SPECIFIC AIMS Radiological scans are critically important for the health and well-being of millions of patients \[1\]. Despite advances in machine learning and computer vision, reading and searching in radiological scans still depends on the visual system of human observers (radiologists), who receive continuous training to best detect tumor masses and anomalies. Radiologists can examine hundreds and hundreds of x-rays on a daily basis, seeing several images one after the other. A main underlying assumption of their work is that radiologists' percepts and decisions on a current x-ray are completely independent from prior viewings. However, participants recently demonstrated that the visual system has visual serial dependencies (VSDs) at many levels, from perception to decision making \[2, 3\]. These sequential dependencies mean that what was seen in the past influences (and captures) what is seen and reported at this moment. Theoretically, VSDs would be helpful in an autocorrelated natural world, but they are suboptimal in search tasks conducted in artificial situations where images may not always be related. Importantly, serial dependences in perceptual processing could thus produce significant errors during diagnostic searches through radiological scans. The investigators' central hypothesis is that VSD can have a disruptive effect in radiologic searches that impairs accurate detection and recognition of tumors or other structures. This hypothesis is supported by the investigators' robust pilot data, which show that VSD strongly biases object classification in naïve observers and expert radiologists. The rationale for the proposed research projects is that once it is known how serial dependence arises and how it impacts visual search, we can understand how to control for it. Hence, accuracy of tumor detection and diagnosis can significantly improve. The specific objectives of this proposal are to establish (Aim 1), identify (Aim 2) and mitigate (Aim 3) the impact of VSD on visual search tasks in a clinical setting.

Aim 1: Establish the presence of sequential effects in perceptual decision making in clinically relevant tasks.

The investigators' previous theoretical work on sequential dependencies, based on simplified laboratory experiments, predicts that there should also be sequential effects in clinically relevant tasks such as tumor search and recognition in images. The investigators will test tumor recognition in four different domains: tumor detection, tumor classification, tumor localization, and search times. Pilot data in both naïve observers and expert radiologists supports the investigators' hypothesis that there are sequential effects in clinically relevant perceptual judgments.

Aim 2: Identify that the sequential effects in Aim 1 are, in fact, VSD. The investigators will test our prediction that the sequential effects in Aim 1 are, in fact, VSD, which is operationally defined by a set of diagnostic criteria: VSDs are spatially, temporally, and feature-similarity tuned. Aim 2 will therefore identify the tuning and boundary conditions of VSD in tumor search. This is crucial to develop strategies to mitigate the harmful consequences of VSD in Aim 3. Pilot data supports the feasibility of this aim.

Aim 3: Improve visual search performance in radiologists by mitigating VSD. Aim 2 will define the boundary conditions under which VSD operates in tumor search. Aim 3 will develop task manipulations to mitigate the impact of VSD. As a result of these manipulations, visual search performance should improve in measurable ways in several domains (detection, classification, localization, and search times). Aim 3 is particularly crucial because it will allow the investigators to propose new guidelines that will improve tumor recognition in radiological scans, making radiologists' judgments more effective and reliable. More globally, taken together, the proposed studies in Aims 1, 2 and 3 will allow the investigators to establish and work toward mitigating the deleterious role of VSD in a critically important clinical task.