Variations In Cognitive Load Research Articles

Topological data analysis suggests human brain network reconfiguration during the transition from resting state to cognitive load

BACKGROUND: Neural networks of the brain continually adapt to changing environmental demands. The network approach in neuroscience, which focuses on the analysis of structural and functional network characteristics related to cognitive functions, is a highly promising avenue for understanding the psychophysiological mechanisms underlying the adaptive dynamics of cognitive processes. AIM: We aimed to explore how the topological features of functional connectomes in the human brain are linked to different cognitive demands. The focus was on understanding the dynamic changes in brain networks during working memory tasks to identify network characteristics inherent to working memory. METHODS: We examined the topological characteristics of functional brain networks in the resting state and cognitive load provided by the execution of the Sternberg Item Recognition Paradigm based on electroencephalographic data. Electroencephalogram traces from 67 healthy adults were processed to estimate functional connectivity using the coherence method. We propose that the topological properties of functional networks in the human brain are distinct between cognitive load and resting state, with higher integration in the networks during cognitive load. RESULTS: The topological features of functional connectomes depend on the current state of cognitive processing and change with task-induced cognitive load variation. Moreover, functional connectivity during working memory tasks showed a faster emergence of homology group generators, supporting the idea of a relationship between the initial stages of working memory execution and an increase in faster network integration, with connector hubs playing a crucial role. CONCLUSION: Collected evidence suggest that cognitive states, particularly those related to working memory, are associated with distinct topological properties of functional brain networks, highlighting the importance of network dynamics in cognitive processing.

Blink-related EEG activity measures cognitive load during proactive and reactive driving

Assessing drivers’ cognitive load is crucial for driving safety in challenging situations. This research employed the occurrence of drivers’ natural eye blinks as cues in continuously recorded EEG data to assess the cognitive workload while reactive or proactive driving. Twenty-eight participants performed either a lane-keeping task with varying levels of crosswind (reactive) or curve road (proactive). The blink event-related potentials (bERPs) and spectral perturbations (bERSPs) were analyzed to assess cognitive load variations. The study found that task load during reactive driving did not significantly impact bERPs or bERSPs, possibly due to enduring alertness for vehicle control. The proactive driving revealed significant differences in the occipital N1 component with task load, indicating the necessity to adapt the attentional resources allocation based on road demands. Also, increased steering complexity led to decreased frontal N2, parietal P3, occipital P2 amplitudes, and alpha power, requiring more cognitive resources for processing relevant information. Interestingly, the proactive and reactive driving scenarios demonstrated a significant interaction at the parietal P2 and occipital N1 for three difficulty levels. The study reveals that EEG measures related to natural eye blink behavior provide insights into the effect of cognitive load on different driving tasks, with implications for driver safety.

Comparative analysis of multifaceted neural effects associated with varying endogenous cognitive load

Contemporary neuroscience has firmly established that mental state variation concurs with changes in neural dynamic activity in a complex way that a one-to-one mapping cannot describe. To explore the scenario of the multifaceted changes in neural dynamics associated with simple mental state variation, we took cognitive load – a common cognitive manipulation in psychology – as a venue to characterize how multiple neural dynamic features are simultaneously altered by the manipulation and how their sensitivity differs. Electroencephalogram was collected from 152 participants performing stimulus-free tasks with different demands. The results show that task demand alters wide-ranging neural dynamic features, including band-specific oscillations across broad frequency bands, scale-free dynamics, and cross-frequency phase-amplitude coupling. The scale-free dynamics outperformed others in indexing cognitive load variation. This study demonstrates a complex relationship between cognitive dynamics and neural dynamics, which points to a necessity to integrate multifaceted neural dynamic features when studying mind-brain relationship in the future.

