Lily Kelemen discusses how traditional methods of studying facial expressions use static stimuli, which may not accurately reflect real-life scenarios. In her research, she emphasizes using more naturalistic images from the Wild Faces Database to analyze expressions and interactions. Participants are tasked with identifying the odd image from groups of three, based on perceived gender or emotional expressions. The behavioral data from this task is analyzed through an algorithm, resulting in a multidimensional scaling plot that visually represents the similarity of the facial images studied.
These are examples of stimuli that have been used in the past to study facial emotion expression, but when you look at these images, there's something a little bit off.
Instead in this project, I'm focusing on using more naturalistic stimuli... the faces aren't always oriented directly 90 degrees at the camera, and the expressions aren't always in a fixed posed position.
We ask participants to look at groups of three of these thousand images and pick the odd one out. Now, there's lots of ways you could do this.
We fed that behavioral data into an algorithm, and that gave us this multidimensional scaling plot, which put the images that were rated more similar, closer together.
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