Recent research reported that task-irrelevant colors captured attention if these colors previously served as search targets and received high monetary reward. We showed that both monetary reward and value-independent mechanisms influenced selective attention. Participants searched for two potential target colors among distractor colors in the training phase. Subsequently, they searched for a shape singleton in a testing phase. Experiment 1 found that participants were slower in the testing phase if a distractor of a previous target color was present rather than absent. Such slowing was observed even when no monetary reward was used during training. Experiment 2 associated monetary rewards with the target colors during the training phase. Participants were faster finding the target associated with higher monetary reward. However, reward training did not yield value-dependent attentional capture in the testing phase. Attentional capture by the previous target colors was not significantly greater for the previously high-reward color than the previously low or no-reward color. These findings revealed both the power and limitations of monetary reward on attention. Although monetary reward can increase attentional priority for the high-reward target during training, subsequent attentional capture effects may not be reward-based, but reflect, in part, attentional capture by previous targets.

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.

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.

It has long been known that frequently occurring targets are attended better than infrequent ones in visual search. But does this frequency-based attentional prioritization reflect momentary or durable changes in attention? Here we observed both short-term and long-term attentional biases for visual features as a function of different types of statistical associations between the targets, distractors, and features. Participants searched for a target, a line oriented horizontally or vertically among diagonal distractors, and reported its length. In one set of experiments we manipulated the target's color probability: Targets were more often in Color 1 than in Color 2. The distractors were in other colors. Participants found Color 1 targets more quickly than Color 2 targets, but this preference disappeared immediately when the target's color became random in the subsequent testing phase. In the other set of experiments, we manipulated the diagnostic values of the two colors: Color 1 was more often a target than a distractor; Color 2 was more often a distractor than a target. Participants found Color 1 targets more quickly than Color 2 targets. Importantly, and in contrast to the first set of experiments, the featural preference was sustained in the testing phase. These results suggest that short-term and long-term attentional biases are products of different statistical information. Finding a target momentarily activates its features, inducing short-term repetition priming. Long-term changes in attention, on the other hand, may rely on learning diagnostic features of the targets.

The visual environment contains predictable information - “statistical regularities” - that can be used to aid perception and attentional allocation. Here we investigate the role of statistical learning in facilitating search tasks that resemble medical-image perception. Using faux X-ray images, we employed two tasks that mimicked two problems in medical-image perception: detecting a target signal that is poorly segmented from the background; and discriminating a candidate anomaly from benign signals. In the first, participants searched a heavily camouflaged target embedded in cloud-like noise. In the second, the noise opacity was reduced, but the target appeared among visually similar distractors. We tested the hypothesis that learning may be task-specific. To this end, we introduced statistical regularities by presenting the target disproportionately more frequently in one region of the space. This manipulation successfully induced incidental learning of the target’s location probability, producing faster search when the target appeared in the high-probability region. The learned attentional preference persisted through a testing phase in which the target’s location was random. Supporting the task-specificity hypothesis, when the task changed between training and testing, the learned priority did not transfer. Eye tracking showed fewer, but longer, fixations in the detection than in the discrimination task. The observation of task-specificity of statistical learning has implications for theories of spatial attention and sheds light on the design of effective training tasks.