Purchase this article with an account.
Todd Horowitz, Michael Cohen, Piers Howe, Jeremy Wolfe; Do multiple object tracking and letter identification use the same visual attention resource?. Journal of Vision 2009;9(8):247. doi: 10.1167/9.8.247.
Download citation file:
© ARVO (1962-2015); The Authors (2016-present)
Humans can track 3–5 independently moving objects among identical moving distractors (multiple object tracking). This has been taken as a demonstration of simultaneous parallel attention. Does this distributed attention facilitate letter identification, or do tracking and letter identification employ separate attentional resources?
Eight participants tracked 2, 3, or 4 out of 8 disks for 5–10 seconds. Two trial types were randomly intermixed. On tracking probe trials, one disk turned white and participants made a 2AFC target-distractor discrimination. On letter probe trials, pre- and post-masked letters were flashed on one target and three distractors locations for 80 ms. Participants made a 13AFC decision about the letter presented on the target disk (possible letters: ABCDEHLNSTXYZ).
Tracking accuracy was good: .92, .88, and .82 for 2, 3, and 4 targets, respectively. Letter accuracy was .69, .55 and .45.
We used data from 2-target trials to predict letter accuracy for 3 and 4 targets using two models. The separate resource model assumes that not all tracked objects are attended but any letter flashed on an attended object is perfectly identified. Conversely, the unlimited unified resource model assumes that all tracked objects are attended but that letters flashed on attended objects are not perfectly identified. The separate model estimated 1.3 items simultaneously attended, predicting letter identification accuracy of .48 (3 targets) and .38 (4 targets), significantly underpredicting performance. The unified model estimated letter identification accuracy for letters on attended targets to be .71, predicting letter identification accuracy of .65 (3 targets) and .62. (4 targets), significantly overpredicting performance.
The best explanation for the data is an (admittedly ad hoc) limited capacity unified resource model in which all tracked items are attended but the probability of identifying a letter on a tracked object declines as the number of tracked items increases.
This PDF is available to Subscribers Only