The rise and fall of hybrid visual and memory search

Abstract

In hybrid search, observers search through arrays of visually presented items for any of a set of targets held in memory; think of looking on a store shelf for items on your grocery list, searching luggage x-rays for potential banned items, or chest x-rays for signs of cancer. As the size of the memory sets used in hybrid search increased, descriptions of the RT by set size function moved from linear (Shiffrin & Schneider, 1977) to logarithmic (Wolfe, 2012). In order to study hybrid search at larger set sizes, with greater resolution, and as a function of expertise, I utilized the Airport Scanner (Kedlin Co., www.airportscannergame.com) dataset (see Mitroff and Biggs 2014). Airport Scanner is a game for mobile devices, which simulates searching through baggage x-rays for threats under time constraints. Players move through five ranks as they acquire game expertise, from Trainee to Elite. Critically, new items are added to the list of potential threats as the game progresses. Eliminating trainees to reduce potential learning effects, I analyzed only trials (bags) with a single target. This left 3,491,664 trials from 18,595 players. Memory set size(potential threats) ranged from 3 to 155 items. Visual set size had no influence on performance. For set sizes from 6-12, the logarithmic relationship held. However, across the full range of set sizes, at all expertise levels, reaction time was best described as a quadratic, rather than logarithmic, function of set size. For the low expertise groups, the curve opened upward, while at high expertise, it opened downward. These data suggest that encoding and retrieval strategies in hybrid search change in a complex fashion as the size of the memory set increases, and as observers gain more expertise with the task. Models developed for small set sizes may not generalize to realistically large set sizes. Meeting abstract presented at VSS 2015. Language: en