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Attentional blink

About: Attentional blink is a research topic. Over the lifetime, 1346 publications have been published within this topic receiving 53064 citations. The topic is also known as: Attentional blinks.


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Posted ContentDOI
24 Jul 2019-bioRxiv
TL;DR: A visual attentional blink (VAB) model (vabCPM) is constructed, comparing its performance predictions and network edges associated with successful and unsuccessful behavior to the saCPM, and it is concluded that these partially overlapping networks each have general attentional functions.
Abstract: Attention is a critical cognitive function, allowing humans to select, enhance, and sustain focus on information of behavioral relevance. Attention contains dissociable neural and psychological components. Nevertheless, some brain networks support multiple attentional functions. Connectome-based Predictive Models (CPM), which associate individual differences in task performance with functional connectivity patterns, provide a compelling example. A sustained attention network model (saCPM) successfully predicted performance for selective attention, inhibitory control, and reading recall tasks. Here we constructed a visual attentional blink (VAB) model (vabCPM), comparing its performance predictions and network edges associated with successful and unsuccessful behavior to the saCPM9s. In the VAB, attention devoted to a target often causes a subsequent item to be missed. Although frequently attributed to attentional limitations, VAB deficits may attenuate when participants are distracted or deploy attention diffusely. Participants (n=73; 24 males) underwent fMRI while performing the VAB task and while resting. Outside the scanner, they completed other cognitive tasks over several days. A vabCPM constructed from these data successfully predicted VAB performance. Strikingly, the network edges that predicted better VAB performance (positive edges) predicted worse selective and sustained attention performance, and vice versa. Predictions from the saCPM mirrored these results, with the network9s negative edges predicting better VAB performance. Furthermore, the vabCPM9s positive edges significantly overlapped with the saCPM9s negative edges, and vice versa. We conclude that these partially overlapping networks each have general attentional functions. They may indicate an individual9s propensity to diffusely deploy attention, predicting better performance for some tasks and worse for others.

14 citations

Journal ArticleDOI
TL;DR: Investigation of the effect of performance feedback on the modulation of the acoustic startle reflex in a Go/NoGo reaction time task found blink latency shortening was not affected, and blink magnitude modulation was smallest in the Easy/No Feedback and the Difficult/Feedback conditions.

14 citations

Journal ArticleDOI
TL;DR: Functional magnetic resonance imaging is used to disentangle the distinct neural substrates of T2 processing during this attentional blink phenomenon and provides the first evidence for effects of behavioral performance on hemodynamic responses in V1 under conditions of the attentional blinking.
Abstract: When two masked targets are presented in a rapid sequence, attentional limitations are reflected in reduced identification accuracy for the second target (T2). We used functional magnetic resonance imaging to disentangle the distinct neural substrates of T2 processing during this attentional blink phenomenon. Spatially separating the two targets allows the retinotopic localization of the different stimuli's encoding sites in primary visual cortex (V1) and thus enables activation elicited by each target to be differentially measured in V1. The encoding location of the second target mirrored T2 identification accuracy in a retinotopically specific manner. These results are the first evidence for effects of behavioral performance on hemodynamic responses in V1 under conditions of the attentional blink.

14 citations

Journal ArticleDOI
14 Dec 2015-PLOS ONE
TL;DR: It is found that the timing of attention to targets may be more important than the amount of allocated attention in accounting for individual differences, and the dynamics of temporal attention in small versus large blinkers differ in a number of ways.
Abstract: Background Attention is restricted for the second of two targets when it is presented within 200-500 ms of the first target. This attentional blink (AB) phenomenon allows one to study the dynamics of temporal selective attention by varying the interval between the two targets (T1 and T2). Whereas the AB has long been considered as a robust and universal cognitive limitation, several studies have demonstrated that AB task performance greatly differs between individuals, with some individuals showing no AB whatsoever. Methodology/Principal Findings Here, we studied these individual differences in AB task performance in relation to differences in attentional timing. Furthermore, we investigated whether AB magnitude is predictive for the amount of attention allocated to T1. For both these purposes pupil dilation was measured, and analyzed with our recently developed deconvolution method. We found that the dynamics of temporal attention in small versus large blinkers differ in a number of ways. Individuals with a relatively small AB magnitude seem better able to preserve temporal order information. In addition, they are quicker to allocate attention to both T1 and T2 than large blinkers. Although a popular explanation of the AB is that it is caused by an unnecessary overinvestment of attention allocated to T1, a more complex picture emerged from our data, suggesting that this may depend on whether one is a small or a large blinker. Conclusion The use of pupil dilation deconvolution seems to be a powerful approach to study the temporal dynamics of attention, bringing us a step closer to understanding the elusive nature of the AB. We conclude that the timing of attention to targets may be more important than the amount of allocated attention in accounting for individual differences.

14 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202312
202266
202148
202043
201945
201840