Distractor-interference reduction is dimensionally constrained

Liesefeld, Heinrich René; Liesefeld, Anna M.; Müller, Hermann J.

doi:10.1080/13506285.2018.1561568

Cited by 34 publications

(42 citation statements)

References 77 publications

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“…Testing this quantitative prediction is complicated by the fact that—because of up-weighting of the target dimension—the same-dimension distractor necessarily has a higher priority (and therefore causes more interference) than the different-dimension distractor to begin with (Liesefeld et al, 2019; Liesefeld & Müller, 2019). Multiplying with the same (spatial weighting) factor has a larger effect on higher than on lower values 2 .…”

Section: Resultsmentioning

confidence: 99%

“…Targets and distractors always appeared on the second ring from fixation except for the midline vertical (12 and 6 o’clock) locations; that is, there were five possible locations per display side (relevant regions). The exclusion of midline positions and the crucial distractor-location manipulation were the only notable deviations from Liesefeld et al (2019).…”

Section: Methodsmentioning

confidence: 97%

“…The stimuli and trial procedure were virtually identical to the orientation-target group of Liesefeld, Liesefeld, and Müller (2019). Stimuli were gray bars (0.18 × 0.81°) presented on a thin-film-transistor (TFT) monitor (screen resolution: 1,920 × 1,080 pixels; refresh rate: 60 Hz), at a viewing distance of approximately 70 cm, against a black background.…”

Section: Methodsmentioning

confidence: 99%

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Modulations of saliency signals at two hierarchical levels of priority computation revealed by spatial statistical distractor learning.

Liesefeld

Müller

2021

Journal of Experimental Psychology: General

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Many attention theories assume that selection is guided by a preattentive, spatial representation of the scene that combines bottom-up stimulus information with top-down influences (task goals and prior experience) to code for potentially relevant locations (priority map). At which level(s) of priority computation top-down influences modulate bottom-up stimulus signals is an open question. In a visual-search task, here we induced experience-driven spatial suppression (statistical learning) by presenting 1 of 2 salient distractors more frequently in one display region than the other. When a distractor standing out in the same dimension as the target was spatially biased in Experiment 1, processing of both the target and another, spatially unbiased distractor standing out in a different dimension was likewise hampered in the suppressed region. This indicates that constraining spatial suppression to a specific distractor feature is not possible, and participants instead resort to purely space-based (distractor-feature-independent) suppression at a supradimensional, overall-priority map. In line with a common locus of suppression, a novel computational model of distraction in visual search captures all 3 location effects with a single spatial-weighting parameter. In contrast, when the different-dimension distractor was spatially biased in Experiment 2, processing of other objects in the suppressed region was unaffected, indicating suppression constrained to a subordinate, dimension-specific level of priority computation. In sum, we demonstrate experience-driven top-down modulations of saliency signals at the overall-priority and dimension-specific levels that do not reach down to the specific distractor features.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 97%

Section: Methodsmentioning

confidence: 99%

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Modulations of saliency signals at two hierarchical levels of priority computation revealed by spatial statistical distractor learning.

Liesefeld

Müller

2021

Journal of Experimental Psychology: General

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“…Ultimately, of course, it is the priority map via which the inhibition is always expressed in search guidance, but the true level, at which it is instantiated at least in certain conditions, may be below the priority map. This is as envisaged by the dimension-weighting account, according to which selection is ultimately based on the priority map which, however, is itself shaped by the weighting applied to the various, target- and distractor-defining feature dimensions (e.g., Liesefeld, Liesefeld, & Müller, 2019; for a recent review, see Liesefeld, Liesefeld, Pollmann, & Müller, in press).…”

Section: Discussionmentioning

confidence: 99%

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

Zhang¹,

Allenmark²,

Liesefeld³

et al. 2019

Journal of Experimental Psychology: Human Perception and Perfor

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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.

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“…Motor conflicts occur when we must deviate from a prepotent response pattern, such as when drivers need to hit the brakes because of an unexpected stop sign (Botvinick et al, 2004;Egner, 2008;Aron, 2011;Cohen, 2014a). Both attentional and motor conflicts lead to increased error rates and slower reaction times (Criaud and Boulinguez, 2013;Gaspelin and Luck, 2018a;Wessel, 2018;Liesefeld et al, 2019). To understand how the brain enables top-down control of our behavior, it is crucial to find out whether different types of conflicts are resolved by separate, domain-specific or shared, domain-general top-down control mechanisms in the brain (Cocchi et al, 2013;Mackie et al, 2013;Hampshire and Sharp, 2015;Gratton et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Motor Interference, But Not Sensory Interference, Increases Midfrontal Theta Activity and Brain Synchronization during Reactive Control

Kaiser

Schütz‐Bosbach

2021

J. Neurosci.

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Cognitive control helps us to overcome task interference in challenging situations. Resolving conflicts because of interfering influences is believed to rely on midfrontal theta oscillations. However, different sources of interference necessitate different types of control. Attentional control is needed to suppress salient distractors. Motor control is needed to suppress goal-incompatible action impulses. While previous studies mostly studied the additive effects of attentional and motor conflicts, we independently manipulated the need for attentional control (via visual distractors) and motor control (via unexpected response deviations) in an EEG study with male and female humans. We sought to find out whether these different types of control rely on the same midfrontal oscillatory mechanisms. Motor conflicts, but not attentional conflicts, elicited increases in midfrontal theta power during conflict resolution. Independent of the type of conflict, theta power was predictive of motor slowing. Connectivity analysis via phase-based synchronization indicated a widespread increase interbrain connectivity for motor conflicts, but a midfrontal-to-posterior decrease in connectivity for attentional conflicts. For each condition, we found stronger midfrontal connectivity with the parietal region contralateral to, rather than ipsilateral to, the acting hand. Parietal lateralization in connectivity was strongest for motor conflicts. Previous studies suggested that midfrontal theta oscillations might represent a general control mechanism, which aids conflict resolution independent of the conflict domain. In contrast, our results show that oscillatory theta dynamics during reactive control mostly reflect motor-related adjustments.

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Distractor-interference reduction is dimensionally constrained

Cited by 34 publications

References 77 publications

Modulations of saliency signals at two hierarchical levels of priority computation revealed by spatial statistical distractor learning.

Modulations of saliency signals at two hierarchical levels of priority computation revealed by spatial statistical distractor learning.

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

Motor Interference, But Not Sensory Interference, Increases Midfrontal Theta Activity and Brain Synchronization during Reactive Control

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