A machine learning investigation of factors that contribute to predicting cognitive performance: Difficulty level, reaction time and eye-movements

2022 | Decision Support Systems | Citations: 0

Authors: Bachurina, Valentina; Sushchinskaya, Svetlana; Sharaev, Maxim; Burnaev, Evgeny; Arsalidou, Marie

Abstract: Predicting accuracy in cognitively challenging tasks has potential applications ... Expand

Abstract: Predicting accuracy in cognitively challenging tasks has potential applications in education and industry. Task demand has been linked with increases in response time and variations in reaction time and eye-tracking metrics, however, machine learning research has not been used to predict performance on tasks with multiple levels of difficulty. We report data on adult participants who performed tasks of mental attentional capacity with six levels of difficulty and use machine learning methods to predict accuracy scores considering metrics associated with task difficulty, reaction time and eye movements. Results show that machine learning models can robustly predict performance with reaction times and difficulty level being the strongest predictors. Eye-tracking indices can also predict accuracy independently, with the most important features of the model driven by the number of fixations, number of saccades, duration of the current fixation and pupil size. Practical and theoretical impli­ cations of the results are discussed. Collapse

Topics: Python decision support system software developer

Methods: oculometry machine learning descriptive statistic computational algorithm regression analysis method

Theories: cognitive theory

Animated conversational agents and e-consumer productivity: The roles of agents and individual characteristics

2017 | Information & Management | Citations: 0

Authors: Ben Mimoun, Mohammed Slim; Poncin, Ingrid; Garnier, Marion

Abstract: This research presents two studies, in a lab (eye-tracking) and in a natural con ... Expand

Abstract: This research presents two studies, in a lab (eye-tracking) and in a natural context, that highlight the effects of interacting with an animated conversational agents (ACA) on the objective and perceived econsumer productivity. Results from Study 1 specify that only objective e-consumer productivity depends on interaction with the ACA. Going further, Study 2 then reveals that individual characteristics affect perceived productivity, either independently from ACA use (for involvement or product familiarity) or in interaction with using the ACA (for Internet skills and need for interaction). These findings highlight the need to personalize websites that display an agent fitting user profiles. Collapse

Topics: productivity website individual characteristic IT skill social presence

Methods: oculometry psychometrics analysis of covariance literature study survey