To quit or not to quit in dynamic search
- PDF / 2,131,692 Bytes
- 19 Pages / 595.276 x 790.866 pts Page_size
- 80 Downloads / 221 Views
40 YEARS OF FEATURE INTEGRATION: SPECIAL ISSUE IN MEMORY OF ANNE TREISMAN
To quit or not to quit in dynamic search Zhuanghua Shi 1 & Fredrik Allenmark 1 & Xiuna Zhu 1 & Mark A. Elliott 2 & Hermann J. Müller 1
# The Psychonomic Society, Inc. 2019
Abstract Searching for targets among similar distractors requires more time as the number of items increases, with search efficiency measured by the slope of the reaction-time (RT)/set-size function. Horowitz and Wolfe (Nature, 394(6693), 575–577, 1998) found that the target-present RT slopes were as similar for “dynamic” as for standard static search, even though the items were randomly reshuffled every 110 ms in dynamic search. Somewhat surprisingly, attempts to understand dynamic search have ignored that the target-absent RT slope was as low (or “flat”) as the target-present slope—so that the mechanisms driving search performance under dynamic conditions remain unclear. Here, we report three experiments that further explored search in dynamic versus static displays. Experiment 1 confirmed that the targetabsent:target-present slope ratio was close to or smaller than 1 in dynamic search, as compared with being close to or above 2 in static search. This pattern did not change when reward was assigned to either correct target-absent or correct target-present responses (Experiment 2), or when the search difficulty was increased (Experiment 3). Combining analysis of search sensitivity and response criteria, we developed a multiple-decisions model that successfully accounts for the differential slope patterns in dynamic versus static search. Two factors in the model turned out to be critical for generating the 1:1 slope ratio in dynamic search: the “quit-the-search” decision variable accumulated based upon the likelihood of “target absence” within each individual sample in the multiple-decisions process, whilst the stopping threshold was a linear function of the set size and reward manipulation. Keywords Visual search . Dynamic display . Search slope . Search efficiency
Significance
The challenge of dynamic search
When to quit plays a critical role in visual search. Here we investigated search performance under dynamic and static search, and developed a multiple-decision model with a quitting strategy that successfully accounts for the 1:1 search slope ratios for target-absent and target-present trials in dynamic search, for which classic search models fail to provide a ready account.
Anne Treisman’s feature integration theory (FIT; Treisman, 1986; Treisman & Gelade, 1980) is one of the early influential search models to draw a distinction between parallel and serial search. According to FIT, memory plays a critical role in serial search when a target is defined by a conjunction of features (such as when looking for a T amongst Ls). In this case, items are selected and processed serially by focal attention, with rechecking of already attended items prevented by some “inhibition of return (IOR)”-type tagging mechanism (Klein, 1988), yielding a linearly increasing function
Data Loading...
