scholarly journals More capture, more suppression: Distractor suppression due to statistical regularities is determined by the magnitude of attentional capture

2019 ◽  
Vol 27 (1) ◽  
pp. 86-95 ◽  
Author(s):  
Michel Failing ◽  
Jan Theeuwes
Keyword(s):  
Visual Field ◽  
Implicit Learning ◽  
Attentional Capture ◽  
High Probability ◽  
Attentional Selection ◽  
Best Interests ◽  
Salient Object ◽  
Specific Location ◽  
Statistical Regularities ◽  
Task Irrelevant

AbstractSalient yet irrelevant objects often interfere with daily tasks by capturing attention against our best interests and intentions. Recent research has shown that through implicit learning, distraction by a salient object can be reduced by suppressing the location where this distractor is likely to appear. Here, we investigated whether suppression of such high-probability distractor locations is an all-or-none phenomenon or specifically tuned to the degree of interference caused by the distractor. In two experiments, we varied the salience of two task-irrelevant singleton distractors each of which was more likely to appear in one specific location in the visual field. We show that the magnitude of interference by a distractor determines the magnitude of suppression for its high-probability location: The more salient a distractor, the more it becomes suppressed when appearing in its high-probability location. We conclude that distractor suppression emerges as a consequence of the spatial regularities regarding the location of a distractor as well as its potency to interfere with attentional selection.

Spatial suppression due to statistical regularities in a visual detection task

2021 ◽  
Author(s):  
Dirk van Moorselaar ◽  
Jan Theeuwes
Keyword(s):  
Statistical Learning ◽  
Attentional Capture ◽  
High Probability ◽  
Visual Detection ◽  
Detection Task ◽  
Distractor Location ◽  
Specific Location ◽  
Statistical Regularities ◽  
Unique Shape ◽  
Singleton Target

AbstractIncreasing evidence demonstrates that observers can learn the likely location of salient singleton distractors during visual search. To date, the reduced attentional capture at high-probability distractor locations has typically been examined using so called compound search, in which by design a target is always present. Here, we explored whether statistical distractor learning can also be observed in a visual detection task, in which participants respond target present if the singleton target is present and respond target absent when the singleton target is absent. If so, this allows us to examine suppression of the location that is likely to contain a distractor both in the presence, but critically also in the absence, of a priority signal generated by the target singleton. In an online variant of the additional singleton paradigm, observers had to indicate whether a unique shape was present or absent, while ignoring a colored singleton, which appeared with a higher probability in one specific location. We show that attentional capture was reduced, but not absent, at high-probability distractor locations, irrespective of whether the display contained a target or not. By contrast, target processing at the high-probability distractor location was selectively impaired on distractor-present displays. Moreover, all suppressive effects were characterized by a gradient such that suppression scaled with the distance to the high-probability distractor location. We conclude that statistical distractor learning can be examined in visual detection tasks, and discuss the implications for attentional suppression due to statistical learning.

Spatial suppression due to statistical regularities is driven by distractor suppression not by target activation

2018 ◽  
Author(s):  
Michel Failing ◽  
Benchi Wang ◽  
Jan Theeuwes
Keyword(s):  
High Probability ◽  
Target Location ◽  
Target Selection ◽  
Visual Space ◽  
Alternative Interpretation ◽  
Distractor Location ◽  
Target Presentation ◽  
Statistical Regularities ◽  
Low Probability ◽  
Presentation Experiment

Where and what we attend to is not only determined by what we are currently looking for but also by what we have encountered in the past. Recent studies suggest that biasing the probability by which distractors appear at locations in visual space may lead to attentional suppression of high probability distractor locations which effectively reduces capture by a distractor but also impairs target selection at this location. However, in many of these studies introducing a high probability distractor location was tantamount to increasing the probability of the target appearing in any of the other locations (i.e. the low probability distractor locations). Here, we investigate an alternative interpretation of previous findings according to which attentional selection at high probability distractor locations is not suppressed. Instead, selection at low probability distractor locations is facilitated. In two visual search tasks, we found no evidence for this hypothesis: neither when there was only a bias in target presentation but no bias in distractor presentation (Experiment 1), nor when there was only a bias in distractor presentation but no bias in target presentation (Experiment 2). We conclude that recurrent presentation of a distractor in a specific location leads to attentional suppression of that location through a mechanism that is unaffected by any regularities regarding the target location.

scholarly journals Multivariate EEG analyses support high-resolution tracking of feature-based attentional selection

2016 ◽  
Author(s):  
Johannes Jacobus Fahrenfort ◽  
Anna Grubert ◽  
Christian N. L. Olivers ◽  
Martin Eimer
Keyword(s):  
Visual Field ◽  
Multivariate Analyses ◽  
Target Selection ◽  
Target Position ◽  
Attentional Selection ◽  
Forward Model ◽  
Hemispheric Differences ◽  
Neural Basis ◽  
Eeg Data ◽  
Feature Based

