# Frequency effects in Linear Discriminative Learning

arXiv: https://arxiv.org/abs/2306.11044 · HTML: https://mlsanity.com/paper/2306.11044/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 108 references that are in the map.
- First posted on arXiv 19 Jun 2023.

## Builds on (most cited first, 5 of 5)

- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [Modeling morphology with Linear Discriminative Learning: considerations and design choices](https://mlsanity.com/paper/2106.07936/) (2021)
- [Vector Space Morphology with Linear Discriminative Learning](https://mlsanity.com/paper/2107.03950/) (2021)
- [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)
- [Keeping it simple: Implementation and performance of the proto-principle of adaptation and learning in the language sciences](https://mlsanity.com/paper/2003.03813/) (2020)

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

- [Word-specific tonal realizations in Mandarin](https://mlsanity.com/paper/2405.07006/) (2024)
- [Is deeper always better? Replacing linear mappings with deep learning networks in the Discriminative Lexicon Model](https://mlsanity.com/paper/2410.04259/) (2024)
- [Transformers perform adaptive partial pooling](https://mlsanity.com/paper/2602.03980/) (2026)
- [Hard to Be Heard: Phoneme-Level ASR Analysis of Phonologically Complex, Low-Resource Endangered Languages](https://mlsanity.com/paper/2604.18204/) (2026)

## Abstract (arXiv)

Word frequency is a strong predictor in most lexical processing tasks. Thus, any model of word recognition needs to account for how word frequency effects arise. The Discriminative Lexicon Model (DLM; Baayen et al., 2018a, 2019) models lexical processing with linear mappings between words' forms and their meanings. So far, the mappings can either be obtained incrementally via error-driven learning, a computationally expensive process able to capture frequency effects, or in an efficient, but frequency-agnostic solution modelling the theoretical endstate of learning (EL) where all words are learned optimally. In this study we show how an efficient, yet frequency-informed mapping between form and meaning can be obtained (Frequency-informed learning; FIL). We find that FIL well approximates an incremental solution while being computationally much cheaper. FIL shows a relatively low type- and high token-accuracy, demonstrating that the model is able to process most word tokens encountered by speakers in daily life correctly. We use FIL to model reaction times in the Dutch Lexicon Project (Keuleers et al., 2010) and find that FIL predicts well the S-shaped relationship between frequency and the mean of reaction times but underestimates the variance of reaction times for low frequency words. FIL is also better able to account for priming effects in an auditory lexical decision task in Mandarin Chinese (Lee, 2007), compared to EL. Finally, we used ordered data from CHILDES (Brown, 1973; Demuth et al., 2006) to compare mappings obtained with FIL and incremental learning. The mappings are highly correlated, but with FIL some nuances based on word ordering effects are lost. Our results show how frequency effects in a learning model can be simulated efficiently, and raise questions about how to best account for low-frequency words in cognitive models.