Complex Cognitive Tasks Research Articles

Auditory training can improve working memory, attention, and communication in adverse conditions for adults with hearing loss.

Auditory training (AT) helps compensate for degradation in the auditory signal. A series of three high-quality training studies are discussed, which include, (i) a randomized controlled trial (RCT) of phoneme discrimination in quiet that trained adults with mild hearing loss (n = 44), (ii) a repeated measures study that trained phoneme discrimination in noise in hearing aid (HA) users (n = 30), and (iii) a double-blind RCT that directly trained working memory (WM) in HA users (n = 57). AT resulted in generalized improvements in measures of self-reported hearing, competing speech, and complex cognitive tasks that all index executive functions. This suggests that for AT related benefits, the development of complex cognitive skills may be more important than the refinement of sensory processing. Furthermore, outcome measures should be sensitive to the functional benefits of AT. For WM training, lack of far-transfer to untrained outcomes suggests no generalized benefits to real-world listening abilities. We propose that combined auditory-cognitive training approaches, where cognitive enhancement is embedded within auditory tasks, are most likely to offer generalized benefits to the real-world listening abilities of adults with hearing loss.

Cross-hemispheric collaboration and segregation associated with task difficulty as revealed by structural and functional connectivity.

Although it is known that brain regions in one hemisphere may interact very closely with their corresponding contralateral regions (collaboration) or operate relatively independent of them (segregation), the specific brain regions (where) and conditions (how) associated with collaboration or segregation are largely unknown. We investigated these issues using a split field-matching task in which participants matched the meaning of words or the visual features of faces presented to the same (unilateral) or to different (bilateral) visual fields. Matching difficulty was manipulated by varying the semantic similarity of words or the visual similarity of faces. We assessed the white matter using the fractional anisotropy (FA) measure provided by diffusion tensor imaging (DTI) and cross-hemispheric communication in terms of fMRI-based connectivity between homotopic pairs of cortical regions. For both perceptual and semantic matching, bilateral trials became faster than unilateral trials as difficulty increased (bilateral processing advantage, BPA). The study yielded three novel findings. First, whereas FA in anterior corpus callosum (genu) correlated with word-matching BPA, FA in posterior corpus callosum (splenium-occipital) correlated with face-matching BPA. Second, as matching difficulty intensified, cross-hemispheric functional connectivity (CFC) increased in domain-general frontopolar cortex (for both word and face matching) but decreased in domain-specific ventral temporal lobe regions (temporal pole for word matching and fusiform gyrus for face matching). Last, a mediation analysis linking DTI and fMRI data showed that CFC mediated the effect of callosal FA on BPA. These findings clarify the mechanisms by which the hemispheres interact to perform complex cognitive tasks.

Examining empathy and its association with aggression in young Western Cape children

Objective: In a context of disturbing rates of violent crime, this pilot study initiated examination of the association between empathy and aggressive behaviour in young Western Cape children. Establishing which empathy measures are appropriate for our context was a primary concern.Method: To capture various aspects of empathy in young children we adopted a multi-method approach. We examined empathy scores in 65 lower middle socio-economic status (SES) English-speaking Grade 1 pupils (6.0–8.0 years old) and their association with aggressive externalising behaviour.Results: Parent report measures of callous-unemotional traits and aggression performed well, however, the Griffith Empathy Measure, a well-established parent report scale, proved problematic. Empathy scores, particularly those for callous-unemotional traits, were associated with aggression. We also found several associations between aspects of empathy, consistent with the international literature. Cognitive empathy, as measured by first-order false belief reasoning, was not well established, suggesting that other more complex social cognitive tasks were inappropriate for this age group in our context.Conclusion: The association between empathy and aggression warrants further investigation in this context. Appropriate measures for the South African context must be utilised.

Frontal midline theta oscillations during mental arithmetic: effects of stress.

