# Distributed neural encoding of binding to thematic roles

arXiv: https://arxiv.org/abs/2110.12342 · HTML: https://mlsanity.com/paper/2110.12342/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 24 Oct 2021.

## Builds on (most cited first, 2 of 2)

- [Assessing Composition in Sentence Vector Representations](https://mlsanity.com/paper/1809.03992/) (2018)
- [Searchlight-based multi-voxel pattern analysis of fMRI by cross-validated MANOVA](https://mlsanity.com/paper/1401.4122/) (2014)

## Abstract (arXiv)

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.