REVISING AND VALIDATING THE RANDOM SEARCH MODEL FOR COMPETITIVE SEARCH

A random search model was fit to a total of 2592 visual search times o n a single-target detection task. By using a competing homogeneous bac kground and uniform stimulus material, specifying viewing distance, co ntrolling the presentation of search task material, and eliminating so me options for extreme search strategies, very high correlation coeffi cients were found when a random search model was fit to both the indiv idual data and to pooled data. A response time parameter was incorpora ted into the traditional random-search model and very good predictions of search performance were obtained.