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How does the brain find objects in cluttered visual environments? For decades researchers have employed the classic visual search paradigm to answer this question using factorial designs. Although such approaches have yielded important information, they represent only a tiny fraction of the possible parametric space. Here we use a novel approach, by using a genetic algorithm (GA) to discover the way the brain solves visual search in complex environments, free from experimenter bias. Participants searched a series of complex displays, and those supporting fastest search were selected to reproduce (survival of the fittest). Their display properties (genes) were crossed and combined to create a new generation of "evolved" displays. Displays evolved quickly over generations towards a stable, efficiently searched array. Color properties evolved first, followed by orientation. The evolved displays also contained spatial patterns suggesting a coarse-to-fine search strategy. We argue that this behavioral performance-driven GA reveals the way the brain selects information during visual search in complex environments. We anticipate that our approach can be adapted to a variety of sensory and cognitive questions that have proven too intractable for factorial designs.

Evolving the stimulus to fit the brain: A genetic algorithm reveals the brain's feature priorities in visual search