Statistical learning of frequent distractor locations in visual search involves regional signal suppression in early visual cortex

Zhang, Bei; Weidner, Ralph; Allenmark, Fredrik; Bertleff, Sabine; Fink, Gereon R.; Shi, Zhuanghua; Müller, Hermann J.

doi:10.1101/2021.04.16.440127

Cited by 5 publications

(8 citation statements)

References 70 publications

(60 reference statements)

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“…Valsecchi & Turatto, 2021; see also Bogaerts et al, 2022;Müller et al, 2009;Won et al, 2019). Further, while we have firmly established distractor-location learning as an essentially 'local' phenomenon, it remains to be seen whether it is to be classed as 'habituation' in the strict sense of "attenuated processing of previously encountered sensory information that did not elicit attentive processing or a behavioral response" (Won & Geng, 2020, p. 1994) -as has been argued by some; or, alternatively, whether it is top-down mediated in that it results from observers identifying a distractor that captured attention as a 'distractor', as a precursor to (actively) suppressing its location and so releasing attention to be reoriented elsewhere in the service of search -as we have argued (e.g, Zhang et al, 2021). For the remaining models, we considered the possibility that the amount of interference depends on how much information is gained by observing a distractor in a particular location/region, as formalized by Claude Shannon (1948).…”

Section: Discussionmentioning

confidence: 52%

“…The stimuli were essentially the same as those we had used in several previous studies (e.g., Sauter et al, 2018Sauter et al, , 2019Sauter et al, , 2020Zhang et al, 2021). The search displays consisted of 37 gray bars, which, except for one central bar, were arranged around three concentric circles (2°, 4°, and 6°of visual angle in radius), presented on a black background (see Figure 2).…”

Section: Stimulimentioning

confidence: 99%

“…In Zhang et al's (2021) fMRI study, we used (in one condition) essentially the same stimuli as in the present experiments and a fixed distractor-probability ratio of 4:1 between the frequent and rare distractor regions. The results revealed distractor-generated signaling (indexed by the beta values) to be reduced in early visual-cortex areas -from V1 to V4 -for the frequent vs. the rare region, with the beta-values of distractors at (retinotopically mapped) locations in the frequent and, respectively, rare regions predicting the magnitude of distractor interference: the lower an individual's beta value, the lower the interference.…”

Section: Locus and Dynamics Of Distractor-location Learning (Zhang Et...mentioning

confidence: 99%

“…Critically, however, rejecting a distractor, and thus inhibiting its location from the priority map downwards, requires a (more or less explicit) process of identifying an attended distractor as an erroneously selected, task-irrelevant 'non-target' item. In the words of Zhang et al (2021), "'distractor'-location inhibition is top-down mediated" in that it is "tied to the status of distractors as 'distractors'" (p. 13) -and this would be at variance with a strict notion of 'local habituation'.…”

Section: Locus and Dynamics Of Distractor-location Learning (Zhang Et...mentioning

confidence: 99%

“…Accordingly, while both types of theory might converge on where, in the cognitive architecture, the learning is implemented, they differ radically with regard to the cognitive dynamics assumed to underlie the learning. Based on an fMRI study (Zhang et al, 2021; as well as drawing on ideas from the oculomotor-capture study of Sauter et al, 2020), we have recently advocated an -in Won and Geng's (2020) terms -active, 'top-down' view of statistical distractor-location probability learning, where the learning changes the responsivity of local feature-coding mechanisms in early visual cortex.…”

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confidence: 99%

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Learning to suppress likely distractor locations in visual search is driven by the local distractor frequency

Allenmark¹,

Zhang

Shi³

et al. 2022

Preprint

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Salient but task-irrelevant distractors interfere less with visual search when they appear in a display region where distractors have appeared more frequently in the past (‘distractor-location probability cueing’). This effect could reflect the (re-)distribution of a global, limited attentional ‘inhibition resource’. Accordingly, changing the frequency of distractor appearance in one display region should also affect the magnitude of interference generated by distractors in a different region. Alternatively, distractor-location learning may reflect a local response (e.g., ‘habituation’) to distractors occurring at a particular location. In this case, the local distractor frequency in one display region should not affect distractor interference in a different region. To decide between these alternatives, we conducted three experiments in which participants searched for an orientation-defined target while ignoring a more salient orientation distractor that occurred more often in one vs. another display region. Experiment 1 varied the ratio of distractors appearing in the frequent vs. rare regions (60/40–90/10), with a fixed global distractor frequency. The results revealed the cueing effect to increase with increasing probability ratio. In Experiments 2 and 3, one (‘test’) region was assigned the same local distractor frequency as in one of the conditions of Experiment 1, but a different frequency in the other region – dissociating local from global distractor frequency. Together, the three experiments showed that distractor interference in the test region was not significantly influenced by the frequency in the other region, consistent with purely local learning. We discuss the implications for theories of statistical distractor-location learning.Public Significance StatementWe are frequently distracted by salient visual stimuli which are irrelevant to the task at hand. Previous studies have shown that ‘knowledge’ of the location(s) where a distractor is most likely to occur helps the observer to mitigate distraction. In this study we compared different theories of how the frequency and spatial distribution of distractor occurrence in different locations could influence the ability to avoid distraction. The results favored a local learning account: the ability to avoid distraction by distractors occuring in a particular spatial region is primarily influenced by how often distractors have occurred in that region.

