“…A mixed-design ANOVA with the factors target condition and group revealed RTs to be significantly slower for targets appearing in the frequent versus the rare distractor region [1055 vs. 1035 ms; F (1, 42) = 4.67, p = 0.036, η p 2 = 0.10, BF incl = 1.67]—replicating previous studies using this paradigm (Wang and Theeuwes 2018a , b ; Zhang et al 2019 ). However, this target-position effect did not differ significantly between the ASD and TD groups [25 vs. 15 ms; F (1, 42) = 0.28, p = 0.60, BF incl = 0.34]—indicating that both groups acquired the same strategy of generally suppressing any singleton targets and distractors in the frequent distractor region, in line with suppression operating at the level of the search-guiding priority map (see Liesefeld and Müller 2019 , 2020 ; Sauter et al 2018 , 2019 ; 2020 ). Fig.…”
Individuals with Autism Spectrum Disorder (ASD) are thought to under-rely on prior knowledge in perceptual decision-making. This study examined whether this applies to decisions of attention allocation, of relevance for ‘predictive-coding’ accounts of ASD. In a visual search task, a salient but task-irrelevant distractor appeared with higher probability in one display half. Individuals with ASD learned to avoid ‘attentional capture’ by distractors in the probable region as effectively as control participants—indicating typical priors for deploying attention. However, capture by a ‘surprising’ distractor at an unlikely location led to greatly slowed identification of a subsequent target at that location—indicating that individuals with ASD attempt to control surprise (unexpected attentional capture) by over-regulating parameters in post-selective decision-making.
“…A mixed-design ANOVA with the factors target condition and group revealed RTs to be significantly slower for targets appearing in the frequent versus the rare distractor region [1055 vs. 1035 ms; F (1, 42) = 4.67, p = 0.036, η p 2 = 0.10, BF incl = 1.67]—replicating previous studies using this paradigm (Wang and Theeuwes 2018a , b ; Zhang et al 2019 ). However, this target-position effect did not differ significantly between the ASD and TD groups [25 vs. 15 ms; F (1, 42) = 0.28, p = 0.60, BF incl = 0.34]—indicating that both groups acquired the same strategy of generally suppressing any singleton targets and distractors in the frequent distractor region, in line with suppression operating at the level of the search-guiding priority map (see Liesefeld and Müller 2019 , 2020 ; Sauter et al 2018 , 2019 ; 2020 ). Fig.…”
Individuals with Autism Spectrum Disorder (ASD) are thought to under-rely on prior knowledge in perceptual decision-making. This study examined whether this applies to decisions of attention allocation, of relevance for ‘predictive-coding’ accounts of ASD. In a visual search task, a salient but task-irrelevant distractor appeared with higher probability in one display half. Individuals with ASD learned to avoid ‘attentional capture’ by distractors in the probable region as effectively as control participants—indicating typical priors for deploying attention. However, capture by a ‘surprising’ distractor at an unlikely location led to greatly slowed identification of a subsequent target at that location—indicating that individuals with ASD attempt to control surprise (unexpected attentional capture) by over-regulating parameters in post-selective decision-making.
