Addressing disorientation in endoscopy: A study on mental rotation difficulty and its impact on cognitive load measured by pupillary response

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Recent evidence shows that physiological cues, such as pupil dilation (PD), heart rate (HR), skin conductivity (SC), and electroencephalography (EEG), can indicate cognitive load (CL) in users while performing tasks. This paper aims to investigate physiological (multimodal) measurement of CL in a Sternberg memory task as the difficulty level increases in both maintenance and probe phases. For this purpose, we designed a Sternberg memory test with four levels of difficulty determined by the number of letters in the words that need to be remembered. Our behavioral performance results show that the CL of the task is related to the number of letters in non-semantic words, which confirms that this task serves as an appropriate metric of CL (the task difficulty increases as the number of letters in words increases). We were interested in investigating the suitability of multimodal physiological measures as correlates of four CL levels for both the maintenance and probe phases in the Sternberg memory task. Our motivation was to: (1) design and create four levels of task difficulty with a gradual increase in CL rather than just high and low CL, (2) use the Sternberg test as our test bed, (3) explore both the maintenance and probe phases for measurement of CL, and (4) explore the correlation of physiological cues (PD, HR, SC, EEG) with CL in both phases. Testing with the system, we found that for both the maintenance and probe phases, there was a significant positive linear relationship between average baseline corrected PD and CL. We also observed that the average baseline corrected SC showed significant increases as the number of letters in the words increased for both the maintenance and probe phases. However, the HR analysis did not show any correlation with an increase in CL in either of the maintenance or probe phases. An additional analysis was conducted to investigate the correlation of these physiological signals for high (seven-letter words) versus low (four-letter words) CL loads. Our EEG analysis for the maintenance phase found significant positive linear relationships between the power spectral density (PSD) and CL for the upper alpha bands in the centrotemporal, frontal, and occipitoparietal regions of the brain and significant positive linear relationships between the PSD and CL for the lower alpha band in the frontal and occipitoparietal regions. However, our EEG analysis of the probe phase did not show any linear relationship between the PSD and CL in any region. These results suggest that PD, SC, and EEG could be used as suitable metrics for the measurement of cognitive load in Sternberg memory tasks. We discuss this, limitations of the study, and directions for future work.

Mental effort in adolescents with ADHD

Objectives:Speech understanding may be cognitively demanding, but it can be enhanced when semantically related text cues precede auditory sentences. The present study aimed to determine whether (a) providing text cues reduces pupil dilation, a measure of cognitive load, during listening to sentences, (b) repeating the sentences aloud affects recall accuracy and pupil dilation during recall of cue words, and (c) semantic relatedness between cues and sentences affects recall accuracy and pupil dilation during recall of cue words.Design:Sentence repetition following text cues and recall of the text cues were tested. Twenty-six participants (mean age, 22 years) with normal hearing listened to masked sentences. On each trial, a set of four-word cues was presented visually as text preceding the auditory presentation of a sentence whose meaning was either related or unrelated to the cues. On each trial, participants first read the cue words, then listened to a sentence. Following this they spoke aloud either the cue words or the sentence, according to instruction, and finally on all trials orally recalled the cues. Peak pupil dilation was measured throughout listening and recall on each trial. Additionally, participants completed a test measuring the ability to perceive degraded verbal text information and three working memory tests (a reading span test, a size-comparison span test, and a test of memory updating).Results:Cue words that were semantically related to the sentence facilitated sentence repetition but did not reduce pupil dilation. Recall was poorer and there were more intrusion errors when the cue words were related to the sentences. Recall was also poorer when sentences were repeated aloud. Both behavioral effects were associated with greater pupil dilation. Larger reading span capacity and smaller size-comparison span were associated with larger peak pupil dilation during listening. Furthermore, larger reading span and greater memory updating ability were both associated with better cue recall overall.Conclusions:Although sentence-related word cues facilitate sentence repetition, our results indicate that they do not reduce cognitive load during listening in noise with a concurrent memory load. As expected, higher working memory capacity was associated with better recall of the cues. Unexpectedly, however, semantic relatedness with the sentence reduced word cue recall accuracy and increased intrusion errors, suggesting an effect of semantic confusion. Further, speaking the sentence aloud also reduced word cue recall accuracy, probably due to articulatory suppression. Importantly, imposing a memory load during listening to sentences resulted in the absence of formerly established strong effects of speech intelligibility on the pupil dilation response. This nullified intelligibility effect demonstrates that the pupil dilation response to a cognitive (memory) task can completely overshadow the effect of perceptual factors on the pupil dilation response. This highlights the importance of taking cognitive task load into account during auditory testing.

