Pierre-Majorique Léger’s research while affiliated with HEC Montréal and other places

Computer-based learning has gained popularity in recent years, providing learners greater flexibility and freedom. However, these learning environments do not consider the learner’s mental state in real-time, resulting in less optimized learning experiences. This research aimed to explore the effect on the learning experience of a novel EEG-based Brain-Computer Interface (BCI) that adjusts the speed of information presentation in real-time during a learning task according to the learner’s cognitive load. We also explored how motivation moderated these effects. In accordance with three experimental groups (non-adaptive, adaptive, and adaptive with motivation), participants performed a calibration task (n-back), followed by a memory-based learning task concerning astrological constellations. Learning gains were assessed based on performance on the learning task. Self-perceived mental workload, cognitive absorption and satisfaction were assessed using a post-test questionnaire. Between-group analyses using Mann–Whitney tests suggested that combining BCI and motivational factors led to more significant learning gains and an improved learning experience. No significant difference existed between the BCI without motivational factor and regular non-adaptive interface for overall learning gains, self-perceived mental workload, and cognitive absorption. However, participants who undertook the experiment with an imposed learning pace reported higher overall satisfaction with their learning experience and a higher level of temporal stress. Our findings suggest BCI’s potential applicability and feasibility in improving memorization-based learning experiences. Further work should seek to optimize the BCI adaptive index and explore generalizability to other learning contexts.

Organizations are increasingly replacing performance ratings with continuous feedback systems. The current study assesses how people react to continuous performance feedback in terms of its content and sources concerning their performance, motivation to improve, and task engagement. A task-based experiment was conducted with 36 participants who received continuous feedback. The participants were divided into two groups, receiving either quantitative or qualitative feedback content. Feedback was delivered through computer-mediated, person-mediated, or no source. The results highlight that person-mediated feedback, regardless of content, positively influenced performance, motivation, and task engagement. On the other hand, quantitative feedback only showed a positive association with performance. These findings suggest that qualitative feedback is more effective, enhancing motivation and engagement. Managers should prioritize person-mediated feedback to optimize performance, as it yields superior outcomes compared to computer-mediated feedback. However, further research is required to comprehensively understand the effectiveness of continuous performance feedback and its specific characteristics.

BACKGROUND Cognitive functional ability affects the accessibility of information technology (IT) and is thus something that should be controlled for in user experience (UX) research. However, many cognitive function assessment batteries are long and complex, making them impractical for use in conventional experimental time frames. Therefore, there is a need for a short and reliable cognitive assessment which has discriminant validity for cognitive functions needed for general IT tasks. One potential candidate is the Trail Making Test (TMT). OBJECTIVE The present study investigated the usefulness of a digital TMT as a cognitive profiling tool in IT-related UX research by assessing its predictive validity on general IT task performance, and exploring its discriminant validity according to discrete cognitive functions required to perform the IT task. METHODS A digital TMT (Parts A and B) named Axon was administered to 27 healthy participants, followed by administration of five IT tasks in the form of CAPTCHAs. The discrete cognitive functions required to perform each CAPTCHA were rated by trained evaluators. To further explain and cross-validate our results, the original TMT and two psychological assessments of visuomotor and short-term memory function were administered. RESULTS Axon A and B were administrable in under five minutes and overall performance was significantly predictive of general IT task performance (F(5, 19) = 6.352, p = 0.001, Wilks’ Lambda = 0.374). This result was driven by performance on Axon B (F(5, 19) = 3.382, p = 0.024, Wilks’ Lambda = 0.529), particularly for IT tasks involving the combination of executive and visual function. Axon was furthermore cross validated with the original TMT (pcorr = 0.001 and pcorr = 0.017 for A and B, respectively) and visuomotor and short-term memory tasks. CONCLUSIONS The results demonstrate that variance in IT task performance among an age-homogenous neurotypical population can be related to inter-subject variance in cognitive function as assessed by Axon. Although Axon’s predictive validity seemed stronger for tasks involving the combination of executive and visual function, these cognitive functions are arguably relevant to the majority of IT interfaces. In conclusion, and considering its short administration time and remote implementability, the Axon digital TMT has potential to be a useful cognitive profiling tool for IT-based UX research.