The double task-switching protocol: An investigation into the effects of similarity and conflict on cognitive flexibility in the context of mental fatigue.

Considerable fundamental studies have focused on the mechanisms governing cognitive flexibility and the associated costs of switching between tasks. Task-switching costs refer to the phenomenon that reaction times and accuracy decrease briefly following the switch from one task to another. However, cognitive flexibility also impacts day-to-day life in many complex work environments where operators have to perform several different tasks. One major difference between typical tasks examined in fundamental studies and real-world applications is that fundamental studies often rely on much more similar tasks, which is not the case for real-world applications. In the latter, operators may switch between vastly dissimilar tasks. Therefore, this behavioural study aims to test if task-switching costs are different for switches between similar and dissimilar tasks. The proposed protocol has participants switch between 2 pairs of two tasks each. Between pairs, there is more dissimilarity, while the two tasks within each pair are more similar. In addition, this study examines the impact of mental fatigue and interference in form of confounding information on cognitive flexibility. To induce mental fatigue the participants' breaks between blocks will be limited. We expect that dissimilarity between tasks will result in greater task-switching costs.

Cognitive effects of prolonged continuous human-machine interaction: The case for mental state-based adaptive interfaces.

Operators of complex systems across multiple domains (e.g., aviation, automotive, and nuclear power industry) are required to perform their tasks over prolonged and continuous periods of time. Mental fatigue as well as reduced cognitive flexibility, attention, and situational awareness all result from prolonged continuous use, putting at risk the safety and efficiency of complex operations. Mental state-based adaptive systems may be a solution to this problem. These systems infer the current mental state of an operator based on a selection of metrics ranging from operator independent measures (e.g., weather and time of day), to behavioral (e.g., reaction time and lane deviation) as well as physiological markers (e.g., electroencephalography and cardiac activity). The interaction between operator and system may then be adapted in one of many ways to mitigate any detected degraded cognitive state, thereby ensuring continued safety and efficiency. Depending on the task at hand and its specific problems, possible adaptations -usually based on machine learning estimations- e.g., include modifications of information, presentation modality or stimuli salience, as well as task scheduling. Research on adaptive systems is at the interface of several domains, including neuroergonomics, human factors, and human-computer interaction in an applied and ecological context, necessitating careful consideration of each of the aforementioned aspects. This article provides an overview of some of the key questions and aspects to be considered by researchers for the design of mental state-based adaptive systems, while also promoting their application during prolonged continuous use to pave the way toward safer and more efficient human-machine interaction.

Map Learning with a 3D Printed Interactive Small-Scale Model: Improvement of Space and Text Memorization in Visually Impaired Students.

Special education teachers for visually impaired students rely on tools such as raised-line maps (RLMs) to teach spatial knowledge. These tools do not fully and adequately meet the needs of the teachers because they are long to produce, expensive, and not versatile enough to provide rapid updating of the content. For instance, the same RLM can barely be used during different lessons. In addition, those maps do not provide any interactivity, which reduces students' autonomy. With the emergence of 3D printing and low-cost microcontrollers, it is now easy to design affordable interactive small-scale models (SSMs) which are adapted to the needs of special education teachers. However, no study has previously been conducted to evaluate non-visual learning using interactive SSMs. In collaboration with a specialized teacher, we designed a SSM and a RLM representing the evolution of the geography and history of a fictitious kingdom. The two conditions were compared in a study with 24 visually impaired students regarding the memorization of the spatial layout and historical contents. The study showed that the interactive SSM improved both space and text memorization as compared to the RLM with braille legend. In conclusion, we argue that affordable home-made interactive small scale models can improve learning for visually impaired students. Interestingly, they are adaptable to any teaching situation including students with specific needs.