Task specificity of attention training: the case of probability cuing

Self Cite

Frequently finding a visual search target in one region of space induces a spatial attentional bias toward that region. Past studies on this effect typically tested fewer than 20 participants. The small sample prevents an investigation of two properties of learning: visual field uniformity and role of explicit awareness. Pooling data from multiple studies, here we examined location probability learning from ~120,000 visual search trials across 420 participants. Participants performed a serial search task. Unbeknownst to them, the target was disproportionately likely to appear in one visual quadrant. Location probability learning (LPL) was measured as the difference in reaction time to targets in the high-probability "rich" quadrant and the low-probability "sparse" quadrants. Results showed a lack of visual field effect. LPL was equivalent for "rich" quadrant in the upper left, upper right, lower left, and lower right. Learning did not induce a hotspot diagonal to the "rich" quadrant. To the contrary, RT was the longest in the diagonal quadrant. Recognition rate of the "rich" quadrant was above chance. However, recognition accuracy was unrelated to the size of LPL. Implicit learning induces visual-field-independent changes in spatial attention.

Section: Search Taskmentioning

confidence: 78%

Section: Other Inclusion Criteriamentioning

confidence: 99%

Experience-guided attention: Uniform and implicit

Jiang

Sha

Sisk

2018

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“…Second, the small amount of money did not prevent reward-based effects from emerging in previous studies (Stankevich & Geng, 2014), and it was sufficient to drive goal-driven attention (Experiment 4). Finally, although reward-based attention was frequently assessed in transfer tasks (Anderson et al, 2011; Chelazzi et al, 2014), significant effects have been observed in the training task (Failing & Theeuwes, 2014; Jiang, Swallow, Won, Cistera, & Rosenbaum, 2014; Lee & Shomstein, 2014; Le Pelley et al, 2014). Thus, although it is important to explore effects of reward size and spatial precision in future research, we believe that alternative approaches are needed for a deeper understanding of our findings.…”

Section: Discussionmentioning

confidence: 99%

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

Jiang

Sha

Remington

2015

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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.

“…However, recent results suggest that such transfer may not occur: a bias to attend to a region in space induced by probabilistic cuing did not transfer to a foraging task (Jiang et al, 2015). It is possible that spatial biases do not transfer because space must be shared for all manner of tasks (e.g., attending to the bottom right is relevant in typing, cooking, opening doors…).…”

Section: Introductionmentioning

confidence: 99%

Category-specific learned attentional bias to object parts

Chua

Gauthier

2015

Humans can selectively attend to information in visual scenes. Learning from previous experiences plays a role in how visual attention is subsequently deployed. For example, visual search times are faster in areas that are statistically more likely to contain a target (Jiang et al., 2014). Here, we examined whether similar attentional biases can be created for different locations on complex objects as a function of their category, based on a history of these locations containing a target. Subjects performed a visual search task in the context of novel objects called Greebles. The target appeared in one half (e.g., top) of the Greebles 89% of the time and in the other half (e.g., bottom) 11% of the time. We found a reaction time advantage when the target was located in a “target-rich” region, even after target location probabilities were equated. This indicates that attentional biases can be associated not only with regions of space, but with specific object features, or at least with locations in an object-based frame of reference.