The development of attentional control mechanisms in multisensory environments

Turoman, Nora; Tivadar, Ruxandra I.; Retsa, Chrysa; Maillard, Anne; Scerif, Gaia; Matusz, Paweł J.

doi:10.1101/2020.06.23.166975

Cited by 8 publications

(18 citation statements)

References 84 publications

(194 reference statements)

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“…To summarise, in the real world, attention should be captured more strongly by stimuli that are unpredictable (Schroeder et al 2015), but also by those unknown or without a clear meaning. On the other hand, stimuli with high strong spatial and/or temporal alignment across senses (and so stronger bottom-up salience) may be more resistant to such goal-based attentional control (suppression), as we have shown here (multisensory enhancement of attentional capture; see also Santangelo & Spence 2007;Matusz & Eimer 2011;van der Burg et al 2011;Turoman et al 2021;Fleming et al 2020). As multisensory distractors captured attention more strongly even in current, context-rich settings, this confirms the importance of multisensory salience as a source of potential bottom-up attentional control in naturalistic environments (see SOMs for a short discussion of this replication).…”

Section: How We Pay Attention In Naturalistic Settingsmentioning

confidence: 53%

“…Like in the former study, each experimental trial consisted of the following sequence of arrays: base array (In Experiments 1 and 3: randomly varied between 100, 250, and 450ms; in Experiments 2 and 4: 450ms), followed by distractor array (50ms), followed by a fixation point (150ms), and finally a target array (50ms, see Figure 1A). However, compared to the original Matusz and Eimer (2011) paradigm, the number of elements was reduced from 6 to 4 and targets were reshaped to look like diamonds rather than rectangles, as Experiment 1 served as an adult control for a different, developmental study (reported in Turoman et al, 2020a, 2020b). Thus, the base array contained four differently coloured sets of closely aligned dots, each dot subtending 0.1° × 0.1° of visual angle.…”

Section: Methodsmentioning

confidence: 99%

“…Following Matusz and Eimer (2011), RT data were cleaned by discarding incorrect and missed trials, as well as RTs below 200ms and above 1000ms. Additionally, to enable more direct comparisons with the developmental study for which current Experiment 1 served as an adult control (Turoman et al, 2020a, 2020b), we have further removed trials with RTs outside 2.5SD of the individual mean RT. As a result, a total of 5% of trials across all experiments were removed.…”

Section: Methodsmentioning

confidence: 99%

“…We recently used this approach to better understand brain and cognitive mechanisms of attentional control. We revealed that distinct brain networks are active ~N2pc time-window during visual goal-based and multisensory bottom-up attention control (across the lifespan; Turoman et al, 2020a, 2020b). However, these reflect spatially-selective, lateralised brain mechanisms, partly captured by the N2pc (via the contra- and ipsilateral comparison).…”

Section: Introductionmentioning

confidence: 96%

“…Compared to the original paradigm, we made two additional changes, to enable the Task 1 to serve as an adult control study in a developmental study(Turoman et al, 2021). We reduced the number of elements in all arrays from 6 to 4, and targets were reshaped to look like diamonds rather than rectangles.…”

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Towards understanding how we pay attention in naturalistic visual search settings