Complex cognitive tasks such as mental arithmetic heavily rely on intact, well-coordinated prefrontal cortex (PFC) function. Converging evidence suggests that frontal midline theta (FMT) oscillations play an important role during the execution of such PFC-dependent tasks. Additionally, it is well-established that acute stress impairs PFC function, and recent evidence suggests that FMT is decreased under stress. In this EEG study, we investigated FMT oscillations during a mental arithmetic task that was carried out in a stressful and a neutral control condition. Our results show late-onset, sustained FMT increases during mental arithmetic. In the neutral condition FMT started to increase earlier than in the stress condition. Direct comparison of the conditions quantified this difference by showing stronger FMT increases in the neutral condition in an early time window. Between-subject correlation analysis showed that attenuated FMT under stress was related to slowed reaction times. Our results suggest that FMT is associated with stimulus independent mental processes during the natural and complex PFC-dependent task of mental arithmetic, and is a possible marker for intact PFC function that is disrupted under stress.

Mean-field thalamocortical modeling of longitudinal EEG acquired during intensive meditation training

Meditation training has been shown to enhance attention and improve emotion regulation. However, the brain processes associated with such training are poorly understood and a computational modeling framework is lacking. Modeling approaches that can realistically simulate neurophysiological data while conforming to basic anatomical and physiological constraints can provide a unique opportunity to generate concrete and testable hypotheses about the mechanisms supporting complex cognitive tasks such as meditation. Here we applied the mean-field computational modeling approach using the scalp-recorded electroencephalogram (EEG) collected at three assessment points from meditating participants during two separate 3-month-long shamatha meditation retreats. We modeled cortical, corticothalamic, and intrathalamic interactions to generate a simulation of EEG signals recorded across the scalp. We also present two novel extensions to the mean-field approach that allow for: (a) non-parametric analysis of changes in model parameter values across all channels and assessments; and (b) examination of variation in modeled thalamic reticular nucleus (TRN) connectivity over the retreat period. After successfully fitting whole-brain EEG data across three assessment points within each retreat, two model parameters were found to replicably change across both meditation retreats. First, after training, we observed an increased temporal delay between modeled cortical and thalamic cells. This increase provides a putative neural mechanism for a previously observed reduction in individual alpha frequency in these same participants. Second, we found decreased inhibitory connection strength between the TRN and secondary relay nuclei (SRN) of the modeled thalamus after training. This reduction in inhibitory strength was found to be associated with increased dynamical stability of the model. Altogether, this paper presents the first computational approach, taking core aspects of physiology and anatomy into account, to formally model brain processes associated with intensive meditation training. The observed changes in model parameters inform theoretical accounts of attention training through meditation, and may motivate future study on the use of meditation in a variety of clinical populations.

Genetic determinants of cognitive responses to caffeine drinking identified from a double-blind, randomized, controlled trial

The widely observed between-subject variability in cognitive responses to coffee may have a genetic basis.We evaluated cognitive responses to caffeine throughout three complex cognitive tasks assessing different subdomains of attention, namely Alerting and Orienting (Categorical Search Task) and Executive Control (Stroop Task and Eriksen Flanker Task). We explored whether they are influenced by gene variants affecting adenosine metabolism or catecholamine receptors.We recruited 106 healthy male subjects who were administered, in a double-blind design, 40mL of either a decaffeinated coffee preparation plus 3mg/kg caffeine (caf) or the corresponding vehicle (decaf). The protocol was repeated 24h later with the alternative preparation. Cognitive tasks were performed between 30min and 2h after caf or decaf administration. Each subject underwent ambulatory blood pressure monitoring for 2h. Blood samples were collected for genetic evaluations and for plasma caffeine and catecholamines measures.We found a significant reduction of reaction times in two of the cognitive tasks (Categorical Search Task and Stroop Task) after caf compared with decaf, indicating that caffeine, on average, improved the attention level in the domains under investigation. We also found, however, a great inter-individual variability in the cognitive performance responses to caffeine. In exploring genetic sources for such variability, we found a relation between polymorphisms of adenosine A2A and the caffeine effects on the attentional domains of Orienting and Executive control.In conclusion, variability in the attentional response to coffee may be partly explained by genetic polymorphisms of adenosine and adrenergic receptors.

