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.

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.

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.

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.

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.

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.

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.

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.