Rapid and selective updating of the target template in visual search.

Frequent target stimuli are detected more rapidly than infrequent ones. Here, we examined whether the frequency effect reflected durable attentional biases toward frequent target features, and whether the effect was confined to featural properties that defined the target. Participants searched for two specific target colors among distractors of heterogeneous colors and reported the line orientation of the target. The target was more often in one specific feature (e.g., a specific color or a specific orientation) than another in a training phase. This frequency difference was removed or reversed in a testing phase. Experiments 1 and 2 showed that when frequency differences were introduced to the target's defining feature, participants more rapidly found the high-frequency target than the low-frequency target. However, changes in attention were not durable-the search advantage vanished immediately when the frequency differences were removed. Experiments 3-5 showed that only featural properties that defined the target facilitated search of the more frequent feature. Features that did not define the target, such as the target feature that participants reported, sped up response but did not facilitate search. These data showed that when searching for multiple targets in a feature search task, people selectively and rapidly adapt to the frequency in the target's defining feature.

Redundancy gain in visual search of simulated X-ray images.

Cancer diagnosis frequently relies on the interpretation of medical images such as chest X-rays and mammography. This process is error prone; misdiagnoses can reach a rate of 15% or higher. Of particular interest are false negatives-tumors that are present but missed. Previous research has identified several perceptual and attentional problems underlying inaccurate perception of these images. But how might these problems be reduced? The psychological literature has shown that presenting multiple, duplicate images can improve performance. Here we explored whether redundant image presentation can improve target detection in simulated X-ray images, by presenting four identical or similar images concurrently. Displays with redundant images, including duplicates of the same image, showed reduced false-negative rates, compared with displays with a single image. This effect held both when the target's prevalence rate was high and when it was low. Eye tracking showed that fixating on two or more images in the redundant condition speeded target detection and prolonged search, and that the latter effect was the key to reducing false negatives. The redundancy gain may result from both perceptual enhancement and an increase in the search quitting threshold.

Perceptual learning in the identification of lung cancer in chest radiographs.

Extensive research has shown that practice yields highly specific perceptual learning of simple visual properties such as orientation and contrast. Does this same learning characterize more complex perceptual skills? Here we investigated perceptual learning of complex medical images. Novices underwent training over four sessions to discriminate which of two chest radiographs contained a tumor and to indicate the location of the tumor. In training, one group received six repetitions of 30 normal/abnormal images, the other three repetitions of 60 normal/abnormal images. Groups were then tested on trained and novel images. To assess the nature of perceptual learning, test items were presented in three formats - the full image, the cutout of the tumor, or the background only. Performance improved across training sessions, and notably, the improvement transferred to the classification of novel images. Training with more repetitions on fewer images yielded comparable transfer to training with fewer repetitions on more images. Little transfer to novel images occurred when tested with just the cutout of the cancer region or just the background, but a larger cutout that included both the cancer region and some surrounding regions yielded good transfer. Perceptual learning contributes to the acquisition of expertise in cancer image perception.

Statistical learning of anomalous regions in complex faux X-ray images does not transfer between detection and discrimination.

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.

Experience-guided attention: Uniform and implicit.

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.

Short-term and long-term attentional biases to frequently encountered target features.

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.

Components of reward-driven attentional capture.

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.

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

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.