Distributed neural encoding of binding to thematic roles

TL;DR

This paper introduces a novel fMRI analysis method that models the neural encoding of complex linguistic structures, capturing the positional contributions of constituents and revealing overlapping neural representations of thematic role bindings.

Contribution

It presents a new superposition-based approach that accounts for constituent positions in neural encoding, advancing understanding of linguistic structure representation in the brain.

Findings

Neural representations of thematic role binding are non-orthogonal and overlapping.

The method distinguishes between different models of neural composition.

Reanalysis of existing data reveals insights into neural encoding of linguistic structure.

Abstract

A framework and method are proposed for the study of constituent composition in fMRI. The method produces estimates of neural patterns encoding complex linguistic structures, under the assumption that the contributions of individual constituents are additive. Like usual techniques for modeling compositional structure in fMRI, the proposed method employs pattern superposition to synthesize complex structures from their parts. Unlike these techniques, superpositions are sensitive to the structural positions of constituents, making them irreducible to structure-indiscriminate ("bag-of-words") models of composition. Reanalyzing data from a study by Frankland and Greene (2015), it is shown that comparison of neural predictive models with differing specifications can illuminate aspects of neural representational contents that are not apparent when composition is not modelled. The results indicate that the neural instantiations of the binding of fillers to thematic roles in a sentence are non-orthogonal, and therefore spatially overlapping.