Extraction of working memory load and the importance of understanding the temporal dynamics

Working memory processing is central for higher-order cognitive functions. Although the ability to access and extract working memory load has been proven feasible, the temporal resolution is low and cross-task generalization is poor. In this study, EEG oscillatory activity was recorded from sixteen healthy subjects while they performed two versions of the visual n-back task. Observed effects in the working memory-related EEG oscillatory activity, specifically in theta, alpha and low beta power, are significantly different in the two tasks (i.e. two categories of visual stimuli) and these differences are greatest after image onset. Furthermore, cross-task generalization can be obtained by concatenating both tasks and although similar performances are observed before and after image onset, this study highlights the complexity of working memory processing related to different categories of visual stimuli, particularly after image onset, that are crucial to understand, in order to interpret the extraction of working memory load.

[1]  T. Pasternak,et al.  Working memory in primate sensory systems , 2005, Nature Reviews Neuroscience.

[2]  Yufeng Ke,et al.  Towards an effective cross-task mental workload recognition model using electroencephalography based on feature selection and support vector machine regression. , 2015, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[3]  L. Reder,et al.  Individual differences in working memory capacity are reflected in different ERP and EEG patterns to task difficulty , 2015, Brain Research.

[4]  Wolfgang Rosenstiel,et al.  Using Cross-Task Classification for Classifying Workload Levels in Complex Learning Tasks , 2013, 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction.

[5]  Xiting Huang,et al.  Modulation of Alpha and Beta Oscillations during an n-back Task with Varying Temporal Memory Load , 2016, Front. Psychol..

[6]  P. Maquet,et al.  Orienting Attention to Locations in Perceptual Versus Mental Representations , 2004, Journal of Cognitive Neuroscience.

[7]  Robert Oostenveld,et al.  Estimating workload using EEG spectral power and ERPs in the n-back task , 2012, Journal of neural engineering.

[8]  Michael E. Smith,et al.  Monitoring Working Memory Load during Computer-Based Tasks with EEG Pattern Recognition Methods , 1998, Hum. Factors.

[9]  Glenn F. Wilson,et al.  Topographical changes in the ongoing EEG related to the difficulty of mental tasks , 2005, Brain Topography.

[10]  Mirka Pesonen,et al.  Brain oscillatory 4–30 Hz responses during a visual n-back memory task with varying memory load , 2007, Brain Research.

[11]  F. Castellanos,et al.  Neuroscience of attention-deficit/hyperactivity disorder: the search for endophenotypes , 2002, Nature Reviews Neuroscience.

[12]  Carryl L. Baldwin,et al.  Adaptive training using an artificial neural network and EEG metrics for within- and cross-task workload classification , 2012, NeuroImage.

[13]  Glenn F. Wilson,et al.  Real-Time Assessment of Mental Workload Using Psychophysiological Measures and Artificial Neural Networks , 2003, Hum. Factors.

[14]  Elaine Astrand,et al.  Selective visual attention to drive cognitive brain–machine interfaces: from concepts to neurofeedback and rehabilitation applications , 2014, Front. Syst. Neurosci..

[15]  Daphne N. Yu,et al.  High-resolution EEG mapping of cortical activation related to working memory: effects of task difficulty, type of processing, and practice. , 1997, Cerebral cortex.

[16]  Amanda J Quisenberry,et al.  Computerized Working-Memory Training as a Candidate Adjunctive Treatment for Addiction , 2014, Alcohol research : current reviews.

[17]  Peter Ford Dominey,et al.  Comparison of Classifiers for Decoding Sensory and Cognitive Information from Prefrontal Neuronal Populations , 2014, PloS one.

[18]  B. Postle Working memory as an emergent property of the mind and brain , 2006, Neuroscience.