Modulation of spatial attention by goals, statistical learning, and monetary reward

Abstract

This study documented the relative strength of task goals, visual statistical learning, and monetary reward in guiding spatial attention. Using a difficult T-among-L search task, we cued spatial attention to one visual quadrant by (i) instructing people to prioritize it (goal-driven attention), (ii) placing the target frequently there (location probability learning), or (iii) associating that quadrant with greater monetary gain (reward-based attention). Results showed that successful goal-driven attention exerted the strongest influence on search RT. Incidental location probability learning yielded a smaller though still robust effect. Incidental reward learning produced negligible guidance for spatial attention. The 95 % confidence intervals of the three effects were largely nonoverlapping. To understand these results, we simulated the role of location repetition priming in probability cuing and reward learning. Repetition priming underestimated the strength of location probability cuing, suggesting that probability cuing involved long-term statistical learning of how to shift attention. Repetition priming provided a reasonable account for the negligible effect of reward on spatial attention. We propose a multiple-systems view of spatial attention that includes task goals, search habit, and priming as primary drivers of top-down attention.

How do magnitude and frequency of monetary reward guide visual search?

“…This is contrasted with the failure to find such learning with a larger scale, higher accuracy visual search task used by us and Jiang et al (2015). Most important for the present purposes, the Chelazzi study did not compare the reward magnitude manipulation to a frequency manipulation.…”

“…Jiang et al (2015) previously addressed this issue by only rewarding responses on trials whose RTs were faster than the participants' median RTs. The logic goes that participants only have enough time to search part of the display while the reward is still available.…”

“…Jiang et al (2015) recently reported robust effects the former and negligible effects of the latter, although the tasks were not directly equated. One key difference was that Jiang et al's magnitude manipulation used monetary incentives whereas the frequency manipulation did not.…”

“…Given the traditionally strong focus of attention research on spatial attention (Bisley & Goldberg, 2010;Fecteau & Munoz, 2006), one might expect more published studies demonstrating learning of spatial reward magnitude. Moreover, one recent study has reported a failure to observe such learning (Jiang, Li, & Remington, 2015).…”

Memory-based attentional biases survive spatial suppression driven by selection history

“…Recently, it was recognized that activity within priority maps is not only controlled by top-down or bottom-up processes but can also be influenced by the "history" of former attentional deployment (Awh, Belopolsky, & Theeuwes, 2012). As a specific case, it is proposed that statistical regularities within a visual search display help to shape weights within priority maps such that frequent target and distractor locations become respectively facilitated and suppressed (Jiang, Sha, & Remington, 2015;Jiang, Swallow, & Rosenbaum, 2013;Wang & Theeuwes, 2018a, 2018b. To test how statistical regularities modulate distractor filtering, studies have manipulated the configuration of visual search displays such that the singleton distractor was more frequently presented on one specific location (Ferrante et al, 2017;Reder, Weber, Shang, & Vanyukov, 2003;Wang & Theeuwes, 2018a, 2018b, 2018c.…”