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Several research groups have shown how to correlate fMRI responses to themeanings of presented stimuli. This paper presents new methods for doing sowhen only a natural language annotation is available as the description of thestimulus. We study fMRI data gathered from subjects watching an episode of BBCsSherlock [1], and learn bidirectional mappings between fMRI responses andnatural language representations. We show how to leverage data from multiplesubjects watching the same movie to improve the accuracy of the mappings,allowing us to succeed at a scene classification task with 72% accuracy (randomguessing would give 4%) and at a scene ranking task with average rank in thetop 4% (random guessing would give 50%). The key ingredients are (a) the use ofthe Shared Response Model (SRM) and its variant SRM-ICA [2, 3] to aggregatefMRI data from multiple subjects, both of which are shown to be superior tostandard PCA in producing low-dimensional representations for the tasks in thispaper; (b) a sentence embedding technique adapted from the natural languageprocessing (NLP) literature [4] that produces semantic vector representation ofthe annotations; (c) using previous timestep information in the featurizationof the predictor data.

Mapping Between fMRI Responses to Movies and their Natural Language Annotations