakos, Müller, & Zehetleitner, 2014;Leber, Gwinn, Hong, & O'Toole, 2016;Sauter, Liesefeld, & Müller, in press;Sauter, Liesefeld, Zehetleitner, & Müller, 2018;Wang & Theeuwes, 2018a; for a recent review see Chelazzi, Marini, Pascucci, & Turatto, 2019). Depending on the specific manipulations performed, these effects have been observed for relatively wide regions of the visual field (i.e., contrasting visual hemifields with high vs. low distractor frequency, as in Goschy et al, 2014;Sauter et al, 2018;in press), but also for discrete spatial locations, emerging in a graded fashion which reflected the precise statistical contingencies applied (Ferrante et al, 2018;Wang & Theeuwes, 2018a.…”

Neural mechanisms underlying distractor inhibition on the basis of feature and/or spatial expectations

“…Critically, however, this appears specific to conditions that allow observers to learn, often implicitly, from statistical regularities across search displays (Chelazzi, Marini, Pascucci, & Turatto, 2019;van Moorselaar & Slagter, 2020). For example, a recent study by Wang and Theeuwes (2018) demonstrated that in a mixed-feature variant of the additional singleton paradigm, salient singleton distractors were more efficiently ignored at locations with a high distractor probability, even though almost all participants were unaware of the embedded statistical regularity (see also) (Ferrante et al, 2018;Goschy, Bakos, Müller, & Zehetleitner, 2014;Leber, Gwinn, Hong, & O'Toole, 2016). These findings add to a rapidly growing body of research that shows that distractor inhibition may not be under voluntary, top-down control, but greatly relies on expectations derived from past experience about the likelihood of events or statistical learning (Noonan, Crittenden, Jensen, & Stokes, 2018;Theeuwes, 2019;van Moorselaar & Slagter, 2020).…”