Special issue on the 11th conference on naturalistic decision making

The naturalistic decision-making (NDM) approach deals with how humans make decisions in natural settings, especially professional situations, which can be difficult to reproduce in experimental laboratory studies. NDM explores collaboration and cooperation both between humans, and between humans and systems, as well as situations of diagnosis, planning, supervision, and control processes. Basically, decision researchers focus on the analysis of humans at work and their interactions with systems in context, including both environmental and social dimensions. Since the movement was founded, more than 20 years ago, several models of decision making have been developed, each offering a fresh view on how humans perform complex cognitive functions to accomplish situated activities. This very different way of studying human interactions in modern work environments has given rise to a worldwide research community sharing the same issues and a common theoretical background, and relying on ecological models of decision making (Brehmer 1992; Endsley 1995; Hutchins 1995; Rasmussen et al. 1994); models of intelligence, perception, and action as mental models (Johnson-Laird 1983); and activity theory (Engestrom 1999; Kuutti 1996; Nardi 1996). Looking further back, the field of naturalistic decision making can be seen to have its historical roots in the ground-breaking work of Vygotsky and Leont’ev (Leont’ev 1978; Leont’ev and Luria 1968), and even Piaget’s intelligence theory (Piaget 1972, 1977). The NDM approach, which is predicated upon a strong relationship between application fields, research, and models of complex cognitive tasks, is responsible for a now well-established definition of decision making (Klein et al. 1993): ‘‘eight important factors characterize decision making in naturalistic settings, but frequently are ignored in decision-making research. It is not likely that all eight factors will be at their most difficult levels in any one setting, but often several of these factors will complicate the decision task.

The Impact of Sleep Disruption on Complex Cognitive Tasks

We aimed to build upon the state of knowledge about the impacts of sleep disruption into the domain of complex cognitive task performance for three types of sleep disruption: total sleep deprivation, sleep restriction, and circadian cycle. Sleep disruption affects human performance by increasing the likelihood of errors or the time it takes to complete tasks, such as the Psychomotor Vigilance Task. It is not clear whether complex tasks are affected in the same way. Understanding the impact of sleep disruption on complex cognitive tasks is important for, and in some instances more relevant to, professional workers confronted with unexpected, catastrophic failures following a period of disrupted sleep. Meta-analytic review methods were applied to each of the three different areas of sleep disruption research. Complex cognitive task performance declines over consecutive hours of continuous wakefulness as well as consecutive days of restricted sleep, is worse for severely restricted sleep (4 or fewer hours in bed), is worse during the circadian nadir than apex, and appears less degraded than simple task performance. The reviews suggest that complex cognitive task performance may not be impacted by disrupted sleep as severely as simple cognitive task performance. Findings apply to predicting effects of sleep disruption on workers in safety-critical environments, such as health care, aviation, the military, process control, and in particular, safety-critical environments involving shiftwork or long-duration missions.

Selective neuronal degeneration in the retrosplenial cortex impairs the recall of contextual fear memory

The retrosplenial cortex (RSC) is one of the largest cortical areas in rodents, and is subdivided in two main regions, A29 and A30, according to their cytoarchitectural organization and connectivities. However, very little is known about the functional activity of each RSC subdivision during the execution of complex cognitive tasks. Here, we used a well-established fear learning protocol that induced long-lasting contextual fear memory and showed that during evocation of the fear memory, the expression of early growth response gene 1 was up-regulated in A30, and in other brain areas implicated in fear and spatial memory, however, was down-regulated in A29, including layers IV and V. To search for the participation of A29 on fear memory, we triggered selective degeneration of neurons within cortical layers IV and V of A29 by using a non-invasive protocol that takes advantage of the vulnerability that these neurons have MK801-toxicity and the modulation of this neurodegeneration by testosterone. Application of 5mg/kg MK801 in intact males induced negligible neuronal degeneration of A29 neurons and had no impact on fear memory retrieval. However, in orchiectomized rats, 5mg/kg MK801 induced overt degeneration of layers IV-V neurons of A29, significantly impairing fear memory recall. Degeneration of A29 neurons did not affect exploratory or anxiety-related behavior nor altered unconditioned freezing. Importantly, protecting A29 neurons from MK801-toxicity by testosterone preserved fear memory recall in orchiectomized rats. Thus, neurons within cortical layers IV-V of A29 are critically required for efficient retrieval of contextual fear memory.

