Blinks of the eye predict blinks of the mind

Leiden University, Cognitive Psychology Unit & Leiden Institute for Brain and Cognition, Leiden, The Netherlands.
Neuropsychologia (Impact Factor: 3.3). 12/2008; 46(13):3179-83. DOI: 10.1016/j.neuropsychologia.2008.07.006
Source: PubMed


The Attentional Blink (AB)--a deficit in reporting the second of two target stimuli presented in close succession in a rapid sequence of distracters--has been related to individual processing limitations of working memory. Given the known role of dopamine (DA) in working memory processes, the present experiment tested the hypothesis that DA, and in particular the DA/D1 subsystem, plays a role in the AB. We present evidence that the spontaneous eyeblink rate (EBR), a functional marker of central dopaminergic function, reliably predicts the size of AB. Thus, in line with our hypothesis, these data point to a modulatory role for DA in the AB.

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Available from: Heleen Slagter, Oct 02, 2015
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    • "In [24], Slagter et al hypothesised that striatal dopamine level determines AB size in a U-shaped relationship, with high levels causing distractibility and low levels causing impaired updating. The same group proposed a psychophysiological method for detection of AB size by eye blink rate, based on the link between blink rate and dopamine [13] [7]. "
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    ABSTRACT: The psychophysiological method can be used to detect some simple cognitive states such as arousal, attentiveness, or mental workload. This approach can be especially interesting when cognition has some productive purpose, as in knowledge work, and tends to be related to human-computer interaction (HCI). However more interesting for applied purposes are acts of coordinated high-level cognition. High- level (or higher-order) cognition (HLC) is typically associated with decision making, problem solving, and executive control of cognition and action. Further, an intuitive approach for assessing whether someone is engaged in HLC is to measure their performance of a known task. Given this, it is reasonable to define high-performance cognition (HPC) as HLC under some performance restriction, such as real-time pressure or expert skill level. Such states are also interesting for HCI in work, and their detection represents an ambitious aim for using the psychophysiological method. We report a brief review of the literature on the topic.
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    • "Pivik and Dykman (2004) showed that endogenous blinks are modulated by different types of stimulus presentation, the amount of cognitive load and response requirements. Another evidence for the interrelation of eye blinks and attention is that the size of the attentional blink can be predicted from the spontaneous eye blink rate (Colzato et al., 2008). "
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    ABSTRACT: In applied contexts, psychophysiological measures have a long tradition to evaluate the user state. EEG correlates that indicate mechanisms of information processing, however, are hardly accessible since discrete time stamps that are necessary for this approach are commonly not available in natural situations. However, eye blinks may close this gap. Eye blinks are assumed to mark distinct points in information processing, necessary to segment the incoming data stream. By using mobile EEG in a simulated working situation we demonstrate that eye-blink-related potentials provide reliable information about cognitive processing in distinct working environments. During cognitive tasks, an increase in the fronto-central N2 component as well as evoked theta activity can be shown, both indices of enhanced cognitive control. The posterior P3 is reduced during physical tasks (sorting of boxes), probably reflecting the more continuous nature of this task. The data are discussed within a model of dopaminergic modulation of blink activity that involves both task specific aspects like executive control and modulating influences of motivation or fatigue.
    International journal of psychophysiology: official journal of the International Organization of Psychophysiology 10/2013; 91(1). DOI:10.1016/j.ijpsycho.2013.10.006 · 2.88 Impact Factor
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    • "The second aim of the current study was to replicate a previously reported association between sEBR, a marker of striatal dopaminergic functioning [25], and AB magnitude [22], and extend this finding by also examining the relationship between sEBR and AB recovery. One subject had to be excluded from the analyses concerning sEBR due to very noisy sEBR recordings, which prevented clear identification of eye blinks. "
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    ABSTRACT: The attentional blink (AB) refers to an impairment in detecting the second of two target stimuli presented in close succession in a rapid stream of distractors. Recent studies indicate that the AB results, in part, from distractor suppression mechanisms, that may be mediated by striatal dopamine. Yet, it is currently unclear how distractor suppression ability may contribute to the AB. Here, we examined whether distractor suppression ability is predictive of an individual's AB depth and/or recovery. In addition, we investigated the relationship between individual spontaneous eye blink rate (sEBR), a marker of striatal dopaminergic functioning, and AB performance. Subjects were presented with rapid streams of letters containing white distractors, a red T1 and a green T2. T2 was presented either at Lag2, Lag4 or Lag10, and preceded by a distractor that shared the same identity as T2 (T2 primed) or not (T2 not primed). Replicating previous work [1], we found that slow AB recovery (poor T2 performance in Lag4 vs. Lag10) was associated with a failure to inhibit distractors, as indexed by greater positive priming. However, no relationship was observed between a subject's ability to suppress distractors and AB depth (Lag10 vs. Lag2). Moreover, no relationship between sEBR and AB performance was observed. These results indicate that a failure to inhibit distracting information impairs AB recovery, possibly by interfering with target encoding in working memory - but does not affect AB magnitude. The absence of a relationship between individual sEBR and AB performance may be explained by task specifics.
    PLoS ONE 05/2013; 8(5):e64681. DOI:10.1371/journal.pone.0064681 · 3.23 Impact Factor
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