Quantitative assessment of cognitive profile and brain asymmetry in the characterization of autism spectrum in children: A task-based EEG study.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by learning, attention, social, communication, and behavioral impairments. Each person with Autism has a different severity and level of brain functioning, ranging from high functioning (HF) to low functioning (LF), depending on their intellectual/developmental abilities. Identifying the level of functionality remains crucial in understanding the cognitive abilities of Autistic children. Assessment of EEG signals acquired during specific cognitive tasks is more appropriate in identifying brain functional and cognitive load variations. The spectral power of EEG sub-band frequency and parameters related to brain asymmetry has the potential to be employed as indices to characterize brain functioning. Thus, the objective of this work is to analyze the cognitive task-based electrophysiological variations in autistic and control groups, using EEG acquired during two well-defined protocols. Theta to Alpha ratio (TAR) and Theta to Beta ratio (TBR) of absolute powers of the respective sub-band frequencies have been estimated to quantify the cognitive load. The variations in interhemispheric cortical power measured by EEG were studied using the brain asymmetry index. For the arithmetic task, the TBR of the LF group was found to be considerably higher than the HF group. The findings reveal that the spectral powers of EEG sub-bands can be a key indicator in the assessment of high and low-functioning ASD to facilitate appropriate training strategies. Instead of depending solely on behavioral tests to diagnose autism, it could be a beneficial approach to use task-based EEG characteristics to differentiate between the LF and HF groups.

The Role of Cue Utilization and Cognitive Load in the Recognition of Phishing Emails.

Phishing emails represent a major threat to online information security. While the prevailing research is focused on users' susceptibility, few studies have considered the decision-making strategies that account for skilled detection. One relevant facet of decision-making is cue utilization, where users retrieve feature-event associations stored in long-term memory. High degrees of cue utilization help reduce the demands placed on working memory (i.e., cognitive load), and invariably improve decision performance (i.e., the information-reduction hypothesis in expert performance). The current study explored the effect of cue utilization and cognitive load when detecting phishing emails. A total of 50 undergraduate students completed: (1) a rail control task; (2) a phishing detection task; and (3) a survey of the cues used in detection. A cue utilization assessment battery (EXPERTise 2.0) then classified participants with either higher or lower cue utilization. As expected, higher cue utilization was associated with a greater likelihood of detecting phishing emails. However, variation in cognitive load had no effect on phishing detection, nor was there an interaction between cue utilization and cognitive load. Further, the findings revealed no significant difference in the types of cues used across cue utilization groups or performance levels. These findings have implications for our understanding of cognitive mechanisms that underpin the detection of phishing emails and the role of factors beyond the information-reduction hypothesis.

A Cognitive Workload Identification using EEG Power Spectrum

Now a days, Electroencephalography (EEG) is popular to monitor human’s cognitive workload. EEG signals are delicate to the variation in cognitive load in various fields including observing cognitive workload for the intricate environment of military chores. Earlier to acquire the EEG signals high-cost EEG systems were used which bounds their use but now a day’s low-cost headsets are available to capture EEG which makes it a promising set-up to measure cognitive workload. EEGs are initially preprocessed to reflect the artifacts present in it. After preprocessing, signals are ready for further processing. The power spectral density corresponds to the power distribution of EEG signal in the frequency domain which is used to assess the changes in the pattern of the brain. This paper discusses the present progress of research in cognitive workload identification and identifies the techniques associated with the cognitive workload. This proposed research gives the analysis of EEG signal power spectrum density (PSD) during resting state and cognitive workload activities of a human. With power spectral analysis of the EEG signal, seven statistical parameters have been calculated (minimum, maximum, mean, median, mode, standard deviation and range) have been calculated Analysis showed that the in cognitive workload, PSD has significantly changed if compared to the resting state.

The arithmetic complexity of online grocery shopping: the moderating role of product pictures

Purpose Online grocery shopping possesses characteristics that can make it more difficult than regular online shopping. There are numerous buying decisions to make each shopping session, there are large ranges of product types to choose from and there is varied arithmetical complexity. The purpose of this paper is to examine how such characteristics influence the attitude of consumers toward online grocery shopping websites. Design/methodology/approach The authors hypothesized that the product type (search or experience product), the task arithmetic complexity, and the attention and cognitive load associated with browsing through product pictures have an effect on the attitude of online shoppers toward these websites. To test the hypotheses, 31 subjects participated in a within-subject laboratory experiment. Findings The results suggest that visual attention to product pictures has a positive effect on the attitude of online shoppers toward a website when they are shopping for experience goods, but that it has a negative effect on their attitude toward a website when the task arithmetic complexity is greater. They also suggest that the cognitive load associated with browsing through product pictures has a negative effect on the attitude of online shoppers toward a website when they are shopping for experience goods, and that greater cognitive load variation has a positive effect on their attitude toward a website when arithmetic task complexity is greater. Practical implications When designing online grocery websites, providing clear single unit quantities with pictures corresponding to the sales unit could help establish a clear baseline on which consumers can work out their quantity requirements. For decisions involving experience goods, product pictures may act as an important complementary information source and may even be more diagnostic than text description. Originality/value Results reinforce the relevance of enriching the study of self-reported measures of the user experience on e-commerce sites with automatic measures.