Turoman

Tivadar

Retsa

et al. 2020

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Traditional research on attentional control has largely focused on single senses and the importance of one s behavioural goals in controlling attentional selection, thus limiting its generalizability to real-world contexts. These contexts are both inherently multisensory and contain regularities that also contribute to attentional control. To get a better understanding of how attention is controlled in the real world, we investigated how visual attentional capture was impacted by top-down goals (indexed by task-set contingent attentional capture) and the multisensory nature of stimuli, as well as top-down contextual factors such as semantic relationships and temporal predictability of stimulus onset. Participants performed a multisensory version of Folk et al. (1992) spatial cueing paradigm, while their 129-channel event-related potentials (ERPs) were recorded. Reaction-time spatial cueing served as a behavioural measure of attentional control, while the N2pc ERP component was analysed both canonically and using a multivariate electrical neuroimaging (EN) framework. Behaviourally, target-congruent colour distractors captured attention more strongly when they were simultaneous than semantically congruent (nontarget-congruent colour distractors failed to capture attention), with no behavioural evidence for context modulating multisensory enhancements of capture. However, our EN analyses revealed context-based influences on attention to both visual and multisensory distractors, on how strongly they activated brain networks and in the type of activated brain networks. In both cases, these context-driven brain response modulations occurred early on (long before the traditional N2pc time-window), with network-based modulations at app. 30ms post-distractor, followed by strength-based modulations at app. 100ms post-distractor. Our findings revealed that in naturalistic settings, meaning, next to predictions (spatial, temporal etc.) might be a second important source of contextual information utilised to facilitate goal-directed attention. Therein, attentional selection is controlled by an interaction of one s goals, stimulus perceptual (multisensory-driven) salience and an interaction of stimulus meaning and its predictability. Our study demonstrates how investigating more traditional, lab-studied control mechanisms and processes more typical for everyday life reveals a complex interplay between goal-, stimulus- and context-based processes in attentional control. As such, our findings call for a revision of traditional models of visual attentional control to account for the role of both contextual and multisensory control mechanisms.

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Section: How We Pay Attention In Naturalistic Settingsmentioning

confidence: 53%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

mentioning

confidence: 99%

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Towards understanding how we pay attention in naturalistic visual search settings

Turoman

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Retsa

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Context and attention control determine whether attending to competing information helps or hinders learning in school‐aged children

Markant

Amso

2021

WIRES Cognitive Science

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Attention control regulates efficient processing of goal‐relevant information by suppressing interference from irrelevant competing inputs while also flexibly allocating attention across relevant inputs according to task demands. Research has established that developing attention control skills promote effective learning by minimizing distractions from task‐irrelevant competing information. Additional research also suggests that competing contextual information can provide meaningful input for learning and should not always be ignored. Instead, attending to competing information that is relevant to task goals can facilitate and broaden the scope of children's learning. We review this past research examining effects of attending to task‐relevant and task‐irrelevant competing information on learning outcomes, focusing on relations between visual attention and learning in childhood. We then present a synthesis argument that complex interactions across learning goals, the contexts of learning environments and tasks, and developing attention control mechanisms will determine whether attending to competing information helps or hinders learning. This article is categorized under: Psychology > Attention Psychology > Learning Psychology > Development and Aging

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To Pay or Not to Pay Attention: Classifying and Interpreting Visual Selective Attention Frequency Features

Fanda

Cid

Matusz

et al. 2021

Explainable and Transparent AI and Multi-Agent Systems

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Selective attention is the ability to promote the processing of objects important for the accomplishment of our behavioral goals (target objects) over the objects not important to those goals (distractor objects). Previous investigations have shown that the mechanisms of selective attention contribute to enhancing perception in both simple daily tasks and more complex activities requiring learning new information. Recently, it has been verified that selective attention to target objects and distractor objects is separable in the frequency domain, using Logistic Regression (LR) and Support Vector Machines (SVMs) classification. However, discerning dynamics of target and distractor objects in the context of selective attention has not been accomplished yet. This paper extends the investigations on the possible classification and interpretation of distraction and intention solely relying on neural activity (frequency features). In particular, this paper (i) classifies distractor objects vs. target object replicating the LR classification of prior studies, extending the analysis by (ii) interpreting the coefficient weights relating to all features with a focus on N2PC features, and (iii) retrains an LR classifier with the features deemed important by the interpretation analysis. As a result of the interpretation methods, we have successfully decreased the feature size to 7.3 % of total features -i.e., from 19,072 to 1,386 features -while recording only a 0.04 loss in performance accuracy score -i.e., from 0.65 to 0.61. Additionally, the interpretation of the classifiers' coefficient weights unveiled new evidence regarding frequency which has been discussed along with the paper.

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The development of attentional control mechanisms in multisensory environments

Cited by 8 publications

References 84 publications

Towards understanding how we pay attention in naturalistic visual search settings

Towards understanding how we pay attention in naturalistic visual search settings

Context and attention control determine whether attending to competing information helps or hinders learning in school‐aged children

To Pay or Not to Pay Attention: Classifying and Interpreting Visual Selective Attention Frequency Features

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