Visual search, a subproblem of vision, is computationally intractable in thegeneral sense. Attentional methods can be used to attain a tractable approximation;the Selective Tuning (ST) model in particular provides a solution tomany challenges of visual search. ST components which address the BoundaryProblem, however, have previously not been fully implemented. This thesis implementsthe proposed visual fixation control using a set of modules including aPeripheral Priority Map, a Fixation History Map, and a History Biased PriorityMap. The system is tested in both a physical and a virtual environment. Thetest on physical hardware demonstrates proof-of-principle performance for theproposed method of fixational control. The virtual data-set compares fixationsproduced by the system developed in this thesis with human fixational data; oursystem out-performs AIM in reproducing human-like fixation patterns. Severalavenues of future research are proposed using this system as a developmentplatform.

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