Coffee has a color, smell, you can drink it—coffee makes you feel good—it has sensory, emotional, and social aspects …
What do you feel when you think about the word coffee? Love it or hate it, you have sensory and experientially based feelings of coffee that give rise to what and how you think about coffee. These feelings are like subconscious memories that are activated even if you’re not consciously aware of them every time you encounter the word. It turns out that taking these feelings into account helped scientists create a more accurate model of how the brain represents words than previous studies that did not.
The strength of association of each word and its attributes allowed us to estimate how its meanings would be represented across the brain … which made it easier to interpret the relationship between the predictive model and brain activity patterns.”
The team was then able to recombine activity patterns for individual words, in order to predict brain patterns for entire sentences built up out of new combinations of those words
Sentences Show Up as Predictable Patterns in Your Brain | Futurity: Monique Patenaude
We introduce an approach that predicts neural representations of word meanings contained in sentences then superposes these to predict neural representations of new sentences. A neurobiological semantic model based on sensory, motor, social, emotional, and cognitive attributes was used as a foundation to define semantic content. Previous studies have predominantly predicted neural patterns for isolated words, using models that lack neurobiological interpretation. … The results show how a neurobiologically motivated semantic model can decompose sentence-level fMRI data into activation features for component words, which can be recombined to predict activation patterns for new sentences.
Predicting Neural Activity Patterns Associated with Sentences Using a Neurobiologically Motivated Model of Semantic Representation |
Cerebral Cortex: Andrew James Anderson • Jeffrey R. Binder • Leonardo Fernandino • Colin J. Humphries • Lisa L. Conant • Mario Aguilar • Xixi Wang • Donias Doko • Rajeev D. S. Raizada