Abstract
A sample of 33 experiments was extracted from the Web-of-Science database over a 5-year period (2016–2020) that used physiological measures to measure intrinsic cognitive load. Only studies that required participants to solve tasks of varying complexities using a within-subjects design were included. The sample identified a number of different physiological measures obtained by recording signals from four main body categories (heart and lungs, eyes, skin, and brain), as well as subjective measures. The overall validity of the measures was assessed by examining construct validity and sensitivity. It was found that the vast majority of physiological measures had some level of validity, but varied considerably in sensitivity to detect subtle changes in intrinsic cognitive load. Validity was also influenced by the type of task. Eye-measures were found to be the most sensitive followed by the heart and lungs, skin, and brain. However, subjective measures had the highest levels of validity. It is concluded that a combination of physiological and subjective measures is most effective in detecting changes in intrinsic cognitive load.
Highlights
IntroductionThe main aim of this study was to examine the validity of using physiological techniques to measure cognitive load by examining construct validity (see Gravetter and Forzano, 2018) and sensitivity (see Longo and Orru, 2018)
The main aim of this study was to examine the validity of using physiological techniques to measure cognitive load by examining construct validity and sensitivity
Respiration rates and blood pressure (BP) were measured along with some novel indices, such as the Vascular response index, calculated from the ratio of different amplitudes taken from a photoplethysmogram waveform
Summary
The main aim of this study was to examine the validity of using physiological techniques to measure cognitive load by examining construct validity (see Gravetter and Forzano, 2018) and sensitivity (see Longo and Orru, 2018). To investigate the ability of physiological measures to detect differences in intrinsic cognitive load caused by tasks of varying complexity. To meet this aim we examined the findings from a number of studies drawn from a 5-year sample that measured cognitive load using physiological techniques. Researchers across many fields have been interested in the amount of mental resources invested in attempting a task One such field is human factors, where studies have focused on everyday tasks such as driving a motor vehicle. Of much interest has been the mental resources required to drive the car but deal with other requirements or distractors. Measurement of these mental resources has received much attention, for example, Wickens (2002) developed a model based on multiple resource theory that predicted dual-task interference
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