Purchase this article with an account.
Todd Horowitz, Kilian Semmelmann, Sage Boettcher, Jeremy M Wolfe; Visual foraging: Quitting behavior when searching aerial maps follows the Marginal Value Theorem. Journal of Vision 2013;13(9):1250. doi: 10.1167/13.9.1250.
Download citation file:
© ARVO (1962-2015); The Authors (2016-present)
Consider a radiologist searching a mammogram for tumors, a baggage screener searching for weapons, or an intelligence analyst poring over satellite imagery of North Korea. In each of these visual search tasks, each image contains an unknown number of targets and there are many images to get through. How do observers choose when to move to the next image? These tasks can be thought of as foraging tasks, which have been extensively studied in behavioral ecology. According to the Marginal Value Theorem (MVT), an organism leaves a patch when the local rate of acquiring energy drops below the global rate. Here we test whether MVT applies to a novel visual map foraging task. Stimuli were 50 maps sampled from ten major US metropolitan areas. From each metropolitan area, we selected five 3.4 square km images ranging from dense downtowns to sparse rural areas. Observers (N = 29) searched maps for gas stations using a custom interface created via the Google Maps API, allowing them to zoom in and out as well as pan around map images. Observers were given 50 minutes to find as many gas stations as possible. Our primary finding was that observers’ patch-leaving (i.e., map-leaving) strategy was well-described by MVT. The instantaneous reward rate for the last gas station located on a map was approximately the average global reward rate for that observer. Second, we found that observers were more likely to miss gas stations in maps that had more of them, independent of literal "crowding" by urban density. This may reflect "satisfaction of search". Third, we identified three within-patch search strategies: "knowledge-based" (e.g., searching along major thoroughfares); systematic "scanning" (e.g., searching along a grid); and "random sampling". Knowledge-based strategies were most successful. Interestingly, use of the knowledge-based strategy declined with age.
Meeting abstract presented at VSS 2013
This PDF is available to Subscribers Only