Figure - available from: Frontiers in Human Neuroscience
This content is subject to copyright.
Source publication
Cognitive performance is defined as the ability to utilize knowledge, attention, memory, and working memory. In this study, we briefly discuss various markers that have been proposed to predict cognitive performance. Next, we develop a novel approach to characterize cognitive performance by analyzing eye-blink rate variability dynamics. Our finding...
Citations
... Considering the potential reactivity that can be engendered through the deployment of the think-aloud method in metareasoning research, the question arises as to whether alternative and less reactive techniques are available to detect fluctuating states of uncertainty during ongoing reasoning. Some possible options that come to mind include the use of eye-gaze tracking (e.g., Ball 2013), pupillometry (e.g., Mathôt and Vilotijević 2022) and eye-blink rate (Paprocki and Lenskiy 2017), as well as physiological measures, such as skin conductance (Figner et al. 2019) and heart-rate variability (Forte et al. 2019). The challenge with these methods, however, is that they have not traditionally been used to pinpoint states of uncertainty during task performance, instead being more closely associated with measures of fluctuating cognitive workload, arousal, stress and fatigue. ...
Metareasoning refers to processes that monitor and control ongoing thinking and reasoning. The "metareasoning framework" that was established in the literature in 2017 has been useful in explaining how monitoring processes during reasoning are sensitive to an individual's fluctuating feelings of certainty and uncertainty. The framework was developed to capture metareasoning at an individual level. It does not capture metareasoning during collaborative activities. We argue this is significant, given the many domains in which team-based reasoning is critical, including design, innovation, process control, defence and security. Currently, there is no conceptual framework that addresses the nature of collaborative metareasoning in these kinds of domains. We advance a framework of collaborative metareasoning that develops an understanding of how teams respond to the demands and opportunities of the task at hand, as well as to the demands and opportunities afforded by interlocuters who have different perspectives, knowledge, skills and experiences. We point to the importance of a tripartite distinction between "self-monitoring", "other monitoring" and "joint monitoring". We also highlight a parallel distinction between "self-focused control", "other-focused control" and "joint control". In elaborating upon these distinctions, we discuss the prospects for developing a comprehensive collaborative metareasoning framework with a unique focus on language as a measure of both uncertainty and misalignment.
... In our investigation of blink duration, a notable contrast emerged between experienced and novice lifeguards, with the former displaying shorter blink durations. This observation holds significance, given that blink duration has been previously associated with fatigue (Maffei & Angrilli, 2018;Paprocki & Lenskiy, 2017). The implication is that 20 experienced lifeguards may be less susceptible to fatigue while engaged in the surveillance of a water-related environment. ...
The present study sought to examine the gaze behaviours exhibited by lifeguards with different levels of experience while performing a task focused on detecting drowning incidents across extended periods. The results indicated a gradual decline in detection performance over time, regardless of the lifeguards' levels of experience. Analysis of the participants' gaze behaviours unveiled that this decline was associated with alterations in both the number and duration of fixations. The results indicated that lifeguards with greater experience maintained higher levels of detection performance and fixation numbers for extended durations, while exhibiting consistent fixation durations throughout the task, in contrast to their less experienced counterparts. These findings offer initial indications that lifeguards with more experience may possess an attentional advantage during tasks requiring sustained vigilance.
... Moreover, the results obtained using the neural network model speak in favour of the hypothesis that originates from the quantum oscillator model and that suggests that the phase change in the response of the dynamical system has the effect of eye blinking, an action known to induce a reversal of the perceptual state of the Necker cube [11,14,48]. Although this hypothesis has not been verified yet, it is known that the dynamics of eye blinks can be studied using the methods developed to investigate highly nonlinear and chaotic processes [102][103][104]. Hence, since the neural network model employs data produced by a generator of truly random numbers, its predictions should be consistent with the dynamics of the eye blink [84,105]. ...
Ambiguous optical illusions have been a paradigmatic object of fascination, research and inspiration in arts, psychology and video games. However, accurate computational models of perception of ambiguous figures have been elusive. In this paper, we design and train a deep neural network model to simulate human perception of the Necker cube, an ambiguous drawing with several alternating possible interpretations. Defining the weights of the neural network connection using a quantum generator of truly random numbers, in agreement with the emerging concepts of quantum artificial intelligence and quantum cognition, we reveal that the actual perceptual state of the Necker cube is a qubit-like superposition of the two fundamental perceptual states predicted by classical theories. Our results finds applications in video games and virtual reality systems employed for training of astronauts and operators of unmanned aerial vehicles. They are also useful for researchers working in the fields of machine learning and vision, psychology of perception and quantum–mechanical models of human mind and decision making.
... Taking an J o u r n a l P r e -p r o o f average, as done in the current study, would not detect such a change. Paprocki & Lenskiy (2017) argue for the use of blink rate variability in cognition research, as the temporal distribution of blinks distinguish spontaneous eye blinks from reflexive or voluntary eye blinks. Blink rate variability has been successfully trialled as a measure of cognitive load and performance in a limited number of human studies (Gebrehiwot et al., 2016;Paprocki & Lenskiy, 2017). ...
