Evaluating mental workload while interacting with computer-generated artificial environments

Internet cognitive failure relevant to self-efficacy, learning interest, and satisfaction with social media learning

“…In this sense, the implication of CATLM paved the way to study interest in learning with social media and possible correlations of learning satisfaction with social media. Moreover, Di Stasi, Antolí, and Cañas (2013) stated that individual differences in cognitive traits could be used to predict the variability of cognitive processes in relation to affective responses during humanecomputer interaction. In line with CATLM, the purpose of this study was to develop a conceptual framework to identify the cognitive role that internet cognitive failure plays to affect two affective factors (i.e., self-efficacy in learning a musical instrument from social media and interest in learning with social media) and how it reflects another affective factor, learning satisfaction with social media.…”

Cognitive workload estimation due to vague visual stimuli using saccadic eye movements

“…There are several metrics related to each of these activities like the pupil size, saccade length, saccadic velocity, mean fixation duration, blink rate, etc. that can be used for the estimation of induced cognitive workload [7,8].…”

Mental workload assessment using a fuzzy multi-criteria method

“…Mental workload therefore refers to "a composite brain state or set of states that mediates human performance of perceptual, cognitive, and motor tasks" [3]. Mental workload is defined as the difference between the processing capacity level of the human information processing system and the capacity required to affect the actual performance.…”