Threshold-varying integrate-and-fire model reproduces distributions of spontaneous blink intervals

Abstract

Spontaneous blinking is one of the most frequent human behaviours. While attentionally guided blinking may benefit human survival, the function of spontaneous frequent blinking in cognitive processes is poorly understood. To model human spontaneous blinking, we proposed a leaky integrate-and-fire model with a variable threshold which is assumed to represent physiological fluctuations during cognitive tasks. The proposed model is capable of reproducing bimodal, normal, and widespread peak-less distributions of inter-blink intervals as well as the more common popular positively skewed distributions. For bimodal distributions, the temporal positions of the two peaks depend on the baseline and the amplitude of the fluctuating threshold function. Parameters that reproduce experimentally derived bimodal distributions suggest that relatively slow oscillations (0.11–0.25 Hz) govern blink elicitations. The results also suggest that changes in blink rates would reflect fluctuations of threshold regulated by human internal states.

Rapid serial blinks: An index of temporally increased cognitive load

“…The range of the total number of blinks was 13 to 166 (SD = 41.69, Mean = 68.95, Median = 68) for the multiplication videos and that was 31 to 270 (SD = 60.01, Mean = 122.3, Median = 127.5) for the differentiation video. Likewise the previous studies [13–14], the total number of blinks in this paper demonstrated substantial individual differences. Moreover, simple counts of blinking could not detect the understanding level ( z = −1.73, p = 0.08 for the multiplication videos; z = −1.31, p = 0.19 the differentiation videos).…”

“…Human blinking characteristics have large interindividual variability [14, 17]. This has been a problematic fact for many researchers from early empirical studies [20] to current days [11,13]. The individual-based criteria proposed in this paper would be one of the resolutions for psychological phenomenon being problematic because of its large interindividual variability.…”

“…In natural viewing settings as well, temporal increases of blink rates would be an index to detect occurrences of increased cognitive load. If we can adopt Nomura et al’s computational approach [13], which can reproduce four major distributions of inter-blink intervals, assuming cognitive loads accumulate in each cognitive event. When the integrated cognitive loads reach a threshold, a blink generator in the nerve system immediately elicits a blink.…”

Highly reproducible eyeblink timing during formula car driving

“…First, there may be a relation to eye physiology. 44 For example, after prolonged eyeblink suppression, the probability of eyeblink generation could be higher owing to eye-surface irritation. As a result, eyeblinks might synchronize more after suppression than in our model.…”