Mapping between fMRI responses to movies and their natural language annotations

• DOI: 10.1016/j.neuroimage.2017.06.042
• Publication Date: 2017-06-23T10:00:41Z
• Authors: Kiran Vodrahalli; Po-Hsuan Chen; Yingyu Liang; Christopher Balda...; Janice Chen; Esther Yong; Christopher Honey; Uri Hasson; Peter Ramadge; Kenneth A. Norman; Sanjeev Arora
• Keywords: FOS: Computer and information sciences; 0301 basic medicine; Computer Science - Machine Learning; Brain Mapping; Computer Science - Computation and Language; Motion Pictures; Brain; Magnetic Resonance Imaging; Semantics; Machine Learning (cs.LG); 03 medical and health sciences; Quantitative Biology - Neurons and Cognition; FOS: Biological sciences; Image Processing, Computer-Assisted; Humans; Neurons and Cognition (q-bio.NC); Computation and Language (cs.CL); Language; Natural Language Processing

Several research groups have shown how to correlate fMRI responses to the meanings of presented stimuli. This paper presents new methods for doing so when only a natural language annotation is available as the description of the stimulus. We study fMRI data gathered from subjects watching an episode of BBCs Sherlock [1], and learn bidirectional mappings between fMRI responses and natural language representations. We show how to leverage data from multiple subjects watching the same movie to improve the accuracy of the mappings, allowing us to succeed at a scene classification task with 72% accuracy (random guessing would give 4%) and at a scene ranking task with average rank in the top 4% (random guessing would give 50%). The key ingredients are (a) the use of the Shared Response Model (SRM) and its variant SRM-ICA [2, 3] to aggregate fMRI data from multiple subjects, both of which are shown to be superior to standard PCA in producing low-dimensional representations for the tasks in this paper; (b) a sentence embedding technique adapted from the natural language processing (NLP) literature [4] that produces semantic vector representation of the annotations; (c) using previous timestep information in the featurization of the predictor data.<br/>19 pages, 9 figures, in submission to NeuroImage. Prior version presented at MLINI-2016 workshop, 2016 (arXiv:1701.01437) and ICML 2016 Workshop on Multi-view Representation Learning<br/>