Neuroethics Publications

Document Type

Journal Article

Date of this Version

11-26-2012

Publication Source

Frontiers in Human Neuroscience

Volume

6

Start Page

Article 313

DOI

10.3389/fnhum.2012.00313

Abstract

What happens in the mind of a person who first hears a potentially exciting idea? We examined the neural precursors of spreading ideas with enthusiasm, and dissected enthusiasm into component processes that can be identified through automated linguistic analysis, gestalt human ratings of combined linguistic and non-verbal cues, and points of convergence/divergence between the two. We combined tools from natural language processing (NLP) with data gathered using fMRI to link the neurocognitive mechanisms that are set in motion during initial exposure to ideas and subsequent behaviors of these message communicators outside of the scanner. Participants' neural activity was recorded as they reviewed ideas for potential television show pilots. Participants' language from video-taped interviews collected post-scan was transcribed and given to an automated linguistic sentiment analysis (SA) classifier, which returned ratings for evaluative language (evaluative vs. descriptive) and valence (positive vs. negative). Separately, human coders rated the enthusiasm with which participants transmitted each idea. More positive sentiment ratings by the automated classifier were associated with activation in neural regions including medial prefrontal cortex; MPFC, precuneus/posterior cingulate cortex; PC/PCC, and medial temporal lobe; MTL. More evaluative, positive, descriptions were associated exclusively with neural activity in temporal-parietal junction (TPJ). Finally, human ratings indicative of more enthusiastic sentiment were associated with activation across these regions (MPFC, PC/PCC, DMPFC, TPJ, and MTL) as well as in ventral striatum (VS), inferior parietal lobule and premotor cortex. Taken together, these data demonstrate novel links between neural activity during initial idea encoding and the enthusiasm with which the ideas are subsequently delivered. This research lays the groundwork to use machine learning and neuroimaging data to study word of mouth communication and the spread of ideas in both traditional and new media environments.

Copyright/Permission Statement

This work is under a Creative Commons Attribution (CC BY) license 3.0 (http://creativecommons.org/licenses/by/3.0/).

Comments

At the time of publication, author Emily B. Falk was affiliated with University of Michigan. Currently, she is a faculty member at the the Annenberg School for Communication at the University of Pennsylvania.

Keywords

fMRI, sentiment analysis, natural language processing, information diffusion, word-of-mouth

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Date Posted: 16 February 2015

This document has been peer reviewed.