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Section: Discussionmentioning

confidence: 52%

Section: Stimulimentioning

confidence: 99%

Section: Locus and Dynamics Of Distractor-location Learning (Zhang Et...mentioning

confidence: 99%

Section: Locus and Dynamics Of Distractor-location Learning (Zhang Et...mentioning

confidence: 99%

mentioning

confidence: 99%

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Learning to suppress likely distractor locations in visual search is driven by the local distractor frequency

Allenmark¹,

Zhang

Shi³

et al. 2022

Preprint

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Asymmetric learning of dynamic spatial regularities in visual search: facilitation of anticipated target locations, no suppression of predictable distractor locations

Yu¹,

Allenmark²,

Müller³

et al. 2022

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Static statistical regularities in the placement of targets and salient distractors within the search display can be learned and used to optimize attentional guidance. Whether statistical learning also extends to dynamic regularities governing the placement of targets and distractors on successive trials has been less investigated. Here, we applied the same dynamic cross-trial regularity (one-step shift of the critical item in clock-/counterclockwise direction) either to the target or a distractor, and additionally varied whether the distractor was defined in a different (color) or the same dimension (shape) as the target. We found robust learning of the predicted target location: processing of the target at this (vs. a random) location was facilitated. But we found no evidence of proactive suppression of the predictable distractor location. Facilitation of the anticipated target location was associated with explicit awareness of the dynamic regularity, whereas participants showed no awareness of the distractor regularity. We propose that this asymmetry arises because, owing to the target’s central role in the task set, its location is explicitly encoded in working memory, enabling the learning of dynamic regularities. In contrast, the distractor is not explicitly encoded; so, statistical learning of distractor locations is limited to static regularities.Public significance statementCan we learn the cross-trial dynamic regularity of a target or a task-irrelevant salient distractor (e.g., one-step shift of the critical item in clock-/counterclockwise direction) to boost search performance? The present study found robust learning of the predicted target location, but no evidence of proactive suppression of the predictable distractor location. Facilitation of the anticipated target location was associated with explicit awareness of the dynamic regularity. This asymmetry highlights the important role of the target-centered task set in the learning of dynamic regularities.

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Statistical Learning of Distractor Suppression Down-regulates Pre-Stimulus Neural Excitability in Early Visual Cortex

Ferrante

Zhigalov

Hickey

et al. 2022

Preprint

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Visual attention is highly influenced by past experiences. Recent behavioral research has shown that expectations about the spatial location of distractors within a search array are implicitly learned, with expected distractors becoming less interfering. Little is known about the neural mechanism supporting this form of statistical learning. Here we used magnetoencephalography (MEG) to measure human brain activity to test whether proactive mechanisms are involved in the statistical learning of distractor locations. Specifically, we used a new technique called rapid invisible frequency tagging (RIFT) to assess neural excitability in early visual cortex during statistical learning of distractor suppression, while concurrently investigating the modulation of posterior alpha-band activity (8-12 Hz). Male and female human participants performed a visual search task in which a target was occasionally presented alongside a color-singleton distractor. Unbeknown to the participants, the distracting stimuli were presented with different probabilities across the two hemifields. RIFT analysis showed that early visual cortex exhibited reduced neural excitability in the pre-stimulus interval at retinotopic locations associated with higher distractor probabilities. In contrast, we did not find any evidence of expectation-driven distractor suppression in alpha-band activity. These findings indicate that proactive mechanisms of attention are involved in predictive distractor suppression and that these mechanisms are associated with altered neural excitability in early visual cortex. Moreover, our findings indicate that RIFT and alpha-band activity might subtend different and possibly independent attentional mechanisms.

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Statistical learning of frequent distractor locations in visual search involves regional signal suppression in early visual cortex

Cited by 5 publications

References 70 publications

Learning to suppress likely distractor locations in visual search is driven by the local distractor frequency

Learning to suppress likely distractor locations in visual search is driven by the local distractor frequency

Asymmetric learning of dynamic spatial regularities in visual search: facilitation of anticipated target locations, no suppression of predictable distractor locations

Statistical Learning of Distractor Suppression Down-regulates Pre-Stimulus Neural Excitability in Early Visual Cortex

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