Patterns of eye blinks are modulated by auditory input in humans

Huber, S.; Martini, Markus; Sachse, Pierre

doi:10.1016/j.cognition.2021.104982

Cited by 16 publications

(19 citation statements)

References 87 publications

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“…Kobald et al (2019) found that people inhibit blinks when they expect relevant information to occur in a purely auditory task and compensate for this by an increased blink frequency after they heard the relevant information. Huber et al (2022) observed a similar pattern showing that blinks occurred less often before the occurrence of a tone within a tone sequence and more often succeeding the tone, especially when the timing of the tone occurrence was predictable. However, whether blink patterns reflect attention to continuous speech, is not known yet.…”

Section: Introductionmentioning

confidence: 66%

“…To test whether a participant's blink pattern was temporally associated with the pauses in the attended speech stream, we computed a blink distribution time-locked to the pause onsets of the attended speech stream. If there was no temporal association between the pauses and blinks, the blinks would occur at random relative to the pause onsets and the blink distribution would follow a uniform distribution (Figure 1B, right;Huber et al, 2022). However, if there was any temporal association between the pauses and blinks, the pause-locked blink distribution would deviate from a uniform distribution (Figure 1B, left).…”

Section: Hypothesis 1: Blink Distribution Time-locked To Pause Onsetsmentioning

confidence: 99%

“…The histograms consisted of 20 bins (100 ms duration each) spanning the range from -1 to 1 s relative to the pause onsets. In order to be able to statistically compare the pause-locked distribution to a uniform distribution, a minimum expected count of 5 blinks per bin has been recommended (Huber et al, 2022;Rolke & Gongora, 2021). Therefore, as mentioned earlier, we excluded 6 participants who had less than 100 blinks in the time range from -1 to 1 s relative to pause onset.…”

Section: Hypothesis 1: Blink Distribution Time-locked To Pause Onsetsmentioning

confidence: 99%

“…To perform a group level analysis while correcting for multiple comparisons, we used a binomial distribution. We calculated the probability that a certain number out of all 35 participants showed a significant result given the null hypothesis that there is no difference between the conditions (Huber et al, 2022). For this, we used the MATLAB function binopdf with an alpha error level of 0.05.…”

Section: Correction For Multiple Comparison and Group Level Statisticsmentioning

confidence: 99%

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Eye-blink patterns reflect attention to continuous speech

Holtze¹,

Rosenkranz²,

Bleichner³

et al. 2022

Preprint

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Eye blinks do not only serve to maintain the tear film of the eye but also seem to have a functional role in information processing. People tend to inhibit an eye blink when they expect relevant information to occur and blink more often when the information has been processed. Recent studies have shown that this relation also holds for auditory information processing. Yet so far, only artificial auditory stimuli like tones or controlled sentences were used. In the current study, we tested whether there is a temporal association between the pauses in a continuous speech stream and the listener’s eye blinks. To this end, we analyzed the eye blinks of 35 participants who were instructed to attended to one of two simultaneously presented audio books. We found that the blink patterns of 13 participants were coupled with the speech pauses in the attended speech stream. These participants blinked more often during the pauses in the attended speech stream. Contrary to our prediction, participants did not inhibit their blinking preceding a pause in the attended speech stream. As expected, there was no evidence that the listeners’ blink pattern was coupled to the pauses in the ignored speech stream. Thus, we conclude that the listeners’ blink patterns can reflect attention to continuous speech.

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Section: Introductionmentioning

confidence: 66%

Section: Hypothesis 1: Blink Distribution Time-locked To Pause Onsetsmentioning

confidence: 99%

Section: Hypothesis 1: Blink Distribution Time-locked To Pause Onsetsmentioning

confidence: 99%

Section: Correction For Multiple Comparison and Group Level Statisticsmentioning

confidence: 99%

See 2 more Smart Citations

Eye-blink patterns reflect attention to continuous speech

Holtze¹,

Rosenkranz²,

Bleichner³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Blink is also a measure to indicate cognitive load or attention (Chen & Epps, 2013;Huber et al, 2022;Stern et al, 1984) but is not used as often as pupillary response. One difference from pupillary response is that studies tried to identify the type of mental tasks when blink measures are relevant, for example, visual load and memory load tasks (Bentivoglio et al, 1997;Jessee, 2010;Stern et al, 1984), and blink was found to be inhibited more when the visual load is higher.…”

Section: Background and Related Workmentioning

confidence: 99%

Pupillometric and blink measures of diverse task loads: Implications for working memory models

Chen

Epps

Paas

2022

Brit J of Edu Psychol

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Background: Inconsistent observations of pupillary response and blink change in response to different specific tasks raise questions regarding the relationship between eye measures, task types and working memory (WM) models. On the one hand, studies have provided mixed evidence from eye measures about tasks: pupil size has mostly been reported to increase with increasing task demand while this expected change was not observed in some studies, and blink rate has exhibited different trends in different tasks. On the other hand, a WM model has been developed to integrate a component to reconcile recent findings that the human motor system plays an important role in cognition and learning. However, how different tasks correlate with WM components has not been experimentally examined using eye activity measurements. Aims: The current study uses a four-dimensional task load framework to bridge eye measures, task types and WM models. Sample: Twenty participants (10 males, 10 females; Age: M = 25.8, SD = 7.17) above 18 years old volunteered. All participants had normal or corrected to normal vision with contact lenses and had no eye diseases causing obvious excessive blinking. Methods: We examined the ability of pupil size and blink rate to index low and high levels of cognitive, perceptual, physical and communicative task load. A network of the four load types and WM components was built and analysed to verify the necessity of integrating a physical task-related component into the WM model. Results: Results demonstrate that pupil size can index cognitive load and communicative load but not perceptual or physical load. Blink rate can index the level of cognitive load

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