Income Taxes and Risky Investment Decisions: An Experiment on Behavioral Tax Biases

We design a controlled experiment to examine investment behavior when a proportional income tax (with and without full loss offset) is introduced. Our design enables us to disentangle tax effects predicted by standard neoclassical theory and behavioral tax biases that cannot be predicted by this theory. We observe that investment behavior can be heavily distorted by behavioral tax biases and is consequently inconsistent with theoretical predictions. However, if we isolate these effects, we find support for the theory. We argue that the unpredicted behavior is a result of a tax-induced cognitive load variation.

Investigating the Neural Correlates of Emotion-Cognition Interaction Using an Affective Stroop Task.

The human brain has the capacity to integrate various sources of information and continuously adapts our behavior according to situational needs in order to allow a healthy functioning. Emotion–cognition interactions are a key example for such integrative processing. However, the neuronal correlates investigating the effects of emotion on cognition remain to be explored and replication studies are needed. Previous neuroimaging studies have indicated an involvement of emotion and cognition related brain structures including parietal and prefrontal cortices and limbic brain regions. Here, we employed whole brain event-related functional magnetic resonance imaging (fMRI) during an affective number Stroop task and aimed at replicating previous findings using an adaptation of an existing task design in 30 healthy young adults. The Stroop task is an indicator of cognitive control and enables the quantification of interference in relation to variations in cognitive load. By the use of emotional primes (negative/neutral) prior to Stroop task performance, an emotional variation is added as well. Behavioral in-scanner data showed that negative primes delayed and disrupted cognitive processing. Trials with high cognitive demand furthermore negatively influenced cognitive control mechanisms. Neuronally, the emotional primes consistently activated emotion-related brain regions (e.g., amygdala, insula, and prefrontal brain regions) while Stroop task performance lead to activations in cognition networks of the brain (prefrontal cortices, superior temporal lobe, and insula). When assessing the effect of emotion on cognition, increased cognitive demand led to decreases in neural activation in response to emotional stimuli (negative > neutral) within prefrontal cortex, amygdala, and insular cortex. Overall, these results suggest that emotional primes significantly impact cognitive performance and increasing cognitive demand leads to reduced neuronal activation in emotion related brain regions, and therefore support previous findings investigating emotion–cognition interaction in healthy adults. Moreover, emotion and cognition seem to be tightly related to each other, as indicated by shared neural networks involved in both of these processes. Emotion processing, cognitive control, and their interaction are crucial for healthy functioning and a lack thereof is related to psychiatric disorders such as, disruptive behavior disorders. Future studies may investigate the neural characteristics of children and adolescents with disruptive behavior disorders.

Pupillary transient responses to within-task cognitive load variation

Changes in physiological signals due to task evoked cognitive load have been reported extensively. However, pupil size based approaches for estimating cognitive load on a moment-to-moment basis are not as well understood as estimating cognitive load on a task-to-task basis, despite the appeal these approaches have for continuous load estimation. In particular, the pupillary transient response to instantaneous changes in induced load has not been experimentally quantified, and the within-task changes in pupil dilation have not been investigated in a manner that allows their consistency to be quantified with a view to biomedical system design. In this paper, a variation of the digit span task is developed which reliably induces rapid changes of cognitive load to generate task-evoked pupillary responses (TEPRs) associated with large, within-task load changes. Linear modelling and one-way ANOVA reveals that increasing the rate of cognitive loading, while keeping task demands constant, results in a steeper pupillary response. Instantaneous drops in cognitive load are shown to produce statistically significantly different transient pupillary responses relative to sustained load, and when characterised using an exponential decay response, the task-evoked pupillary response time constant is in the order of 1–5 s. Within-task test–retest analysis confirms the reliability of the moment-to-moment measurements. Based on these results, estimates of pupil diameter can be employed with considerably more confidence in moment-to-moment cognitive load estimation systems.

