Research in cognitive fatigue has identified the negative impact that cognitive exertion can have on subsequent task performance. An underexamined question is whether there are different types of fatigue, particularly: active fatigue, similar to cognitive fatigue, and passive fatigue, similar to boredom. This online study examined whether active and passive fatigue can be elicited and differentiated using computerized cognitive tasks. We compared subjective and behavioural outcomes to look for distinctions between fatigue types in response to different cognitive tasks. A sample of 122 participants (53% male; age 30.04 ± 3.50 years) rated their subjective state before and after one of three 8-min cognitive task conditions (TloadDback, Mackworth Clock, Documentary/Control). Next, participants also completed a second cognitive task (Flanker task). The task expected to be actively fatiguing (TloadDback) was rated the most difficult, effortful, and mentally and temporally demanding. The task expected to be passively fatiguing (Mackworth Clock) had the greatest increases in subjective fatigue, boredom, and sleepiness, and the greatest decrease in "want-to" motivation. There were no differences between conditions for Flanker performance. We showed that different fatigue types could be elicited using different computerized cognitive tasks. The passively fatiguing task had the most negative influence on subjective fatigue and motivation, suggesting a nonengaging or "boringly fatiguing" task induces a more detrimental type of fatigue. A key next step is to examine longer cognitive tasks to determine whether effects from different fatigue types become more prominent with time-on-task. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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Sci Rep
December 2024
Harman International, HarmanX Neurosense, 30001 Cabot Dr, Novi, MI, 48377, USA.
Cognitive load (CL) is one of the leading factors moderating states and performance among drivers. Heavily increased CL may contribute to the development of mental stress. Averaged heart rate (HR) and heart rate variability (HRV) indices are shown to reflect CL levels in different tasks.
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December 2024
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, 214122, China.
The unknown boundary issue, between superior computational capability of deep neural networks (DNNs) and human cognitive ability, has becoming crucial and foundational theoretical problem in AI evolution. Undoubtedly, DNN-empowered AI capability is increasingly surpassing human intelligence in handling general intelligent tasks. However, the absence of DNN's interpretability and recurrent erratic behavior remain incontrovertible facts.
View Article and Find Full Text PDFNat Commun
December 2024
Computational Neuroscience Unit, Intelligent Systems Labs, Faculty of Engineering, University of Bristol, Bristol, UK.
The brain must maintain a stable world model while rapidly adapting to the environment, but the underlying mechanisms are not known. Here, we posit that cortico-cerebellar loops play a key role in this process. We introduce a computational model of cerebellar networks that learn to drive cortical networks with task-outcome predictions.
View Article and Find Full Text PDFFront Psychol
December 2024
Laboratory of Functional Neurosciences (UR UPJV 4559), Jules Verne University of Picardie, Amiens, France.
Introduction: Numerous studies have explored the linguistic and executive processes underlying verbal fluency using association designs, which provide limited evidence. To assess the validity of our model, we aimed to refine the cognitive architecture of verbal fluency using an interference design.
Methods: A total of 487 healthy participants performed letter and semantic fluency tests under the single condition and dual conditions while concurrently performing a secondary task that interferes with speed, semantics, phonology, or flexibility.
Front Psychiatry
December 2024
Departamento de Personalidad, Evaluación y Tratamiento Psicológicos, Universidad de Salamanca, Salamanca, Spain.
Introduction: It is crucial to provide a quality educational response to the needs of autistic children across various mathematical domains. However, there is no consensus on which of the early skills have the greatest predictive effect in the short and long term within these domains. Therefore, this research aimed to a) compare early numerical skills and mathematics domains, and 2) analyze the predictive value of early numerical skills into mathematics domains.
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