How do people determine the relative difficulty of mental tasks and physical tasks, and how do they determine the preferred order of such tasks? Is it harder to make such decisions if 1 task is ...mainly mental and the other is mainly physical than if both tasks are the same kind? To address these questions, we conducted 3 experiments. In experiment 1 we asked participants to judge the relative difficulty and preferred ordering of mental tasks (math problems). In experiment 2 we asked participants to judge the relative difficulty and preferred ordering of physical tasks (moving a bucket back and forth). In experiment 3 we asked participants to judge the relative difficulty and preferred ordering of the same mental and physical tasks as in the first 2 experiments but with 1 of the tasks being mental and the other being physical. We reasoned that if mental task difficulty and physical task difficulty share a common code and if task ordering is systematically related to task difficulty, then judgments in experiment 3 should be as systematic as judgments in Experiments 1 and 2. The results confirmed the prediction and helped extend the notion of common codes for perception and performance to the evaluation of task difficulty and task ordering. A surprising finding was that mental difficulty was implicitly judged to be more important than physical difficulty for the tasks and population studied here.
Public Significance Statement
It is vital for safety and efficiency for people to make wise decisions about which activities do when. This study showed that activities judged to be easy are generally chosen before activities judged to be hard. In addition, and addressing 1 of the biggest obstacles to a full understanding of how such decisions are made, the present research shows that activities of different kinds (mental tasks and physical tasks) are likely compared and ordered with respect to an underlying abstract code for difficulty. Another finding was that mental difficulty appeared to dominate physical difficulty.
Motor imagery is the task most commonly used to induce changes in electroencephalographic (EEG) signals for mental imagery-based brain computer interfacing (BCI). In this study, we investigated EEG ...patterns that were induced by seven different mental tasks (i.e. mental rotation, word association, auditory imagery, mental subtraction, spatial navigation, imagery of familiar faces and motor imagery) and evaluated the binary classification performance. The aim was to provide a broad range of reliable and user-appropriate tasks to make individual optimization of BCI control strategies possible. Nine users participated in four sessions of multi-channel EEG recordings. Mental tasks resulting most frequently in good binary classification performance include mental subtraction, word association, motor imagery and mental rotation. Our results indicate that a combination of ‘brain-teasers’ – tasks that require problem specific mental work (e.g. mental subtraction, word association) – and dynamic imagery tasks (e.g. motor imagery) result in highly distinguishable brain patterns that lead to an increased performance.
► User-appropriate control strategies can enhance individual performance and comfort. ► Combinations of brain-teasers and dynamic imagery tasks result in best performance. ► Brain patterns are stable over time and support classification results.
A 4-week experimental study (N = 67) examined the motivational predictors and positive emotion outcomes of regularly practicing two mental exercises: counting one's blessings ("gratitude") and ...visualizing best possible selves ("BPS"). In a control exercise, participants attended to the details of their day. Undergraduates performed one of the three exercises during Session I and were asked to continue performing it at home until Session II (in 2 weeks) and again until Session III (in a further 2 weeks). Following previous theory and research, the practices of gratitude and BPS were expected to boost immediate positive affect, relative to the control condition. In addition, we hypothesized that continuing effortful performance of these exercises would be necessary to maintain the boosts (Lyubomirsky, S., Sheldon, K. M., & Schkade, D. (
2005a
). Pursuing happiness: The architecture of sustainable change. Review of General Psychology, 9, 111-131). Finally, initial self-concordant motivation to perform the exercise was expected to predict actual performance and to moderate the effects of performance on increased mood. Results generally supported these hypotheses, and suggested that the BPS exercise may be most beneficial for raising and maintaining positive mood. Implications of the results for understanding the critical factors involved in increasing and sustaining positive affect are discussed.
Brain-computer interface (BCI) is a domain, in which a person can send information without using any exterior nerve or muscles, just using their brain signal, called electroencephalography (EEG) ...signal. Multiview learning or data integration or data fusion from a different set of features is an emerging way in machine learning to improve the generalized performance by considering the knowledge with multiple views. Multiview learning has made rapid progress and development in recent years and is also facing many new challenges. This method can be used in the BCI domain, as the meaningful representation of the EEG signal in plenty of ways. This study utilized the multiview ensemble learning (MEL) approach for the binary classification of five mental tasks on the six subjects individually. In this study, we used a well-known EEG database (Keirn and Aunon database). The EEG signal has been decomposed using by methods i.e wavelet transform (WT), empirical mode decomposition (EMD), empirical wavelet transform (EWT), and fuzzy C-means followed by EWT (FEWT). After that, the feature coding technique is applied using parametric feature formation from the decomposed signal. Hence, we had four views to learn four same type of independent base classifiers and predictions are made in an ensemble manner. The study is performed independently with three types of base classifiers, i.e., K-nearest neighbor (KNN), support vector machine (SVM) with linear and non-linear kernels The performance validation of the ten combinations of mental tasks was performed by three MEL based classifiers, i.e., K-nearest neighbor (KNN), support vector machine (SVM) with linear and non-linear kernels. For reliability of the obtained results of the classifiers, 10-fold cross-validation was used. The proposed algorithm shows a promising accuracy of 80% to 100% for binary pair-wise classification of mental tasks.
