Predicting Human Brain Activity Associated with the Meanings of Nouns

Abstract

The question of how the human brain represents conceptual knowledge has been debated in many scientific fields. Brain imaging studies have shown that different spatial patterns of neural activation are associated with thinking about different semantic categories of pictures and words (for example, tools, buildings, and animals). We present a computational model that predicts the functional magnetic resonance imaging (fMRI) neural activation associated with words for which fMRI data are not yet available. This model is trained with a combination of data from a trillion-word text corpus and observed fMRI data associated with viewing several dozen concrete nouns. Once trained, the model predicts fMRI activation for thousands of other concrete nouns in the text corpus, with highly significant accuracies over the 60 nouns for which we currently have fMRI data.

Commonality of neural representations of sentences across languages: Predicting brain activation during Portuguese sentence comprehension using an English-based model of brain function

“…One of the new insights emerging about human brain function since the advent of fMRI is that individual concepts have identifiable neural signatures (Mitchell et al, 2008), and furthermore, that there is a high degree of commonality of such signatures across people (Just, Cherkassky, Aryal, & Mitchell, 2010). Particularly germane to this study are previous investigations of the commonality of neural representations of concepts across different languages.…”

“…First, it has been possible to develop predictive models, rather than merely discriminative models, of the neural representations of concepts (Just et al, 2010;Mitchell et al, 2008). Discriminative models simply provide a mapping between stimulus items and brain activation patterns.…”

“…By contrast, semantic vector representations of concepts (e.g. Latent Semantic Analysis, (Dumais, 2004)) have limited linguistic interpretability, although the use of such representations has been shown to be predictive of neural activation (e.g., Mitchell et al, 2008;Murphy et al, 2012;Schloss & Li, 2016).…”

Quantitative modeling of the neural representation of objects: How semantic feature norms can account for fMRI activation

“…For example, it is not clear from the analyses above whether a different set of 25 verbs might not provide a better account. To address these issues, additional modeling was done with corpus cooccurrence features using the 485 most frequent verbs in the corpus (including the 25 sensorimotor verbs reported in Mitchell et al, 2008). A greedy algorithm was used to determine the 25 verbs among the 485 that optimize the regression fit.…”

Reading others’ minds by measuring their brains: Fascinating and challenging for science, but ready for use in court?

“…However, these findings were obtained with very simple tasks of imagining highly specific movements or visual categories. More sophisticated mind-reading in terms of measuring the content of silent speech of nouns (Mitchell et al, 2008) or visual processing is rapidly developing but is still far away from what is expected of a true mind-reading machine (Logothetis, 2008). Table 1.…”