Serial dependence in skin lesion judgements
David Whitney, Professor
Psychology
Applications for Fall 2025 are closed for this project.
Previous work in the lab has shown that observers exhibit biases in their judgements of skin lesion malignancy, which depend on the previously shown lesion. For example, after viewing a benign lesion, observers tend to report that the current lesion is also benign if the two lesions are similar in appearance. This is concerning because it means observers may report that a lesion is benign when it is actually malignant, or malignant when the lesion is actually benign. This can lead to misdiagnoses with far reaching effects. This trend of errors depending on the previous trial is called serial dependence and is seen in the skin lesion judgements of medical students as well as practicing dermatologists. The goal of this project is to understand these serial dependencies so that we can reduce them in clinical practice. Implementation of this project involves eye tracking, deep learning models, and human psychophysics data.
Role: The research apprentice will be involved in designing stimuli, running experiments, collecting data and analyzing data. Students will be trained in research methods, statistics, and eye tracking. Students will be responsible for writing Python code and applying statistical toolboxes to analyze the data. The research apprentice will be also expected to read the relevant literature and write reports on the ongoing results of the project. The student will also meet with the supervisor weekly to discuss preliminary data and background literature, in addition to the opportunity to attend weekly lab meetings.
Most tasks and meetings will be remote, but some in-person data collection will be required.
Qualifications: Required: High motivation, interest in visual perception and eye tracking, basic programming skills, ability to work independently on certain projects.
Desirable but not essential: basic knowledge of Adobe Photoshop; experience with programming language Python; basic knowledge of neural networks
Day-to-day supervisor for this project: Haley Frey, Graduate Student
Hours: to be negotiated
Social Sciences Digital Humanities and Data Science Education, Cognition & Psychology