... Paprocki & Lenskiy (2017) argue for the use of blink rate variability in cognition research, as the temporal distribution of blinks distinguish spontaneous eye blinks from reflexive or voluntary eye blinks. Blink rate variability has been successfully trialled as a measure of cognitive load and performance in a limited number of human studies (Gebrehiwot et al., 2016;Paprocki & Lenskiy, 2017). Blink rate variability was higher during a memory recall task than at baseline in humans but lower during a reading task than at baseline . ...
... Blink rate variability was higher during a memory recall task than at baseline in humans but lower during a reading task than at baseline . This suggests that blink rate variability is a more sensitive measure of cognitive performance than blink rate average and that blink rate variability may be applied in scenarios where blink rate alone proves not to be a useful measure (Paprocki & Lenskiy, 2017). However, this is highly time-consuming to achieve using manual counts and the development of automated blink detection is warranted for this reason. ...
... After blinking, the brain experiences an upsurge of visual information when the individuals open their eyes, leading to visual processing-related brain activity. However, previous research linking eye blinks to cognitive load has primarily concentrated on blink features such as eye movements and blink rate variability [12], [13], [14], with less emphasis on brain activity associated with eye blinks. To the best of our knowledge, only a handful of studies have investigated the neuronal processes connected to eye blink events [3], [8], [15], and none have yet investigated the neural and blink patterns involved in decoding cognitive states that precede and follow eye blinks. ...
Accurately evaluating cognitive load during work-related tasks in complex real-world environments is challenging, leading researchers to investigate the use of eye blinking as a fundamental pacing mechanism for segmenting EEG data and understanding the neural mechanisms associated with cognitive workload. Yet, little is known about the temporal dynamics of eye blinks and related visual processing in relation to the representation of task-specific information. Therefore, we analyzed EEG responses from two experiments involving simulated driving (re-active and pro-active) with three levels of task load for each, as well as operating a steam engine (active vs. passive), to decode the temporal dynamics of eye blink activity and the subsequent neural activity that follows blinking. As a result, we successfully decoded the binary representation of difficulty levels for pro-active driving using multivariate pattern analysis. However, the decoding level varied for different re-active driving conditions, which could be attributed to the required level of alertness. Furthermore, our study revealed that it was possible to decode both driving types as well as steam engine operating conditions, with the most significant decoding activity observed approximately 200 ms after a blink. Additionally, our findings suggest that eye blinks have considerable potential for decoding various cognitive states that may not be discernible through neural activity, particularly near the peak of the blink. The findings demonstrate the potential of blink-related measures alongside EEG data to decode cognitive states during complex tasks, with implications for improving evaluations of cognitive and behavioral states during tasks, such as driving and operating machinery.
... • Pupil dilation is a measurement of pupil diameter changes, which can be affected by ambient light, other light intensity changes, emotional change, cognitive load, and arousal. It can also be used to detect emotional change [35], [36], [37]. Pupillometry signals were recorded from both eyes. ...
Fluent human-robot collaboration requires a robot teammate to understand, learn, and adapt to the human's psycho-physiological state. Such collaborations require a computing system that monitors human physiological signals during human-robot collaboration (HRC) to quantitatively estimate a human's level of comfort, which we have termed in this research as comfortability index (CI) and uncomfortability index (unCI). Subjective metrics (surprise, anxiety, boredom, calmness, and comfortability) and physiological signals were collected during a human-robot collaboration experiment that varied robot behavior. The emotion circumplex model is adapted to calculate the CI from the participant's quantitative data as well as physiological data. To estimate CI/unCI from physiological signals, time features were extracted from electrocardiogram (ECG), galvanic skin response (GSR), and pupillometry signals. In this research, we successfully adapt the circumplex model to find the location (axis) of 'comfortability' and 'uncomfortability' on the circumplex model, and its location match with the closest emotions on the circumplex model. Finally, the study showed that the proposed approach can estimate human comfortability/uncomfortability from physiological signals.
... La literatura previa sugiere que el estrés psicológico podría deteriorar el funcionamiento cognitivo (Bedoya-Cardona & Vásquez-Caballero, 2019;Christensen et al., 2023;Khalili-Mahani et al., 2010;Law & Clow, 2020), entendiendo este último como la capacidad de utilizar y procesar información en distintas tareas a través de diferentes procesos cognitivos cuya base se encuentra en los sustratos cerebrales (Paprocki & Lenskiy, 2017;Veses et al., 2015). Específicamente, se conoce que la exposición continua al estrés de alta intensidad tiene repercusiones en la formación de recuerdos, recuperación de información implícita y el razonamiento complejo o flexible (Calvo & Gutiérrez-García, 2016); además, se asocia con una memoria de trabajo disminuida en tareas basadas en estimulación visoespacial (p. ...