Beyond Subjective Self-Rating: EEG Signal Classification of Cognitive Workload

Cognitive workload is an important indicator of mental activity that has implications for human-computer interaction, biomedical and task analysis applications. Previously, subjective rating (self-assessment) has often been a preferred measure, due to its ease of use and relative sensitivity to the cognitive load variations. However, it can only be feasibly measured in a post-hoc manner with the user's cooperation, and is not available as an online, continuous measurement during the progress of the cognitive task. In this paper, we used a cognitive task inducing seven different levels of workload to investigate workload discrimination using electroencephalography (EEG) signals. The entropy, energy, and standard deviation of the wavelet coefficients extracted from the segmented EEGs were found to change very consistently in accordance with the induced load, yielding strong significance in statistical tests of ranking accuracy. High accuracy for subject-independent multichannel classification among seven load levels was achieved, across the twelve subjects studied. We compare these results with alternative measures such as performance, subjective ratings, and reaction time (response time) of the subjects and compare their reliability with the EEG-based method introduced. We also investigate test/re-test reliability of the recorded EEG signals to evaluate their stability over time. These findings bring the use of passive brain-computer interfaces (BCI) for continuous memory load measurement closer to reality, and suggest EEG as the preferred measure of working memory load.

Domain-general involvement of the posterior frontolateral cortex in time-based resource-sharing in working memory: An fMRI study

Working memory is often defined in cognitive psychology as a system devoted to the simultaneous processing and maintenance of information. In line with the time-based resource-sharing model of working memory (TBRS; Barrouillet and Camos, 2015; Barrouillet et al., 2004), there is accumulating evidence that, when memory items have to be maintained while performing a concurrent activity, memory performance depends on the cognitive load of this activity, independently of the domain involved. The present study used fMRI to identify regions in the brain that are sensitive to variations in cognitive load in a domain-general way. More precisely, we aimed at identifying brain areas that activate during maintenance of memory items as a direct function of the cognitive load induced by both verbal and spatial concurrent tasks. Results show that the right IFJ and bilateral SPL/IPS are the only areas showing an increased involvement as cognitive load increases and do so in a domain general manner. When correlating the fMRI signal with the approximated cognitive load as defined by the TBRS model, it was shown that the main focus of the cognitive load-related activation is located in the right IFJ. The present findings indicate that the IFJ makes domain-general contributions to time-based resource-sharing in working memory and allowed us to generate the novel hypothesis by which the IFJ might be the neural basis for the process of rapid switching. We argue that the IFJ might be a crucial part of a central attentional bottleneck in the brain because of its inability to upload more than one task rule at once.

Variation in dual-task performance reveals late initiation of speech planning in turn-taking

The smooth transitions between turns in natural conversation suggest that speakers often begin to plan their utterances while listening to their interlocutor. The presented study investigates whether this is indeed the case and, if so, when utterance planning begins. Two hypotheses were contrasted: that speakers begin to plan their turn as soon as possible (in our experiments less than a second after the onset of the interlocutor’s turn), or that they do so close to the end of the interlocutor’s turn. Turn-taking was combined with a finger tapping task to measure variations in cognitive load. We assumed that the onset of speech planning in addition to listening would be accompanied by deterioration in tapping performance. Two picture description experiments were conducted. In both experiments there were three conditions: (1) Tapping and Speaking, where participants tapped a complex pattern while taking over turns from a pre-recorded speaker, (2) Tapping and Listening, where participants carried out the tapping task while overhearing two pre-recorded speakers, and (3) Speaking Only, where participants took over turns as in the Tapping and Speaking condition but without tapping. The experiments differed in the amount of tapping training the participants received at the beginning of the session. In Experiment 2, the participants’ eye-movements were recorded in addition to their speech and tapping. Analyses of the participants’ tapping performance and eye movements showed that they initiated the cognitively demanding aspects of speech planning only shortly before the end of the turn of the preceding speaker. We argue that this is a smart planning strategy, which may be the speakers’ default in many everyday situations.