“…The behavioural signature of statistical distractor-location learning has been well documented recently: RT interference is reduced for distractors occurring at frequent versus rare locations, and this is associated with reduced capture of the first saccade by distractors at frequent locations (Di Caro et al 2019;Wang et al 2019a;Sauter et al 2020). Together with an ERP component interpreted in terms of distractor suppression (Wang et al 2019b), this has been taken as evidence that observers learn to down-modulate the attentional priority signals (Itti and Koch 2001;Fecteau and Munoz 2006;Wolfe and Gray 2007;Kok et al 2012;Aitken et al 2020) generated by distractors at frequent locations, thus reducing their potential to capture attention and cause interference.…”
Section: Discussionmentioning
confidence: 92%
“…Further of note, the distractor type was manipulated as a within-subject factor in the present study: our participants had to learn the spatial distribution of one type of distractor first and then, after an unlearning phase, the opposite distribution with the other type of distractor. In previous studies (Sauter et al 2018(Sauter et al , 2019(Sauter et al , 2020, we had used a between-subject design to avoid carry-over of acquired suppression strategies from one distractor type to the other. Based on finding dissociative target-location effects between same-and different-dimension distractors, we had proposed that statistical learning of distractor locations typically involves different levels of priority computation:…”
Section: Designmentioning
confidence: 99%
“…This effect is attributable primarily to a proactive suppression of frequent distractor locations: oculomotor capture is less likely when distractors occur at frequent (vs. rare) locations (Di Caro et al 2019;Wang et al 2019a;Sauter et al 2020); and for frequent locations, an anticipatory suppressionrelated event-related component (P D ) is observed (Wang et al 2019b). However, how suppression of likely distractor locations is implemented is influenced by how distractors are defined relative to the target (Sauter et al 2018;Allenmark et al 2019;Failing et al 2019;Zhang et al 2019;Liesefeld and Müller 2020): if target and distractor are defined in the same dimension (e.g., target and distractor are both orientation-defined), suppression appears to work at a supra-dimensional level of 'attentionalpriority' computation, impacting both distractor and target signals -as compared to a level of dimension-specific 'feature-contrast' computation when they are defined in a different dimension (orientation-defined target, colour-defined distractor), in which case suppression typically impacts only distractor signals.…”
Observers can learn the locations where salient distractors appear frequently to reduce potential interference - an effect attributed to better suppression of distractors at frequent locations. But how distractor suppression is implemented in the visual cortex and frontoparietal attention networks remains unclear. We used fMRI and a regional distractor-location learning paradigm (Sauter et al. 2018, 2020) with two types of distractors defined in either the same (orientation) or a different (colour) dimension to the target to investigate this issue. fMRI results showed that BOLD signals in early visual cortex were significantly reduced for distractors (as well as targets) occurring at the frequent versus rare locations, mirroring behavioural patterns. This reduction was more robust with same-dimension distractors. Crucially, behavioural interference was correlated with distractor-evoked visual activity only for same- (but not different-) dimension distractors. Moreover, with different- (but not same-) dimension distractors, a colour-processing area within the fusiform gyrus was activated more when a colour distractor was present versus absent and with a distractor occurring at a rare versus frequent location. These results support statistical learning of frequent distractor locations involving regional suppression in the early visual cortex and point to differential neural mechanisms of distractor handling with different- versus same-dimension distractors.
“…Interestingly, while the effects of suppression history were visible for all kinds of distractors while distractor frequencies were manipulated, they were only found in the long term if this higher degree of perceptual/attentional processing had to be involved during the learning phase. The traces left in place during distractor filtering under these conditions are much more complex than those relative to their inhibition, and comprise a wealth of other types of processing, including a preliminary attentional selection based on their perceptual features 31 . Interestingly, recent studies have shown that in similar visual search tasks, if the perceptual features defining distractors are consistently used across trials (i.e., distractors appear more frequently in the same color or at the same location), the facilitation associated with their filtering is crucially linked with adaptations in the cortical activity of brain areas involved in visual processing as early as V1 59 , 60 .…”
Recent findings suggest that attentional and oculomotor control is heavily affected by past experience, giving rise to selection and suppression history effects, so that target selection is facilitated if they appear at frequently attended locations, and distractor filtering is facilitated at frequently ignored locations. While selection history effects once instantiated seem to be long-lasting, whether suppression history is similarly durable is still debated. We assessed the permanence of these effects in a unique experimental setting investigating eye-movements, where the locations associated with statistical unbalances were exclusively linked with either target selection or distractor suppression. Experiment 1 and 2 explored the survival of suppression history in the long and in the short term, respectively, revealing that its lingering traces are relatively short lived. Experiment 3 showed that in the very same experimental context, selection history effects were long lasting. These results seem to suggest that different mechanisms support the learning-induced plasticity triggered by selection and suppression history. Specifically, while selection history may depend on lasting changes within stored representations of the visual space, suppression history effects hinge instead on a functional plasticity which is transient in nature, and involves spatial representations which are constantly updated and adaptively sustain ongoing oculomotor control.
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