# How trial-to-trial learning shapes mappings in the mental lexicon: Modelling Lexical Decision with Linear Discriminative Learning

arXiv: https://arxiv.org/abs/2207.00430 · HTML: https://mlsanity.com/paper/2207.00430/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 190 references that are in the map.
- First posted on arXiv 1 Jul 2022.

## Builds on (most cited first, 8 of 8)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Learning Zero-Shot Multifaceted Visually Grounded Word Embeddings via Multi-Task Training](https://mlsanity.com/paper/2104.07500/) (2021)
- [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)
- [A note on the modeling of the effects of experimental time in psycholinguistic experiments](https://mlsanity.com/paper/2105.13786/) (2021)
- [Deep Predictive Learning in Neocortex and Pulvinar](https://mlsanity.com/paper/2006.14800/) (2020)
- [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)
- [Generating subtour elimination constraints for the TSP from pure integer solutions](https://mlsanity.com/paper/1511.03533/) (2015)

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

- [Word-specific tonal realizations in Mandarin](https://mlsanity.com/paper/2405.07006/) (2024)
- [Frequency effects in Linear Discriminative Learning](https://mlsanity.com/paper/2306.11044/) (2023)
- [Is deeper always better? Replacing linear mappings with deep learning networks in the Discriminative Lexicon Model](https://mlsanity.com/paper/2410.04259/) (2024)
- [An experimental and computational study of an Estonian single-person word naming](https://mlsanity.com/paper/2509.03143/) (2025)

## Abstract (arXiv)

Trial-to-trial effects have been found in a number of studies, indicating that processing a stimulus influences responses in subsequent trials. A special case are priming effects which have been modelled successfully with error-driven learning (Marsolek, 2008), implying that participants are continuously learning during experiments. This study investigates whether trial-to-trial learning can be detected in an unprimed lexical decision experiment. We used the Discriminative Lexicon Model (DLM; Baayen et al., 2019), a model of the mental lexicon with meaning representations from distributional semantics, which models error-driven incremental learning with the Widrow-Hoff rule. We used data from the British Lexicon Project (BLP; Keuleers et al., 2012) and simulated the lexical decision experiment with the DLM on a trial-by-trial basis for each subject individually. Then, reaction times were predicted with Generalised Additive Models (GAMs), using measures derived from the DLM simulations as predictors. We extracted measures from two simulations per subject (one with learning updates between trials and one without), and used them as input to two GAMs. Learning-based models showed better model fit than the non-learning ones for the majority of subjects. Our measures also provide insights into lexical processing and individual differences. This demonstrates the potential of the DLM to model behavioural data and leads to the conclusion that trial-to-trial learning can indeed be detected in unprimed lexical decision. Our results support the possibility that our lexical knowledge is subject to continuous changes.