The time course of cognitive control implementation

Janssens, Clio; Loof, Esther De; Verguts, Tom

doi:10.3758/s13423-015-0992-3

Cited by 16 publications

(13 citation statements)

References 40 publications

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“…Due to its non-immediate mode of operation (Seamans & Yang, 2004), dopaminergic effects would not be expected to arise instantaneously. Consistent with this, it has recently been discussed, how reward-cue information would take a couple of hundreds of milliseconds in order to take an effect (Chiew, Stanek, & Adcock, 2016; but see also Janssens, De Loof, Pourtois, & Verguts, 2016), as was possible in the present Experiment 2. In this context of task preparation, the absence of an interaction between reward and congruency in the present experiments is relevant (concerning behavioral results and the majority of pupillometry and EEG results).…”

Section: Transient Preparatory Effort Reflected In the Eegsupporting

confidence: 90%

Differential effects of sustained and transient effort triggered by reward – A combined EEG and pupillometry study

et al. 2019

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In instrumental task contexts, incentive manipulations such as posting reward on successful performance usually trigger increased effort, which is signified by effort markers like increased pupil size. Yet, it is not fully clear under which circumstances incentives really promote performance, and which role effort plays therein. In the present study, we compared two schemes of associating reward with a Flanker task, while simultaneously acquiring electroencephalography (EEG) and pupillometry data in order to explore the contribution of effort-related processes. In Experiment 1, reward was administered in a block-based fashion, with series of targets in pure reward and no-reward blocks. The results imply increased sustained effort in the reward blocks, as reflected in particular in sustained increased pupil size. Yet, this was not accompanied by a behavioral benefit, suggesting a failure of translating increased effort into a behavioral pay-off. In Experiment 2, we introduced trial-based cues in order to also promote transient preparatory effort application, which indeed led to a behavioral benefit. Again, we observed a sustained pupil-size increase, but also transient ones. Consistent with this, the EEG data of Experiment 2 indicated increased transient preparatory effort preceding target onset, as well as reward modulations of target processing that arose earlier than in Experiment 1. Jointly, our results indicate that incentive-triggered effort can operate on different time-scales, and that, at least for the current task, its transient (and largely preparatory) form is critical for achieving a behavioral benefit, which may relate to the temporal dynamics of the catecholaminergic systems.

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Section: Transient Preparatory Effort Reflected In the Eegsupporting

confidence: 90%

Differential effects of sustained and transient effort triggered by reward – A combined EEG and pupillometry study

et al. 2019

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“…At the end of each block, an empty square appeared and participants had to retrieve and insert the WM load using the numerical pad of the keyboard. The Baseline Task was implemented as a basic visual discrimination task (Janssens, De Loof, Pourtois, & Verguts, 2016) in which participants had to judge whether the largest opening in a geometric figure (a square, a diamond, or a circle) was on the top or in the bottom (see Fig. 1B).…”

Section: Towards a Novel Paradigmmentioning

confidence: 99%

Switching attention from internal to external information processing: A review of the literature and empirical support of the resource sharing account

et al. 2019

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Despite its everyday ubiquity, not much is currently known about cognitive processes involved in flexible shifts of attention between external and internal information. An important model in the task-switching literature, which can serve as a blueprint for attentional flexibility, states that switch costs correspond to the time needed for a serial control mechanism to reallocate a limited resource from the previous task context to the current one. To formulate predictions from this model when applied to a switch between perceptual attention (external component) and working memory (WM; internal component), we first need to determine whether a single, serial control mechanism is in place and, subsequently, whether a limited resource is shared between them. Following a review of the literature, we predicted that a between-domain switch cost should be observed, and its size should be either similar or reduced compared to the standard, within-domain, switch cost. These latter two predictions derive from a shared resource account between external and internal attention or partial independence among them, respectively. In a second phase, we put to the test these opposing predictions in four successive behavioral experiments by means of a new paradigm suited to compare directly between-(internal to external) and within-(external to external) domain switch costs. Across them, we demonstrated the existence of a reliable between-domain switch cost whose magnitude was similar to the within-domain one, thereby lending support to the resource-sharing account.

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“…Furthermore, with stronger control there are less errors, faster RTs, and smaller congruency effects. This occurs both proactively (e.g., because the reward at stake is higher or because the upcoming task is difficult; Janssens, De Loof, Pourtois, & Verguts, 2016;Vassena et al, 2014;Padmala & Pessoa, 2011) and reactively (e.g., in response to current trial processing difficulty or after an error or incongruent trial; Gratton, Coles, & Donchin, 1992).…”

Section: Introductionmentioning

confidence: 99%

Binding by Random Bursts: A Computational Model of Cognitive Control

Verguts

2017

Journal of Cognitive Neuroscience

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138

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Abstract■ A neural synchrony model of cognitive control is proposed. It construes cognitive control as a higher-level action to synchronize lower-level brain areas. Here, a controller prefrontal area (medial frontal cortex) can synchronize two cortical processing areas. The synchrony is achieved by a random theta frequency-locked neural burst sent to both areas. The choice of areas that receive this burst is determined by lateral frontal cortex. As a result of this synchrony, communication between the two areas becomes more efficient. The model is tested on the classical Stroop cognitive control task, and its operation is explored in several simulations. Both reactive and proactive controls are implemented via theta power modulation. Increasing theta power improves behavioral performance; furthermore, via theta-gamma phase-amplitude coupling, theta also increases gamma frequency power and synchrony in posterior processing areas. Thus, the model solves a central computational problem for cognitive control (how to allow rapid communication between arbitrary brain areas), while making rich contact with behavioral and neurophysiological data. ■

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The time course of cognitive control implementation

Cited by 16 publications

References 40 publications

Differential effects of sustained and transient effort triggered by reward – A combined EEG and pupillometry study

Differential effects of sustained and transient effort triggered by reward – A combined EEG and pupillometry study

Switching attention from internal to external information processing: A review of the literature and empirical support of the resource sharing account

Binding by Random Bursts: A Computational Model of Cognitive Control

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