The N-back task is used extensively in literature as a working memory (WM) paradigm and it is increasingly used as a measure of individual differences. However, not much is known about the psychometric properties of this task and the current study aims to shed more light on this issue. We first review the current literature on the psychometric properties of the N-back task. With three experiments using task variants with different stimuli and load levels, we then investigate the nature of the N-back task by investigating its relationship to WM, and its role as an inter-individual difference measure. Consistent with previous literature, our data suggest that the N-back task is not a useful measure of individual differences in WM, partly because of its insufficient reliability. Nevertheless, the task seems to be useful for experimental research in WM and also well predicts inter-individual differences in other higher cognitive functions, such as fluid intelligence, especially when used at higher levels of load.
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http://dx.doi.org/10.1080/09658211003702171 | DOI Listing |
Ecotoxicol Environ Saf
January 2025
National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China; China CDC Key Laboratory of Environment and Population Health, Beijing 100021, China. Electronic address:
Cognitive fatigue in specific occupations may present a risk to personal safety. The study aimed to explore the characteristic volatile organic compounds (VOCs) in exhaled breath in response to cognitive fatigue, to provide a scientific basis for the non-invasive exhaled breath diagnostic techniques for cognitive fatigue assessing. Thirty healthy young adults were recruited and assigned to complete two 1.
View Article and Find Full Text PDFEur J Neurosci
January 2025
Department of Psychology, University of Lübeck, Lübeck, Germany.
Distraction is ubiquitous in human environments. Distracting input is often predictable, but we do not understand when or how humans can exploit this predictability. Here, we ask whether predictable distractors are able to reduce uncertainty in updating the internal predictive model.
View Article and Find Full Text PDFBrain Sci
December 2024
Department of Psychology, Faculty of Humanities and Social Sciences, University of Zagreb, 10000 Zagreb, Croatia.
Background/objectives: Cognitive training paradigms rely on the idea that consistent practice can drive neural plasticity, improving not only connectivity within critical brain networks, but also ultimately result in overall enhancement of trained cognitive functions, irrespective of the specific task. Here we opted to investigate the temporal dynamics of neural activity and cognitive performance during a structured cognitive training program.
Methods: A group of 20 middle-aged participants completed 20 training sessions over 10 weeks.
Neuropsychol Dev Cogn B Aging Neuropsychol Cogn
January 2025
Department of Psychology, Faculty of Humanities and Social Sciences, University of Zagreb, Zagreb, Croatia.
Research on working memory (WM) training reveals significant variability in training effects, indicating that pretraining cognitive abilities might account for these differences. However, consensus on whether higher (magnification account) or lower (compensation account) pretraining abilities predict greater training effects remains elusive. Our study aimed to clarify the role of fluid reasoning in predicting training performance (i.
View Article and Find Full Text PDFComput Methods Programs Biomed
January 2025
College of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai, 201318, PR China; Shanghai Yangpu Mental Health Center, Shanghai, 200093, PR China. Electronic address:
Background And Objective: The hybrid brain computer interfaces (BCI) combining electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) have attracted extensive attention for overcoming the decoding limitations of the single-modality BCI. With the deepening application of deep learning approaches in BCI systems, its significant performance improvement has become apparent. However, the scarcity of brain signal data limits the performance of deep learning models.
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