Individual differences in mind wandering while reading predict lower rates of analogical transfer

Mind wandering has been argued to both impair and facilitate performance on complex cognitive tasks such as those encountered in the classroom. On the one hand, mind wandering may prevent students from fully encoding and remembering to-be-learned information. On the other hand, mind wandering may allow learners to consider to-be-learned information in the light of other contexts and situations. The current study examined the relationship between individual differences in mind wandering while reading and performance on an analogical reasoning task that required participants to recognize similarities between an unsolved problem and information presented in a set of passages. Across two studies, we found no evidence that participants who mind wandered more performed better on the reasoning task than participants who mind wandered less. In fact, the opposite pattern was observed, suggesting that the propensity to mind wander may put individuals at a disadvantage when it comes to analogical reasoning.

Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques.

This paper describes a discrete wavelet transform-based feature extraction scheme for the classification of EEG signals. In this scheme, the discrete wavelet transform is applied on EEG signals and the relative wavelet energy is calculated in terms of detailed coefficients and the approximation coefficients of the last decomposition level. The extracted relative wavelet energy features are passed to classifiers for the classification purpose. The EEG dataset employed for the validation of the proposed method consisted of two classes: (1) the EEG signals recorded during the complex cognitive task--Raven's advance progressive metric test and (2) the EEG signals recorded in rest condition--eyes open. The performance of four different classifiers was evaluated with four performance measures, i.e., accuracy, sensitivity, specificity and precision values. The accuracy was achieved above 98 % by the support vector machine, multi-layer perceptron and the K-nearest neighbor classifiers with approximation (A4) and detailed coefficients (D4), which represent the frequency range of 0.53-3.06 and 3.06-6.12 Hz, respectively. The findings of this study demonstrated that the proposed feature extraction approach has the potential to classify the EEG signals recorded during a complex cognitive task by achieving a high accuracy rate.

Measuring Cognitive Differences among Three Cultural Groups: An Exploratory Study

The purpose of the study was to explore two new macrocognitive measures that assessed complex cognitive differences in natural settings among three national groups; Malay, Indonesian and Arab students. The first, the Cognition in Context scale, measured differences in six cultural dimensions and the relationships among these dimensions. The second, the Dynamic Cognition test, measured decision making performance based on two unfolding scenarios. We predicted some shared cognitive processes among the groups due to their common religious denominator but also expected cultural differences because of their different histories. Results of the one-way ANOVA showed significant differences for all subscales of the Cognition in Context scale. Post-Hoc tests showed significant differences in the three groups on the various subscales. The results of the Dynamic Cognition test, however, were less clear. While these findings showed that Malays, Indonesians and Arabs are culturally different in terms of problem solving and decision making skills, due to the different sample sizes, the overall findings cannot be treated as clearly conclusive. Nevertheless, with respect to the two measures, the Cognition in Context scale appears to be more promising than Dynamic Cognition in understanding cultural differences involving complex cognitive tasks.

Fly-The-Bee: A Game Imitating Concept Learning in Bees

Abstract This article presents a web-based game functionally imitating a part of the cognitive behavior of a living organism. This game is a prototype implementation of an artificial online cognitive architecture based on the usage of distributed data representations and Vector Symbolic Architectures. The game demonstrates the feasibility of creating a lightweight cognitive architecture, which is capable of performing rather complex cognitive tasks. The cognitive functionality is implemented in about 100 lines of code and requires few tens of kilobytes of memory for its operation, which make the concept suitable for implementing in low-end devices such as minirobots and wireless sensors.

