# Measuring individual semantic networks: A simulation study

arXiv: https://arxiv.org/abs/2410.18326 · HTML: https://mlsanity.com/paper/2410.18326/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 48 references that are in the map.
- First posted on arXiv 23 Oct 2024.

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

- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Advances in Pre-Training Distributed Word Representations](https://mlsanity.com/paper/1712.09405/) (2017)

## Built on by (most cited first, 2 of 2)

- [From Associations to Activations: Comparing Behavioral and Hidden-State Semantic Geometry in LLMs](https://mlsanity.com/paper/2602.00628/) (2026)
- [The "Small World of Words" German Free-Association Norms](https://mlsanity.com/paper/2604.19620/) (2026)

## Abstract (arXiv)

Accurately capturing individual differences in semantic networks is fundamental to advancing our mechanistic understanding of semantic memory. Past empirical attempts to construct individual-level semantic networks from behavioral paradigms may be limited by data constraints. To assess these limitations and propose improved designs for the measurement of individual semantic networks, we conducted a recovery simulation investigating the psychometric properties underlying estimates of individual semantic networks obtained from two different behavioral paradigms: free associations and relatedness judgment tasks. Our results show that successful inference of semantic networks is achievable, but they also highlight critical challenges. Estimates of absolute network characteristics are severely biased, such that comparisons between behavioral paradigms and different design configurations are often not meaningful. However, comparisons within a given paradigm and design configuration can be accurate and generalizable when based on designs with moderate numbers of cues, moderate numbers of responses, and cue sets including diverse words. Ultimately, our results provide insights that help evaluate past findings on the structure of semantic networks and design new studies capable of more reliably revealing individual differences in semantic networks.