AbstractThe primary electrophysiological marker of feature-based selection is the N2pc, a lateralized posterior negativity emerging around 180-200 ms. As it relies on hemispheric differences, its ability to discriminate the locus of focal attention is severely limited. Here we demonstrate that multivariate analyses of raw EEG data provide a much more fine-grained spatial profile of feature-based target selection. When training a pattern classifier to determine target position from EEG, we were able to decode target positions on the vertical midline, which cannot be achieved using standard N2pc methodology. Next, we used a forward encoding model to construct a channel tuning function that describes the continuous relationship between target position and multivariate EEG in an eight-position display. This model can spatially discriminate individual target positions in these displays and is fully invertible, enabling us to construct hypothetical topographic activation maps for target positions that were never used. When tested against the real pattern of neural activity obtained from a different group of subjects, the constructed maps from the forward model turned out statistically indistinguishable, thus providing independent validation of our model. Our findings demonstrate the power of multivariate EEG analysis to track feature-based target selection with high spatial and temporal precision.Significance StatementFeature-based attentional selection enables observers to find objects in their visual field. The spatiotemporal profile of this process is difficult to assess with standard electrophysiological methods, which rely on activity differences between cerebral hemispheres. We demonstrate that multivariate analyses of EEG data can track target selection across the visual field with high temporal and spatial resolution. Using a forward model, we were able to capture the continuous relationship between target position and EEG measurements, allowing us to reconstruct the distribution of cortical activity for target locations that were never shown during the experiment. Our findings demonstrate the existence of a temporally and spatially precise EEG signal that can be used to study the neural basis of feature-based attentional selection.

scholarly journals Systemic Effects of Selection History on Learned Ignoring

2021 ◽  
Author(s):  
Andy Jeesu Kim ◽  
Brian A. Anderson
Keyword(s):  
Eye Movements ◽  
Reaction Time ◽  
Young Adult ◽  
High Probability ◽  
Time Course ◽  
Systemic Effects ◽  
Reduced Frequency ◽  
Selection History ◽  
Adult Participants ◽  
Task Irrelevant

Despite our best intentions, physically salient but entirely task-irrelevant stimuli can sometimes capture our attention. With learning, it is possible to more efficiently ignore such stimuli, although specifically how the visual system accomplishes this remains to be clarified. Using a sample of young-adult participants, we examined the time course of eye movements to targets and distractors. We replicate a reduced frequency of eye movements to the distractor when appearing in a location at which distractors are frequently encountered. This reduction was observed even for the earliest saccades, when selection tends to be most stimulus-driven. When the distractor appeared at the high-probability location, saccadic reaction time was slowed specifically for distractor-going saccades, suggesting a slowing of priority accumulation at this location. In the event that the distractor was fixated, disengagement from the distractor was also faster when it appeared in the high-probability location. Both proactive and reactive mechanisms of distractor suppression work together to minimize attentional capture by frequently-encountered distractors.

Attentional capture by completely task-irrelevant faces

2014 ◽  
Vol 79 (4) ◽  
pp. 523-533 ◽  
Author(s):  
Shiori Sato ◽  
Jun I. Kawahara
Keyword(s):  
Attentional Capture ◽  
Task Irrelevant

Sensorimotor knowledge from task-irrelevant feedback contributes to motor learning

2021 ◽  
Author(s):  
Wanying Jiang ◽  
Yajie Liu ◽  
Yuqing Bi ◽  
Kunlin Wei
Keyword(s):  
Motor Learning ◽  
Perceptual Learning ◽  
Implicit Learning ◽  
Motor Adaptation ◽  
Strategic Learning ◽  
Visuomotor Rotation ◽  
Saving Effect ◽  
Sensorimotor Knowledge ◽  
Sensory Processes ◽  
Task Irrelevant

Exposure to task-irrelevant feedback leads to perceptual learning, but its effect on motor learning has been understudied. Here we asked human participants to reach a visual target with a hand-controlled cursor while observing another cursor moving independently in a different direction. While the task-irrelevant feedback did not change the main task's performance, it elicited robust savings in subsequent adaptation to classical visuomotor rotation perturbation. We demonstrated that the saving effect resulted from a faster formation of strategic learning through a series of experiments, not from gains in the implicit learning process. Furthermore, the saving effect was robust against drastic changes in stimulus features (i.e., rotation size or direction) or task types (i.e., for motor adaptation and skill learning). However, the effect was absent when the task-irrelevant feedback did not carry the visuomotor relationship embedded in visuomotor rotation. Thus, though previous research on perceptual learning has related task-irrelevant feedback to changes in early sensory processes, our findings support its role in acquiring abstract sensorimotor knowledge during motor learning. Motor learning studies have traditionally focused on task-relevant feedback, but our study extends the scope of feedback processes and sheds new light on the dichotomy of explicit and implicit learning in motor adaptation as well as motor structure learning.

