Abstract
conceptual representations are critical for human cognition. Despite their importance, key properties of these representations remain poorly understood. Here, we used computational models of distributional semantics to predict multivariate fMRI activity patterns during the activation and contextualization of abstract concepts. We devised a task in which participants had to embed abstract nouns into a story that they developed around a given background context. We found that representations in inferior parietal cortex were predicted by concept similarities emerging in models of distributional semantics. By constructing different model families, we reveal the models’ learning trajectories and delineate how abstract and concrete training materials contribute to the formation of brain-like representations. These results inform theories about the format and emergence of abstract conceptual representations in the human brain.
Highlights
The use of conceptual knowledge is one of the foundations of human intelligence
We used an representational similarity analysis (RSA) searchlight approach, in which we extracted similarity relations among the words across the whole cortex (Fig 1B; see Materials and Methods). We modelled these similarities using a word2vec model of distributional semantics [13], trained on a 45-million sentence corpus (SdeWaC [14])
Our findings yield multiple key insights into abstract concept representation: First, our findings provide novel evidence that the inferior parietal cortex (IPC) is a core area for concept coding [1]
Summary
How do we conceive abstract concepts, like love, peace, or truth? In this study, we investigate how our brains support the activation and contextualization of such abstract concepts. We computed how similar different abstract concepts were represented during this task. We modelled these neural similarities among concepts with computational models of distributional semantics which capture the words’ co-occurance statistics in large natural language corpora. Our results reveal a correspondence between the computational models and brain representations in the inferior parietal cortex. This correspondence held when the computational models were only trained on subsets of the corpora that contained as few as 100,000 sentences and only abstract or concrete words.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