The strength model of self-control has been predominantly tested with people from Western cultures. The present research asks whether the phenomenon of ego-depletion generalizes to a culture ...emphasizing the virtues of exerting mental self-control in everyday life. A pilot study found that whereas Americans tended to believe that exerting willpower on mental tasks is depleting, Indians tended to believe that exerting willpower is energizing. Using dual task ego-depletion paradigms, Studies 1a, 1b, and 1c found reverse ego-depletion among Indian participants, such that participants exhibited better mental self-control on a subsequent task after initially working on strenuous rather than nonstrenuous cognitive tasks. Studies 2 and 3 found that Westerners exhibited the ego-depletion effect whereas Indians exhibited the reverse ego-depletion effect on the same set of tasks. Study 4 documented the causal effect of lay beliefs about whether exerting willpower is depleting versus energizing on reverse ego-depletion with both Indian and Western participants. Together, these studies reveal the underlying basis of the ego-depletion phenomenon in culturally shaped lay theories about willpower.
Today's work environments have high cognitive demands, and mental workload is one of the main causes of work stress, human errors, and accidents. While several mental workload studies have compared ...the mental workload perceived by groups of experienced participants to that perceived by novice groups, no comparisons have been made between the same individuals performing the same tasks at different times.
This work aims to compare NASA Task Load Index (NASA-TLX) to Workload Profile (WP) in terms of their sensitivity. The comparison considers the impact of experience and task differentiation in the same individual once a degree of experience has been developed in the execution of the same tasks. It also considers the acceptability and intrusivity of the techniques.
The sample consisted of 30 participants who performed four tasks in two sessions. The first session was performed when participants had no experience; the second session was performed after a time of practice. Mental workload was assessed after each session. Statistical methods were used to compare the results.
The NASA-TLX proved to be more sensitive to experience, while the WP showed greater sensitivity to task differentiation. In addition, while both techniques featured a similar degree of intrusivity, the NASA-TLX received greater acceptability.
The acceptability of WP is low due to the high complexity of its dimensions and clarifying explanations of these may be necessary to increase acceptability. Future research proposals should be expanded to consider mental workload when designing work environments in current manufacturing environments.
In the last few years, many research works have been suggested on Brain-Computer Interface (BCI), which assists severely physically disabled persons to communicate directly with the help of ...electroencephalogram (EEG) signal, generated by the thought process of the brain. Thought generation inside the brain is a dynamic process, and plenty thoughts occur within a small time window. Thus, there is a need for a BCI device that can distinguish these various ideas simultaneously. In this research work, our previous binary-class mental task classification has been extended to the multi-class mental task problem. The present work proposed a novel feature construction scheme for multi mental task classification. In the proposed method, features are extracted in two phases. In the first step, the wavelet transform is used to decompose EEG signal. In the second phase, each feature component obtained is represented compactly using eight parameters (statistical and uncertainty measures). After that, a set of relevant and non-redundant features is selected using linear regression, a multivariate feature selection approach. Finally, optimal decision tree based support vector machine (ODT-SVM) classifier is used for multi mental task classification. The performance of the proposed method is evaluated on the publicly available dataset for 3-class, 4-class, and 5-class mental task classification. Experimental results are compared with existing methods, and it is observed that the proposed plan provides better classification accuracy in comparison to the existing methods for 3-class, 4-class, and 5-class mental task classification. The efficacy of the proposed method encourages that the proposed method may be helpful in developing BCI devices for multi-class classification.
Highlights • Humans are able to gradually self-regulate regional brain activation by applying cognitive strategies. • Providing rtfMRI neurofeedback can enhance the gradual self-regulation ability. • ...Findings are generalizable to various mental tasks and clinical MR field strengths. • Novel parametric activation paradigm enriches spectrum of rtfMRI-neurofeedback and BCI methodology.
In this paper, classification of mental task-root brain-computer interfaces (BCIs) is being investigated. The mental tasks are dominant area of investigations in BCI, which utmost interest as these ...system can be augmented life of people having severe disabilities. The performance of BCI model primarily depends on the construction of features from brain, electroencephalography (EEG), signal, and the size of feature vector, which are obtained through multiple channels. The availability of training samples to features are minimal for mental task classification. The feature selection is used to increase the ratio for the mental task classification by getting rid of irrelevant and superfluous features. This paper suggests an approach to augment the performance of a learning algorithm for the mental task classification on the utility of power spectral density (PSD) using feature selection. This paper also deals a comparative analysis of multivariate and univariate feature selection for mental task classification. After applying the above stated method, the findings demonstrate substantial improvements in the performance of learning model for mental task classification. Moreover, the efficacy of the proposed approach is endorsed by carrying out a robust ranking algorithm and Friedman's statistical test for finding the best combinations and compare various combinations of PSD and feature selection methods.
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