# Exploring Spatial Schema Intuitions in Large Language and Vision Models

arXiv: https://arxiv.org/abs/2402.00956 · HTML: https://mlsanity.com/paper/2402.00956/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 42 references that are in the map.
- First posted on arXiv 1 Feb 2024.

## Builds on (most cited first, 11 of 11)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies](https://mlsanity.com/paper/2208.10264/) (2022)
- [Large Language Models Sensitivity to The Order of Options in Multiple-Choice Questions](https://mlsanity.com/paper/2308.11483/) (2023)
- [What's "up" with vision-language models? Investigating their struggle with spatial reasoning](https://mlsanity.com/paper/2310.19785/) (2023)
- [Language models show human-like content effects on reasoning tasks](https://mlsanity.com/paper/2207.07051/) (2022)
- [Neural Language Models as Psycholinguistic Subjects: Representations of Syntactic State](https://mlsanity.com/paper/1903.03260/) (2019)
- [Syntactic Structure from Deep Learning](https://mlsanity.com/paper/2004.10827/) (2020)
- [GRASP: A novel benchmark for evaluating language GRounding And Situated Physics understanding in multimodal language models](https://mlsanity.com/paper/2311.09048/) (2023)
- [LMs stand their Ground: Investigating the Effect of Embodiment in Figurative Language Interpretation by Language Models](https://mlsanity.com/paper/2305.03445/) (2023)

## Built on by (most cited first, 4 of 4)

- [Large Language Models as Psychological Simulators: A Methodological Guide](https://mlsanity.com/paper/2506.16702/) (2025)
- [Are Large Language Models Aligned with People's Social Intuitions for Human-Robot Interactions?](https://mlsanity.com/paper/2403.05701/) (2024)
- [Red and blue language: Word choices in the Trump & Harris 2024 presidential debate](https://mlsanity.com/paper/2410.13654/) (2024)
- [Towards a Neurosymbolic Reasoning System Grounded in Schematic Representations](https://mlsanity.com/paper/2509.03644/) (2025)

## Abstract (arXiv)

Despite the ubiquity of large language models (LLMs) in AI research, the question of embodiment in LLMs remains underexplored, distinguishing them from embodied systems in robotics where sensory perception directly informs physical action. Our investigation navigates the intriguing terrain of whether LLMs, despite their non-embodied nature, effectively capture implicit human intuitions about fundamental, spatial building blocks of language. We employ insights from spatial cognitive foundations developed through early sensorimotor experiences, guiding our exploration through the reproduction of three psycholinguistic experiments. Surprisingly, correlations between model outputs and human responses emerge, revealing adaptability without a tangible connection to embodied experiences. Notable distinctions include polarized language model responses and reduced correlations in vision language models. This research contributes to a nuanced understanding of the interplay between language, spatial experiences, and the computations made by large language models. More at https://cisnlp.github.io/Spatial_Schemas/