scholarly journals The Nature of Unconscious Attention to Subliminal Cues

Vision ◽  
2019 ◽  
Vol 3 (3) ◽  
pp. 38 ◽  
Author(s):  
Prasad ◽  
Mishra
Keyword(s):  
Attentional Capture ◽  
Response Times ◽  
Attentional Selection ◽  
Current Status ◽  
Future Directions ◽  
Open Questions ◽  
Selection History ◽  
Spatial Cuing ◽  
Attentional System ◽  
The Individual

Attentional selection in humans is mostly determined by what is important to them or by the saliency of the objects around them. How our visual and attentional system manage these various sources of attentional capture is one of the most intensely debated issues in cognitive psychology. Along with the traditional dichotomy of goal-driven and stimulus-driven theories, newer frameworks such as reward learning and selection history have been proposed as well to understand how a stimulus captures attention. However, surprisingly little is known about the different forms of attentional control by information that is not consciously accessible to us. In this article, we will review several studies that have examined attentional capture by subliminal cues. We will specifically focus on spatial cuing studies that have shown through response times and eye movements that subliminal cues can affect attentional selection. A majority of these studies have argued that attentional capture by subliminal cues is entirely automatic and stimulus-driven. We will evaluate their claims of automaticity and contrast them with a few other studies that have suggested that orienting to unconscious cues proceeds in a manner that is contingent with the top-down goals of the individual. Resolving this debate has consequences for understanding the depths and the limits of unconscious processing. It has implications for general theories of attentional selection as well. In this review, we aim to provide the current status of research in this domain and point out open questions and future directions.

scholarly journals Capture by Context Elements, Not Attentional Suppression of Distractors, Explains the PD with Small Search Displays

2020 ◽  
Vol 32 (6) ◽  
pp. 1170-1183 ◽  
Author(s):  
Dirk Kerzel ◽  
Nicolas Burra
Keyword(s):  
Attentional Capture ◽  
Attentional Selection ◽  
Top Down ◽  
Voltage Difference ◽  
Context Element ◽  
Selection Of

Top–down control of attention allows us to resist attentional capture by salient stimuli that are irrelevant to our current goals. Recently, it was proposed that attentional suppression of salient distractors contributes to top–down control by biasing attention away from the distractor. With small search displays, attentional suppression of salient distractors may even result in reduced RTs on distractor-present trials. In support of attentional suppression, electrophysiological measures revealed a positivity between 200 and 300 msec contralateral to the distractor, which has been referred to as distractor positivity (PD). We reexamined distractor benefits with small search displays and found that the positivity to the distractor was followed by a negativity to the distractor. The negativity, referred to as N2pc, is considered an index of attentional selection of the contralateral element. Thus, attentional suppression of the distractor (PD) preceded attentional capture (N2pc) by the distractor, which is at odds with the idea that attentional suppression avoids attentional capture by the distractor. Instead, we suggest that the initial “PD” is not a positivity to the distractor but rather a negativity (N2pc) to the contralateral context element, suggesting that, initially, the context captured attention. Subsequently, the distractor was selected because, paradoxically, participants searched all lateral target positions (even when irrelevant) before they examined the vertical positions. Consistent with this idea, search times were shorter for lateral than vertical targets. In summary, the early voltage difference in small search displays is unrelated to distractor suppression but may reflect capture by the context.

scholarly journals Reading as Statistical Learning

2018 ◽  
Vol 49 (3S) ◽  
pp. 634-643 ◽  
Author(s):  
Joanne Arciuli
Keyword(s):  
Implicit Learning ◽  
Statistical Learning ◽  
Literacy Instruction ◽  
Word Reading ◽  
Implicit Methods ◽  
New Techniques ◽  
Linguistic Research ◽  
Statistical Regularities ◽  
Basic And Applied Research ◽  
Deep Orthography

Purpose The purpose of this tutorial is to explain how learning to read can be thought of as learning statistical regularities and to demonstrate why this is relevant for theory, modeling, and practice. This tutorial also shows how triangulation of methods and cross-linguistic research can be used to gain insight. Method The impossibility of conveying explicitly all of the regularities that children need to acquire in a deep orthography, such as English, can be demonstrated by examining lesser-known probabilistic orthographic cues to lexical stress. Detection of these kinds of cues likely occurs via a type of implicit learning known as statistical learning (SL). The first part of the tutorial focuses on these points. Next, studies exploring how individual differences in the capacity for SL relate to variability in word reading accuracy in the general population are discussed. A brief overview of research linking impaired SL and dyslexia is also provided. The final part of the tutorial focuses on how we might supplement explicit literacy instruction with implicit learning methods and emphasizes the value of testing the efficacy of new techniques in the classroom. The basic and applied research reviewed here includes corpus analyses, behavioral testing, computational modeling, and classroom-based research. Although some of these methods are not commonly used in clinical research, the depth and breadth of this body of work provide a compelling case for why reading can be thought of as SL and how this view can inform practice. Conclusion Implicit methods that draw on the principles of SL can supplement the much-needed explicit instruction that helps children learn to read. This synergy of methods has the potential to spark innovative practices in literacy instruction and remediation provided by educators and clinicians to support typical learners and those with developmental disabilities.

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