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Aaron Johnson, John Brand, Yvette Esses, Bianca Grohmann, H. Onur Bodur; Prior knowledge of objects improves efficiency during hybrid visual search. Journal of Vision 2015;15(12):1362. doi: https://doi.org/10.1167/15.12.1362.
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© ARVO (1962-2015); The Authors (2016-present)
In our daily lives, visual search often involves looking for items that we have in memory, such as in the search for products to purchase in a supermarket. Previous work has shown that this type of search (termed Hybrid Visual Search) becomes less efficient – as assessed by increases in reaction time – as a function of set size of objects in the search array, and set size of the objects that are memorized. This previous work involved the memorization of random objects. But what would happen if participants already had prior knowledge of the object? For example, searching for a popular brand of cola among other products on a grocery shelf may be faster, as the participant already knows that the target object is red and white. This prior knowledge may therefore improve the efficiency of hybrid search. To test this hypothesis, we ran a hybrid visual search task on 100 participants for known North American brands, versus unknown European brands. Known and unknown brands were matched in terms of product category (i.e. each group of objects contained the same type of products). Brand knowledge (or lack thereof) was confirmed post-experiment via a questionnaire. We manipulated set size (4, 8, 16) for both items memorized, and for number of objects on the screen. All distractors were random objects. Similar to previous studies, we found set size effects for both items memorized, as well as the number of items on the screen for both known and unknown brands. Furthermore, we observed a statistically significant (p< .01) increase in reaction times across all set sizes for the unknown (versus known) objects. In addition, search slopes are steeper for unknown versus known objects. We therefore conclude that prior knowledge of items helps to make hybrid visual search more efficient.
Meeting abstract presented at VSS 2015
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