id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_hc4up7ghr5dpre425mbxpgzlk4	Andrew J. Anderson	Visually Grounded and Textual Semantic Models Differentially Decode Brain Activity Associated with Concrete and Abstract Nouns	2017	14	.pdf	application/pdf	7624	625	55	using computational semantic models to decode brain activity patterns associated with Having both imageand text-based models of semantic representation, and neural activity patterns unclear whether either textor image-based semantic models can decode neural activity patterns associated with abstract words. text-based computational semantic models to decode an fMRI data set spanning a diverse set of analysis we split the fMRI data set into the most concrete and most abstract words based on behavioural The image-based model is built using a deep convolutional neural network approach, similar in nature to those recently used to study neural representations of visual stimuli (see Kriegeskorte (2015), although note this is the first application to study word we were able to compare how well English and Italian text-based semantic models can decode neural 2010; Darwish, 2013) we combined Italian and English text-based models in our decoding analyses in decode the more abstract nouns' neural activity patterns with higher accuracy than the image-based	./cache/work_hc4up7ghr5dpre425mbxpgzlk4.pdf	./txt/work_hc4up7ghr5dpre425mbxpgzlk4.txt