Automated driving systems (ADSs) are becoming ubiquitous to reduce the workload of drivers and improve road safety. However, present-day ADS lacks accurate and effective driver monitoring systems. Driver monitoring systems use physiological measurements, such as pupil dilation, eye-gaze, and eye-blinks, in order to monitor the cognitive load experienced by the drivers. With advances in eye-tracking technology, pupil dilation is emerging as a reliable measure of cognitive load in ADS. However, pupil dilation as a measure of cognitive load suffers from many factors, such as confounding effects, noise, and personal attributes, to name a few. Hence, in order to improve cognitive load estimation in ADS, other noninvasive measures must be studied and incorporated. In this article, various eye-gaze metrics are studied and evaluated as a measure of cognitive load based on data collected from 16 drivers in a simulated driving scenario using a driving simulator.

This secondary analysis of an earlier eye-tracking experiment investigated how triangulating changes in pupil dilation with student-self reports can be used as a measure of cognitive load during instructional videos with complex chemical representations. We incorporated three signaling conditions, dynamic, static and no signals, into instructional videos to purposefully alter cognitive load. Our results indicate that self-reported extraneous cognitive load decreased for dynamic signals compared to static or no signals, while intrinsic cognitive load was not affected by the signaling condition. Analysis of pupil dilation show significantly larger pupils for dynamic signals as compared to the other two conditions, suggesting that when extraneous cognitive load decreased, students still engaged cognitively with the task. Correlation analyses between measures were only significant for pupil dilation and extraneous cognitive load, but not pupil dilation and intrinsic cognitive load. We argue that beneficial design choices such as dynamic signals lead to more working memory capacity that can be leveraged toward learning. These findings extend previous research by demonstrating the utility of triangulating self-report and psychophysiological measures of cognitive load and effort.

Children with reading difficulties (RD) share challenges in executive functions (EF). Neurobiological correlates provide evidence for EF challenges during reading among these readers, but an online cognitive load detection mechanism has yet to be developed. Nevertheless, eye-movement tracking can provide online data of reading patterns (pupil dilation, fixations) and, indeed, atypical eye-movement patterns of children with RD during reading have been documented. To identify eye-movement patterns related to increased cognitive load during reading in children with RD compared to typical readers, eye movements of 8-12-year-old English-speaking children were recorded during their reading of sentences with increasing difficulty (sentences that make sense, then sentences that do not make sense) and comparing incorrect and correct responses. Children with RD demonstrated greater pupil dilation when reading sentences that make sense than when reading sentences that do not make sense and also when reading incorrectly, compared to typical readers. Increased pupil dilation in children with RD when reading sentences correctly was positively correlated with phonological awareness capabilities. Higher phonological awareness and reading abilities were related to increased pupil dilation only in children with RD during correct reading, which is related to a heavier cognitive load. Results suggest that in addition to traditional findings of altered fixation patterns in children with RD, increased pupil dilation during reading may reflect EF challenges among this population. These findings can potentially be used to adapt online written materials for children with RD based on their fixation and pupil dilation patterns.

Users' cognitive load while interacting with a system is a valuable metric for evaluations in HCI. We encourage the analysis of eye movements as an unobtrusive and widely available way to measure cognitive load. In this paper, we report initial findings from a user study with 26 participants working on three visual search tasks that represent different levels of difficulty. Also, we linearly increased the cognitive demand while solving the tasks. This allowed us to analyze the reaction of individual eye movements to different levels of task difficulty. Our results show how pupil dilation, blink rate, and the number of fixations and saccades per second individually react to changes in cognitive activity. We discuss how these measurements could be combined in future work to allow for a comprehensive investigation of cognitive load in interactive settings.

We investigated sex differences in behavioral performance and cognitive load in chronometric mental rotation tasks with abstract and embodied figures. Eighty participants (44 females and 36 males) completed 126 items, which included cube figures, body postures, and human figures, which were all comparable in shape and color. Reaction time, accuracy, and cognitive load, measured by changes in pupil dilation, were analyzed. As a function of angular disparity, participants showed shorter reaction times and higher accuracy rates for embodied stimuli than cube figures. Changes in pupil dilation showed a similar pattern, indicating that mental rotation of embodied figures caused less cognitive load to solve the task. No sex differences appeared in any of the measurements.

