Examining the validity of the Needleman–Wunsch algorithm in identifying decision strategy with eye-movement data

• Published in: Decision Support Systems Vol. 49; no. 4; pp. 396 - 403
• Main Author: Day, Rong-Fuh
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.11.2010; Elsevier; Elsevier Sequoia S.A
• Subjects: Accuracy; Algorithms; Applied sciences; Artificial intelligence; Classification; Computer science; control theory; systems; Data processing. List processing. Character string processing; Decision making models; Decision support systems; Decision theory. Utility theory; Dynamic programming; Empirical analysis; Exact sciences and technology; Eye movements; Eye-tracking approach; Forecasting; Information search behavior; Memory organisation. Data processing; Multi-attribute decision-making; Needleman–Wunsch algorithm; Operational research and scientific management; Operational research. Management science; Pattern recognition. Digital image processing. Computational geometry; Searching; Sequence alignment; Software; Strategy; Studies; Information search behavior; Needleman–Wunsch algorithm; Eye-tracking approach; Multi-attribute decision-making; Sequence alignment; Search strategy; Decision support system; Decision making; Empirical method; Information retrieval; Pursuit eye movement; Eye movement; Multiple decision; Eye; Classification; Ophthalmology; Tracing; Needleman-Wunsch algorithm
• ISSN: 0167-9236; 1873-5797
• DOI: 10.1016/j.dss.2010.05.001

A new generation of eye trackers shows us a promising alternative approach to tracing decision processes beyond the popular computerized-information-board approach. In order to exploit the eye-movement data, this study examined the validity of the Needleman–Wunsch algorithm (NWA) to characterize the decision process, and proposed an NWA-based classification method to predict which typical strategy an empirical search behavior might belong to. An eye-tracking based experiment was conducted. Our results showed that the resemblance score by NWA conformed to the assumption that the pair of information search behaviors based on the same strategy should have the closest resemblance. Moreover, with respect to our NWA-based classification method, our result showed that its overall prediction accuracy, hit-ratio, in identifying underlying strategies achieved 88%, significantly much higher than that gained from chance. On the whole, the combination of eye-fixation data and our NWA-based classification method is qualified.