# An experimental and computational study of an Estonian single-person word naming

arXiv: https://arxiv.org/abs/2509.03143 · HTML: https://mlsanity.com/paper/2509.03143/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 102 references that are in the map.
- First posted on arXiv 3 Sep 2025.

## Builds on (most cited first, 10 of 10)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [Word-specific tonal realizations in Mandarin](https://mlsanity.com/paper/2405.07006/) (2024)
- [Vector Space Morphology with Linear Discriminative Learning](https://mlsanity.com/paper/2107.03950/) (2021)
- [The realization of tones in spontaneous spoken Taiwan Mandarin: a corpus-based survey and theory-driven computational modeling](https://mlsanity.com/paper/2503.23163/) (2025)
- [A note on the modeling of the effects of experimental time in psycholinguistic experiments](https://mlsanity.com/paper/2105.13786/) (2021)
- [Scalable visualisation methods for modern Generalized Additive Models](https://mlsanity.com/paper/1809.10632/) (2018)
- [How trial-to-trial learning shapes mappings in the mental lexicon: Modelling Lexical Decision with Linear Discriminative Learning](https://mlsanity.com/paper/2207.00430/) (2022)
- [Making sense of spoken plurals](https://mlsanity.com/paper/2207.01947/) (2022)
- [Historical and psycholinguistic perspectives on morphological productivity: A sketch of an integrative approach](https://mlsanity.com/paper/2505.12071/) (2025)

## Abstract (arXiv)

This study investigates lexical processing in Estonian. A large-scale single-subject experiment is reported that combines the word naming task with eye-tracking. Five response variables (first fixation duration, total fixation duration, number of fixations, word naming latency, and spoken word duration) are analyzed with the generalized additive model. Of central interest is the question of whether measures for lexical processing generated by a computational model of the mental lexicon (the Discriminative Lexicon Model, DLM) are predictive for these response variables, and how they compare to classical predictors such as word frequency, neighborhood size, and inflectional paradigm size. Computational models were implemented both with linear and deep mappings. Central findings are, first, that DLM-based measures are powerful predictors for lexical processing, second, that DLM-measures using deep learning are not necessarily more precise predictors of lexical processing than DLM-measures using linear mappings, third, that classical predictors tend to provide somewhat more precise fits compared to DLM-based predictors (except for total fixation duration, where the two provide equivalent goodness of fit), and fourth, that in the naming task lexical variables are not predictive for first fixation duration and the total number of fixations. As the DLM works with mappings from form to meaning, the predictivity of DLM-based measures for total fixation duration, naming latencies, and spoken word duration indicates that meaning is heavily involved in the present word naming task.