The Compositional Nature of Verb and Argument Representations in the Human Brain

TL;DR

This study demonstrates that human brain activity patterns can be decoded to identify complex event compositions involving actors, actions, and objects, revealing neural bases of language-like compositionality.

Contribution

The paper introduces a novel approach to decode complex event representations in the brain, including compositional structures, using fMRI data and machine learning.

Findings

Successfully decoded action labels from brain activity

Identified common brain areas involved in action and object recognition

Achieved compositional decoding of complex events with multiple components

Abstract

How does the human brain represent simple compositions of objects, actors,and actions? We had subjects view action sequence videos during neuroimaging (fMRI) sessions and identified lexical descriptions of those videos by decoding (SVM) the brain representations based only on their fMRI activation patterns. As a precursor to this result, we had demonstrated that we could reliably and with high probability decode action labels corresponding to one of six action videos (dig, walk, etc.), again while subjects viewed the action sequence during scanning (fMRI). This result was replicated at two different brain imaging sites with common protocols but different subjects, showing common brain areas, including areas known for episodic memory (PHG, MTL, high level visual pathways, etc.,i.e. the 'what' and 'where' systems, and TPJ, i.e. 'theory of mind'). Given these results, we were also able to successfully show a key aspect of language compositionality based on simultaneous decoding of object class and actor identity. Finally, combining these novel steps in 'brain reading' allowed us to accurately estimate brain representations supporting compositional decoding of a complex event composed of an actor, a verb, a direction, and an object.