Formal Detection of Attentional Tunneling in Human Operator–Automation Interactions
| Autor: | Emmanuel Rachelson, Catherine Tessier, Mickaël Causse, Frédéric Dehais, Sergio Pizziol, Nicolas Regis, Charles Thooris |
|---|---|
| Přispěvatelé: | Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE), Office National d'Etudes et Recherches Aérospatiales - ONERA (FRANCE) |
| Rok vydání: | 2014 |
| Předmět: |
Computer Networks and Communications
Computer science Complex system Fuzzy neural networks Human Factors and Ergonomics Machine learning computer.software_genre Human–robot interaction Cognitive state inference Artificial Intelligence Human operator Automatique / Robotique Adaptive neuro fuzzy inference system business.industry Automation Computer Science Applications Human-Computer Interaction Attentional tunneling Control and Systems Engineering Signal Processing Artificial intelligence User interface Human-robot interaction business Human factors Classifier (UML) computer Test data |
| Zdroj: | IEEE Transactions on Human-Machine Systems. 44:326-336 |
| ISSN: | 2168-2305 2168-2291 |
| DOI: | 10.1109/thms.2014.2307258 |
| Popis: | The allocation of visual attention is a key factor for the humans when operating complex systems under time pressure with multiple information sources. In some situations, attentional tunneling is likely to appear and leads to excessive focus and poor decision making. In this study, we propose a formal approach to detect the occurrence of such an attentional impairment that is based on machine learning techniques. An experiment was conducted to provoke attentional tunneling during which psycho-physiological and oculomotor data from 23 participants were collected. Data from 18 participants were used to train an adaptive neuro-fuzzy inference system (ANFIS). From a machine learning point of view, the classification performance of the trained ANFIS proved the validity of this approach. Furthermore, the resulting classification rules were consistent with the attentional tunneling literature. Finally, the classifier was robust to detect attentional tunneling when performing over test data from four participants. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|