Introducción. La escala de estrés percibido de Cohen (EEP) es el instrumento más utilizado a nivel mundial para medir estrés percibido, sin embargo, no existen análisis psicométricos en población general venezolana. Además, la teoría sugiere que el estrés podría tener efectos en el funcionamiento cognitivo a través de la sintomatología prefrontal y las quejas cognitivas. Objetivos. Analizar las propiedades psicométricas de la EEP y determinar la relación del estrés percibido y el funcionamiento cognitivo mediante los síntomas prefrontales y las quejas cognitivas. Sujetos y métodos. Se aplicaron las pruebas EEP, ISP-18, MFE-30 y MoCA a una muestra de 223 participantes (M= 33, D.E. = 12.82; 64% mujeres). Se ejecutaron análisis factoriales confirmatorios para determinar el ajuste de las versiones de la EEP y modelos de ecuaciones estructurales para analizar las relaciones entre variables. Resultados. La estructura de la EEP con mejor ajuste se compone de diez ítems y dos factores denominados incontrolabilidad percibida y eficacia percibida. Asimismo, el modelo estructural obtuvo un excelente ajuste a los datos y pone de manifiesto que el efecto del estrés sobre el funcionamiento cognitivo ocurre a través de los síntomas prefrontales y las quejas cognitivas (ß = -.303; EE = .123; p = .013). Discusión. La EEP-10 es una herramienta breve, valida y confiable para ser utilizada en contexto venezolano. Los efectos del estrés percibido sobre el funcionamiento cognitivo ocurren a través de una mediación serial que involucra la percepción de los individuos respecto a su funcionamiento ejecutivo y fallos cognitivos cotidianos.
... Blink-related oscillations (BRO) are a recently discovered neurophysiological response following spontaneous blinking, and are distinct from the oculomotor effects of blinking . Although blinking has not been previously associated with cognition, findings from behavioral studies have pointed to a relationship between blink rate and cognitive processes such as attention and memory processing (Paprocki and Lenskiy, 2017). In line with such evidence, neuroimaging studies have characterized the brain response to spontaneous blinking as increased activity in the delta frequency band (0.5-4 Hz) occurring after blinking, and found that it is associated with activity in bilateral precuneus (Liu et al., 2017). ...
Introduction
Repetitive subconcussive head impacts can lead to subtle neural changes and functional consequences on brain health. However, the objective assessment of these changes remains limited. Resting state blink-related oscillations (BROs), recently discovered neurological responses following spontaneous blinking, are explored in this study to evaluate changes in BRO responses in subconcussive head impacts.
Methods
We collected 5-min resting-state electroencephalography (EEG) data from two cohorts of collegiate athletes who were engaged in contact sports (SC) or non-contact sports (HC). Video recordings of all on-field activities were conducted to determine the number of head impacts during games and practices in the SC group.
Results
In both groups, we were able to detect a BRO response. Following one season of games and practice, we found a strong association between the number of head impacts sustained by the SC group and increases in delta and beta spectral power post-blink. There was also a significant difference between the two groups in the morphology of BRO responses, including decreased peak-to-peak amplitude of response over left parietal channels and differences in spectral power in delta and alpha frequency range post-blink.
Discussion
Our preliminary results suggest that the BRO response may be a useful biomarker for detecting subtle neural changes resulting from repetitive head impacts. The clinical utility of this biomarker will need to be validated through further research with larger sample sizes, involving both male and female participants, using a longitudinal design.
... Conversely, blink rate increases during the anticipation of a demanding task but decreases during the task itself. For instance, blink rate variability at rest can predict performance on a subsequent intelligence test (Paprocki & Lenskiy, 2017), and blink rate suppression occurs during demanding tasks in aviation (Wilson, 2002). A helpful mnemonic is that you tend to blink before and after you think (Siegle, Ichikawa, & Steinhauer, 2008 While the interpretation of gaze-independent metrics is less ambiguous than that of gaze-dependent metrics, there are two caveats worth mentioning. ...
Eye tracking is a powerful and sophisticated tool that provides an objective glimpse into the cognition of healthcare providers, patients, caregivers, and medical device users. Insights gleaned from eye tracking can be harnessed to better understand – and ultimately improve – the dynamics of healthcare, which quite literally has the potential to save lives. Nonetheless, the use of eye tracking within healthcare research and medical device testing remains in its infancy, which at least partly reflects the learning curve that it demands. As such, the central aim of this article is to provide an easily digestible primer for healthcare researchers and practitioners interested in first getting started with eye tracking. The discussion offers a general overview of how it works, device types and notable specifications, a taxonomy of common metrics, and various sensible best practices and recommendations tailored to the use of wearable eye trackers in a high-fidelity simulated use study context.
... Blink asymmetry: Studies have shown that the timing and duration of blinks can differ between the left and right eyes, which may be related to differences in motoneuron excitability [Kassem and Evinger 2006]. Blink rate variability: While the average blink rate is an important metric, the variability in blink rates can also provide valuable information about cognitive [Paprocki and Lenskiy 2017] and emotional states [Godin et al. 2015]. Blink patterns: In addition to duration and frequency, the pattern of blinks (e.g., regular, irregular, spontaneous) can also provide insights into cognitive processes [Jongkees and Colzato 2016] and attentional states [Lawson et al. 1998]. ...
