When driving in a snowstorm, where is the car in the lane next to you? When catching a baseball flying through the air, where is it relative to your hand? Many daily tasks require us to make these sorts of difficult perceptual judgments about where objects are located in the world. In doing so, we assume that people generally agree about what they saw (or more specifically, where they saw it). Research from our lab has shown that this isn’t always the case. Instead, people make consistent, idiosyncratic errors when judging the position of a briefly flashed spot on a screen. For example, one person might see a flash at 12 o’clock as being closer to 1 o’clock, while another would see it at 11 o’clock. These individual patterns, or “signatures” of error are generally stable over time, and consistent across different measurement methods. This project will investigate factors that may change these errors. Can people learn to correct their errors over time through visual feedback, and if so, how long do these changes last? In this project, we will develop computer-based tasks in which participants will be asked to report the locations of brief targets using a mouse or button press. For this project, you’ll work on all aspects of the process, from developing the experiment, to collecting the data and analyzing it using R or Matlab. No specific programming experience is required for this project; however, you should have an interest in learning to code.
Studying visual perception is an interdisciplinary endeavour, so one of the most exciting parts of mentorship is getting to work with students who have different backgrounds, interests, and skills. I find that it’s important to take an individualized approach, so that students get the research training that is appropriate to their experience. As a mentor, I try to understand each student’s goals through our individual meetings and work with them to help them develop the skills that they need to achieve them. Students working on this project will also have the opportunity to attend lab meetings to learn about ongoing projects in applied visual perception, and develop skills in programming and statistics. I always find it exciting to see students gain experience and confidence through the research process, and look forward to sharing my enthusiasm for vision research.