Multimodal behavior and interaction as indicators of cognitive load

High cognitive load arises from complex time and safety-critical tasks, for example, mapping out flight paths, monitoring traffic, or even managing nuclear reactors, causing stress, errors, and lowered performance. Over the last five years, our research has focused on using the multimodal interaction paradigm to detect fluctuations in cognitive load in user behavior during system interaction. Cognitive load variations have been found to impact interactive behavior: by monitoring variations in specific modal input features executed in tasks of varying complexity, we gain an understanding of the communicative changes that occur when cognitive load is high. So far, we have identified specific changes in: speech, namely acoustic, prosodic, and linguistic changes; interactive gesture; and digital pen input, both interactive and freeform. As ground-truth measurements, galvanic skin response, subjective, and performance ratings have been used to verify task complexity. The data suggest that it is feasible to use features extracted from behavioral changes in multiple modal inputs as indices of cognitive load. The speech-based indicators of load, based on data collected from user studies in a variety of domains, have shown considerable promise. Scenarios include single-user and team-based tasks; think-aloud and interactive speech; and single-word, reading, and conversational speech, among others. Pen-based cognitive load indices have also been tested with some success, specifically with pen-gesture, handwriting, and freeform pen input, including diagraming. After examining some of the properties of these measurements, we present a multimodal fusion model, which is illustrated with quantitative examples from a case study. The feasibility of employing user input and behavior patterns as indices of cognitive load is supported by experimental evidence. Moreover, symptomatic cues of cognitive load derived from user behavior such as acoustic speech signals, transcribed text, digital pen trajectories of handwriting, and shapes pen, can be supported by well-established theoretical frameworks, including O'Donnell and Eggemeier's workload measurement [1986] Sweller's Cognitive Load Theory [Chandler and Sweller 1991], and Baddeley's model of modal working memory [1992] as well as McKinstry et al.'s [2008] and Rosenbaum's [2005] action dynamics work. The benefit of using this approach to determine the user's cognitive load in real time is that the data can be collected implicitly that is, during day-to-day use of intelligent interactive systems, thus overcomes problems of intrusiveness and increases applicability in real-world environments, while adapting information selection and presentation in a dynamic computer interface with reference to load.

An algorithm for online detection of temporal changes in operator cognitive state using real-time psychophysiological data

We consider the problem of on-the-fly detection of temporal changes in the cognitive state of human subjects due to varying levels of difficulty of performed tasks using real-time EEG and EOG data. We construct the Cognitive State Indicator (CSI) as a function that projects the multidimensional EEG/EOG signals onto the interval [0,1] by maximizing the Kullback–Leibler distance between distributions of the signals, and whose values change continuously with variations in cognitive load. During offline testing (i.e., when evolution in time is disregarded) it was demonstrated that the CSI can serve as a statistically significant discriminator between states of different cognitive loads. In the online setting, a trend detection heuristic (TDH) has been proposed to detect real-time changes in the cognitive state by monitoring trends in the CSI. Our results support the application of the CSI and the TDH in future closed-loop control systems with human supervision.

Time effect of stimulus exposure in semantic verification task

The effect of stimulus exposure in semantic verification task within the framework of two levels of complexity of experimental context was examined in nine experiments. The complexity of experimental context was defined in terms of number of constitutive elements of a statement (subject, predicate and copula) which vary in the course of an experiment. Four experiments with different exposure duration have been performed within the framework of minimal complexity (where only one element of statement varies), while five experiments with different exposure duration have been conducted within the framework of medium complexity (where two elements of statement vary). Systematic manipulation of the two variables (i.e. complexity and exposure duration) provides variation of processing load in verification process, thus influencing the efficiency of working memory during the verification task. The obtained results indicate that the cognitive load variation (defined in terms of variation of exposure duration) affects processing of subject-predicate congruency, which is an obligatory step in statement verification. The shorter the exposure duration, the bigger the difference in processing latencies between ?congruent? and ?incongruent? statements. The effects are more pronounced for statements presented in the framework of medium complexity. Also, the reduction of exposure duration affects the error rate as well as processing of statement?s quality (i.e. affirmative vs. negative form of a statement).

Patterns of state anxiety in listening performance

Although public speaking state anxiety is understood to be a dynamic phenomenon of patterns which continually shift before, during, and after performance, researchers have not investigated patterns of anxiety in listening. Based upon current information processing perspectives, it is argued that state anxiety levels should differ concomitantly with variations in cognitive load during listening performance. In the present study, anxiety levels were observed to produce a within‐performance pattern that contrasts markedly with patterns observed for public speaking performance. Decreases in affect for the listening task were observed, along with an interesting pattern of correlations between anxiety and listening performance.