Why Some Images Are More Memorable than Others
A new study from researchers at Yale University helps demystify why some images stick out in people’s memories more than others.
It’s said that “man bites dog” makes the news while “dog bites man” does not. The latter is unfortunate but not surprising, but the former is very surprising. The same principle seems to apply to images and which ones are more memorable.
“The mind prioritizes remembering things that it is not able to explain very well,” says Ilker Yildirim, an assistant professor of psychology at Yale and senior author of the paper. “If a scene is predictable, and not surprising, it might be ignored.”
The study, published in Nature Human Behavior, examines how the brain prioritizes certain pieces of information within the onslaught of data people are inundated with daily without thought “by pairing a computational model of scene complexity with a behavioral study.”
“For the study, which was led by Yildirim and John Lafferty, the John C. Malone Professor of Statistics and Data Science at Yale,” a press release details. “The researchers developed a computational model that addressed two steps in memory formation — the compression of visual signals and their reconstruction.”
From here, the study includes “a series of experiments in which people were asked if they remembered specific images from a sequence of natural images shown in rapid succession.” This is where the idea of what is memorable is explored. The researchers found that the more difficult it was for the computational model to recreate an image, likely because it was more complicated to explain or make sense of, the more likely the study participants were to remember it.
Yale’s press release provides an example of a fire hydrant in a remote natural environment, which places an item typically found in a more urban landscape where its function or placement makes less sense.
While this can possibly help determine what photos can stand out, it also marks a step in artificial intelligence development.
“We used an AI model to try to shed light on perception of scenes by people,” Lafferty says. “This understanding could help in the development of more efficient memory systems for AI in the future.”
Image credits: Header photo licensed via Depositphotos.
