Topic: memorability

Previous work looking at individual differences in face identity processing (FIP) has found that most commonly used lab-based performance assessments are unfortunately not sufficiently sensitive on their own for measuring performance in both the upper and lower tails of the general population simultaneously. So more recently, researchers have begun incorporating multiple testing procedures into their assessments. Still, though, the growing consensus seems to be that at the individual level, there is quite a bit of variability between test scores. The overall consequence of this is that extreme

There is a surprising consistency in the images we remember and forget – across observers, certain images are intrinsically more memorable than others in spite of our diverse individual experiences. The perception of images at different memorability levels also results in stereotyped patterns in visual and mnemonic regions in the brain, regardless of an individual’s actual memory for that item. In this talk, Dr. Bainbridge will discuss our current neuroscientific understanding of how memorability is represented in patterns in the brain, potentially serving as a signal for how stimulus informat

Understanding how visual attributes influence cognitive properties, such as memorability and emotional valence, is crucial for advancements in computational neuroscience and AI-driven image processing. Previous work has introduced a research framework that uses generative models to learn the complex nonlinear mapping between visual attributes and cognitive properties (Goetschalckx et al., 2019). This approach identifies transformations in the latent space of a generative model, which, when applied, generate images with tailored visual attributes that can enhance or diminish targeted cognitive