Project Website http://cvcl.mit.edu/modeling_attention.html
Understanding cognition on an individual level facilitates communication between natural and artificial systems, resulting in improved interfaces, devices, and neuroprosthetics for healthy and disabled people. Our work has identified that events carry the attribute of memorability, a predictive value of whether a novel event will be later remembered or forgotten. Predicting memorability is not an inexplicable phenomenon: people have a tendency to remember and forget the same images, faces, words, and graphs. Importantly, we are developing computational models that predict what people will remember, as they are encoding an event or even before they witness an event. Cognitive-level algorithms of memory will be a game changer for society, with applications ranging from accurate medical diagnostic tools to educational materials that will foresee the needs of people, to compensate when cognition fails.