We propose a datamining based method for automated reverse engineering of search strategies during active visual search tasks. The method uses a genetic program (GP) that evolves populations of fuzzy decision trees and selects an optimal one. Previous psychophysical observations of subjects engaged in a simple search task result in a database of stimulus conditions and concomitant measures of eye gaze information and associated psychophysical metrics that globally describe the subjects search strategies. Fuzzy rules about the likely design properties of the components of the visual system involved in selecting fixation location during search are defined based on these metrics. A fitness function that incorporates both the fuzzy rules and the information in the database is used to conduct GP based datamining. The information extracted through the GP process is the internal design specification of the visual system vis-à-vis active visual search.
Reverse engineering the visual system via genetic programs
Simoni, D. (2007). Reverse engineering the visual system via genetic programs. In DD. Schmorrow, & LM. Reeves (Eds.), Foundations of Augmented Cognition: Third International Conference, FAC 2007, Held as Part of HCI International 2007, Beijing, China, July 2007, Proceedings (pp. 195-200). Springer Verlag. https://doi.org/10.1007/978-3-540-73216-7