# On the Computational Modeling of Meaning: Embodied Cognition Intertwined with Emotion

arXiv: https://arxiv.org/abs/2307.04518 · HTML: https://mlsanity.com/paper/2307.04518/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 49 references that are in the map.
- First posted on arXiv 10 Jul 2023.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Experience Grounds Language](https://mlsanity.com/paper/2004.10151/) (2020)
- [The Latent Structure of Dictionaries](https://mlsanity.com/paper/1411.0129/) (2014)
- [What A Situated Language-Using Agent Must be Able to Do: A Top-Down Analysis](https://mlsanity.com/paper/2302.08590/) (2023)
- [Vision Language Transformers: A Survey](https://mlsanity.com/paper/2307.03254/) (2023)

## Built on by (most cited first, 1 of 1)

- [A Primer on Computational Semantics for Artificial Intelligence Systems](https://mlsanity.com/paper/2608.25022/) (2026)

## Abstract (arXiv)

This document chronicles this author's attempt to explore how words come to mean what they do, with a particular focus on child language acquisition and what that means for models of language understanding.footnoteI say historical because I synthesize the ideas based on when I discovered them and how those ideas influenced my later thinking. I explain the setting for child language learning, how embodiment -- being able to perceive and enact in the world, including knowledge of concrete and abstract concepts -- is crucial, and how emotion and cognition relate to each other and the language learning process. I end with what I think are some of the requirements for a language-learning agent that learns language in a setting similar to that of children. This paper can act as a potential guide for ongoing and future work in modeling language.