Modelling workload in cognitive and concurrent tasks with time stress using an integrated cognitive architecture

Workload modelling is one of the important issues in human factors modelling and simulation. Focusing on the cognitive rather than the physical aspect of workload, the goal of the present study is to examine a new simulation method for workload modelling in performing complex cognitive activities and concurrent tasks under time stress. In a general-purpose mental architecture called QN-ACTR, overall expected utilisation (OEU) is used as an index of workload. This computational technique possesses construct validity and does not require human subjective rating. We examined and demonstrated QN-ACTR modelling in the simulations of a cognitive judgment single-task and a target-shooting and arithmetic computation dual-task, both with time stress. OEU results from simulation were related to subjective workload measures from human empirical results. The models produced performance results similar to the human data and OEU workload index results capturing the difference between various task demand conditions. This new modelling technique could support the test and evaluation of complex human-machine systems in a wide range of domains.

Make it stick: the science of successful learning

To most of us, learning something hard way implies wasted time and effort. Good teaching, we believe, should be creatively tailored to the different learning styles of students and should use strategies that make learning easier. Make It Stick turns fashionable ideas like these on their head. Drawing on recent discoveries in cognitive psychology and other disciplines, the authors offer concrete techniques for becoming more productive learners.Memory plays a central role in our ability to carry out complex cognitive tasks, such as applying knowledge to problems never before encountered and drawing inferences from facts already known. New insights into how memory is encoded, consolidated, and later retrieved have led to a better understanding of how we learn. Grappling with the impediments that make learning challenging leads both to more complex mastery and better retention of what was learned.Many common study habits and practice routines turn out to be counterproductive. Underlining and highlighting, rereading, cramming, and single-minded repetition of new skills create the illusion of mastery, but gains fade quickly. More complex and durable learning come from self-testing, introducing certain difficulties in practice, waiting to re-study new material until a little forgetting has set in, and interleaving the practice of one skill or topic with another. Speaking most urgently to students, teachers, trainers, and athletes, Make It Stick will appeal to all those interested in the challenge of lifelong learning and self-improvement.

Revisiting the construct of “relational integration” and its role in accounting for general intelligence: The importance of knowledge integration

Recent research suggests that relational integration is a strong predictor of measures of complex cognitive abilities (e.g., Oberauer, Süβ, Wilhem, & Whittmann, 2008). In this paper we argue that there are at least two types of relational integration and that forming new relational structures by integrating relevant prior knowledge with new information is the fundamental relational integration process that underlies skill at performing many complex cognitive tasks. We describe a simple way to measure individual differences in this knowledge integration process and we show that our knowledge integration measure accounts for an impressive proportion of the variance on a battery of cognitive tests that assessed general fluid intelligence and specific abilities (verbal, quantitative, spatial). Knowledge integration also had better predictive power than two popular measures of the combined storage and processing capacity of working memory (reading span and operation span), as well as another relational integration measure that did not require accessing and integrating prior knowledge.

Source-based neurofeedback methods using EEG recordings: training altered brain activity in a functional brain source derived from blind source separation.

A developing literature explores the use of neurofeedback in the treatment of a range of clinical conditions, particularly ADHD and epilepsy, whilst neurofeedback also provides an experimental tool for studying the functional significance of endogenous brain activity. A critical component of any neurofeedback method is the underlying physiological signal which forms the basis for the feedback. While the past decade has seen the emergence of fMRI-based protocols training spatially confined BOLD activity, traditional neurofeedback has utilized a small number of electrode sites on the scalp. As scalp EEG at a given electrode site reflects a linear mixture of activity from multiple brain sources and artifacts, efforts to successfully acquire some level of control over the signal may be confounded by these extraneous sources. Further, in the event of successful training, these traditional neurofeedback methods are likely influencing multiple brain regions and processes. The present work describes the use of source-based signal processing methods in EEG neurofeedback. The feasibility and potential utility of such methods were explored in an experiment training increased theta oscillatory activity in a source derived from Blind Source Separation (BSS) of EEG data obtained during completion of a complex cognitive task (spatial navigation). Learned increases in theta activity were observed in two of the four participants to complete 20 sessions of neurofeedback targeting this individually defined functional brain source. Source-based EEG neurofeedback methods using BSS may offer important advantages over traditional neurofeedback, by targeting the desired physiological signal in a more functionally and spatially specific manner. Having provided preliminary evidence of the feasibility of these methods, future work may study a range of clinically and experimentally relevant brain processes where individual brain sources may be targeted by source-based EEG neurofeedback.