New methods are constantly being developed to adapt cognitive load measurement to different contexts. However, research on middle childhood students' cognitive load measurement is rare. Research indicates that the three cognitive load dimensions (intrinsic, extraneous, and germane) can be measured well in adults and teenagers using differentiated subjective rating instruments. Moreover, digital ink recorded by smartpens could serve as an indicator for cognitive load in adults. With the present research, we aimed at investigating the relation between subjective cognitive load ratings, velocity and pressure measures recorded with a smartpen, and performance in standardized sketching tasks in middle childhood students. Thirty-six children (age 7-12) participated at the university's laboratory. The children performed two standardized sketching tasks, each in two versions. The induced intrinsic cognitive load or the extraneous cognitive load was varied between the versions. Digital ink was recorded while the children drew with a smartpen on real paper and after each task, they were asked to report their perceived intrinsic and extraneous cognitive load using a newly developed 5-item scale. Results indicated that cognitive load ratings as well as velocity and pressure measures were substantially related to the induced cognitive load and to performance in both sketching tasks. However, cognitive load ratings and smartpen measures were not substantially related. Both subjective rating and digital ink hold potential for cognitive load and performance measurement. However, it is questionable whether they measure the exact same constructs.

Automatic classification of eye activity for cognitive load measurement with emotion interference

We used visuomotor tracking as our motor task and studied how subjects learn to adjust for inversion of the relation between joystick movement and target movement. This task requires learning a novel sensorimotor transformation. We have measured tracking performance and pupil dilation simultaneously. We have used pupil dilation as a measure of cognitive load, since the diameter of the human pupil increases with task difficulty across a wide range of cognitive tasks. Subjects observed a target moving at constant velocity along a clockwise circular trajectory on a computer screen. Subjects held a joystick in their hand, and moved it so that a cursor tracked the target as closely as possible. 60 normal subjects participated in the experiment. During 6 blocks of learning, inversion-evoked tracking error and inversion-evoked pupil dilation both decreased significantly. This finding suggests increasing automatization of the to-be-learned sensorimotor transformation. Pupil measures were not correlated with tracking error on individual trials, suggesting that the inversion-evoked cognitive load reflects changes in motor task, and is not merely a response to high errors. Our results thus suggest a relatively direct physiological measure of the processes of motor-skill automatization.

We present a Virtual Reality (VR) game, PlayMeBack, to investigate cognitive load measurement in interactive VR environments using pupil dilation, Galvanic Skin Response (GSR), Electroencephalogram (EEG) and Heart Rate (HR). The user is shown different patterns of tiles lighting up and is asked to replay the pattern back pressing the tiles in the same sequence they lit up. The task difficulty depends on the length of the observed pattern (3-6 keys). This task is designed to explore the effect of cognitive load on physiological cues, and if pupil dilation, EEG, GSR and HR can be used as measures of cognitive load.

Context: In recent years, several studies explored different facets of the developers' cognitive load while executing tasks related to software engineering. Researchers have proposed and assessed different ways to measure developers' cognitive load at work and some studies have evaluated the interplay between developers' cognitive load and other attributes such as productivity and software quality. Problem: However, the body of knowledge about developers' cognitive load measurement is still dispersed. That hinders the effective use of developers' cognitive load measurements by industry practitioners and makes it difficult for researchers to build new scientific knowledge upon existing results. Objective: This work aims to pinpoint gaps providing a classification and a thematic analysis of studies on the measurement of cognitive load in the context of software engineering. Method: We carried out a Systematic Mapping Study (SMS) based on well-established guidelines to investigate nine research questions. In total, 33 articles (out of 2,612) were selected from 11 search engines after a careful filtering process. Results: The main findings are that (1) 55% of the studies adopted electroencephalogram (EEG) technology for monitoring the cognitive load; (2) 51% of the studies applied machine-learning classification algorithms for predicting cognitive load; and (3) 48% of the studies measured cognitive load in the context of programming tasks. Moreover, a taxonomy was derived from the answers of research questions. Conclusion: This SMS highlighted that the precision of machine learning techniques is low for realistic scenarios, despite the combination of a set of features related to developers' cognitive load used on these techniques. Thus, this gap makes the effective integration of the measure of developers' cognitive load in industry still a relevant challenge.

Pupillary assessment and computational modeling of the Stroop task in depression