Purchase this article with an account.
Rachael E. Gwinn, Andrew B. Leber; Targets previously associated with a unique response attract attention . Journal of Vision 2014;14(10):316. doi: https://doi.org/10.1167/14.10.316.
Download citation file:
© ARVO (1962-2015); The Authors (2016-present)
There has been much research aimed at determining how stimuli receive attentional priority. Beyond conventionally defined stimulus-driven and goal-driven contributions, factors such as learned usage of a task set (Leber & Egeth, 2006) and learned value (Anderson & Yantis, 2011) have been shown to affect priority. Here we introduce another factor, response uniqueness, for which targets associated with a unique response attract attention. Experiment 1 began with a training phase: on each trial a single circle, which could be one of six colors, moved across the display. Observers were instructed to produce a speeded go response for five of these colors and no-go for the remaining color. Next, during the test phase, color became task-irrelevant and observers were instructed to attend to shape. The shape on each trial could be a square, diamond, circle, or triangle. Participants now produced a go response to squares and no-go for the other shapes. Test phase results showed participants were faster when the square matched the no-go color from the training phase, suggesting that features previously associated with a unique response are granted greater attentional priority. To then test whether this priority boost was specific to stimuli associated with no-go responses, Experiment 2 reversed the response mapping in the training phase: a no-go response was required for five of the colors while a go response was required for the sixth color. The test phase was identical to that in the first experiment. Here, test phase results now showed that observers responded more rapidly to targets matching the go color from the training phase. This again shows that features previously associated with a unique response are granted greater attentional priority. These results contribute to a growing class of factors, based on learning, that are now understood to determine attentional allocation.
Meeting abstract presented at VSS 2014
This PDF is available to Subscribers Only