Functional brain networks contributing to the Parieto-Frontal Integration Theory of Intelligence

The refinement of localization of intelligence in the human brain is converging onto a distributed network that broadly conforms to the Parieto-Frontal Integration Theory (P-FIT). While this theory has received support in the neuroimaging literature, no functional magnetic resonance imaging study to date has conducted a whole-brain network-wise examination of the changes during engagement in tasks that are reliable measures of general intelligence (e.g., Raven's Progressive Matrices Test; RPM). Seventy-nine healthy subjects were scanned while solving RPM problems and during rest. Functional networks were extracted from the RPM and resting state data using Independent Component Analysis. Twenty-nine networks were identified, 26 of which were detected in both conditions. Fourteen networks were significantly correlated with the RPM task. The networks' spatial maps and functional connectivity measures at 3 frequency levels (low, medium, & high) were compared between the RPM and rest conditions. The regions involved in the networks that were found to be task related were consistent with the P-FIT, localizing to the bilateral medial frontal and parietal regions, right superior frontal lobule, and the right cingulate gyrus. Functional connectivity in multiple component pairs was differentially affected across all frequency levels during the RPM task. Our findings demonstrate that functional brain networks are more stable than previously thought, and maintain their general features across resting state and engagement in a complex cognitive task. The described spatial and functional connectivity alterations that such components undergo during fluid reasoning provide a network-wise framework of the P-FIT that can be valuable for further, network based, neuroimaging inquiries regarding the neural underpinnings of intelligence.

Severity of negative symptoms significantly affects cognitive functioning in patients with chronic schizophrenia: the slowing in cognitive processing

Studies in patients with schizophrenia have shown a decreased overall cognitive performance, and it was found that processing speed and working memory functions are affected. The aim of this study was to describe the general cognitive performance of patients with chronic schizophrenia and analyze its relationship with the severity of psychotic symptoms. Methods: Forty-eight patients diagnosed with DSM IV-TR schizophrenia disorder were examined for symptom improvement, measured by scales SAPS and SANS. Participants also completed the full scale WAIS-III. Results: The results show a generalized cognitive deficit, reflected in the low level of general intelligence, as well as the different index that comprise the scale. The most compromised index was the processing speed. The correlations showed that the overall severity of negative symptoms significantly affects cognitive functioning of chronic patients. The formal thought disorder and alogia significantly correlated with almost all the WAIS-III measures. Conclusions: Multiple studies of specific cognitive domains in schizophrenia have shown that deficits in processing speed are the core element of cognitive impairment in schizophrenia. We support the hypothesis about the slowing in cognitive processing affect both the performance of the basic and more complex cognitive task

Functional connectivity among spike trains in neural assemblies during rat working memory task

Working memory refers to a brain system that provides temporary storage to manipulate information for complex cognitive tasks. As the brain is a more complex, dynamic and interwoven network of connections and interactions, the questions raised here: how to investigate the mechanism of working memory from the view of functional connectivity in brain network? How to present most characteristic features of functional connectivity in a low-dimensional network? To address these questions, we recorded the spike trains in prefrontal cortex with multi-electrodes when rats performed a working memory task in Y-maze. The functional connectivity matrix among spike trains was calculated via maximum likelihood estimation (MLE). The average connectivity value Cc, mean of the matrix, was calculated and used to describe connectivity strength quantitatively. The spike network was constructed by the functional connectivity matrix. The information transfer efficiency Eglob was calculated and used to present the features of the network. In order to establish a low-dimensional spike network, the active neurons with higher firing rates than average rate were selected based on sparse coding. The results show that the connectivity Cc and the network transfer efficiency Eglob vaired with time during the task. The maximum values of Cc and Eglob were prior to the working memory behavior reference point. Comparing with the results in the original network, the feature network could present more characteristic features of functional connectivity.