Medical students' cognitive load in volumetric image interpretation:Insights from human-computer interaction and eye movements

Medical image interpretation is moving from using 2D- to volumetric images, thereby changing the cognitive and perceptual processes involved. This is expected to affect medical students' experienced cognitive load, while learning image interpretation skills. With two studies this explorative research investigated whether measures inherent to image interpretation, i.e. human-computer interaction and eye tracking, relate to cognitive load. Subsequently, it investigated effects of volumetric image interpretation on second-year medical students' cognitive load. Study 1 measured human-computer interactions of participants during two volumetric image interpretation tasks. Using structural equation modelling, the latent variable 'volumetric image information' was identified from the data, which significantly predicted self-reported mental effort as a measure of cognitive load. Study 2 measured participants' eye movements during multiple 2D and volumetric image interpretation tasks. Multilevel analysis showed that time to locate a relevant structure in an image was significantly related to pupil dilation, as a proxy for cognitive load. It is discussed how combining human-computer interaction and eye tracking allows for comprehensive measurement of cognitive load. Combining such measures in a single model would allow for disentangling unique sources of cognitive load, leading to recommendations for implementation of volumetric image interpretation in the medical education curriculum

Medical students' cognitive load in volumetric image interpretation : Insights from human-computer interaction and eye movements

Medical image interpretation is moving from using 2D- to volumetric images, thereby changing the cognitive and perceptual processes involved. This is expected to affect medical students' experienced cognitive load, while learning image interpretation skills. With two studies this explorative research investigated whether measures inherent to image interpretation, i.e. human-computer interaction and eye tracking, relate to cognitive load. Subsequently, it investigated effects of volumetric image interpretation on second-year medical students' cognitive load. Study 1 measured human-computer interactions of participants during two volumetric image interpretation tasks. Using structural equation modelling, the latent variable 'volumetric image information' was identified from the data, which significantly predicted self-reported mental effort as a measure of cognitive load. Study 2 measured participants' eye movements during multiple 2D and volumetric image interpretation tasks. Multilevel analysis showed that time to locate a relevant structure in an image was significantly related to pupil dilation, as a proxy for cognitive load. It is discussed how combining human-computer interaction and eye tracking allows for comprehensive measurement of cognitive load. Combining such measures in a single model would allow for disentangling unique sources of cognitive load, leading to recommendations for implementation of volumetric image interpretation in the medical education curriculum

Medical students' cognitive load in volumetric image interpretation: Insights from human-computer interaction and eye movements

Medical image interpretation is moving from using 2D- to volumetric images, thereby changing the cognitive and perceptual processes involved. This is expected to affect medical students' experienced cognitive load, while learning image interpretation skills. With two studies this explorative research investigated whether measures inherent to image interpretation, i.e. human-computer interaction and eye tracking, relate to cognitive load. Subsequently, it investigated effects of volumetric image interpretation on second-year medical students' cognitive load. Study 1 measured human-computer interactions of participants during two volumetric image interpretation tasks. Using structural equation modelling, the latent variable 'volumetric image information' was identified from the data, which significantly predicted self-reported mental effort as a measure of cognitive load. Study 2 measured participants' eye movements during multiple 2D and volumetric image interpretation tasks. Multilevel analysis showed that time to locate a relevant structure in an image was significantly related to pupil dilation, as a proxy for cognitive load. It is discussed how combining human-computer interaction and eye tracking allows for comprehensive measurement of cognitive load. Combining such measures in a single model would allow for disentangling unique sources of cognitive load, leading to recommendations for implementation of volumetric image interpretation in the medical education curriculum