# Production vs Perception: The Role of Individuality in Usage-Based Grammar Induction

arXiv: https://arxiv.org/abs/2104.09033 · HTML: https://mlsanity.com/paper/2104.09033/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 35 references that are in the map.
- First posted on arXiv 19 Apr 2021.

## Builds on (most cited first, 7 of 7)

- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [Modeling Global Syntactic Variation in English Using Dialect Classification](https://mlsanity.com/paper/1904.05527/) (2019)
- [Frequency vs. Association for Constraint Selection in Usage-Based Construction Grammar](https://mlsanity.com/paper/1904.05529/) (2019)
- [Finding Variants for Construction-Based Dialectometry: A Corpus-Based Approach to Regional CxGs](https://mlsanity.com/paper/2104.01299/) (2021)
- [Global Syntactic Variation in Seven Languages: Towards a Computational Dialectology](https://mlsanity.com/paper/2104.01306/) (2021)
- [A Robust Transformation-Based Learning Approach Using Ripple Down Rules for Part-of-Speech Tagging](https://mlsanity.com/paper/1412.4021/) (2014)
- [Modeling the Complexity and Descriptive Adequacy of Construction Grammars](https://mlsanity.com/paper/1904.05588/) (2019)

## Built on by (most cited first, 6 of 6)

- [Learned Construction Grammars Converge Across Registers Given Increased Exposure](https://mlsanity.com/paper/2110.05663/) (2021)
- [Exposure and Emergence in Usage-Based Grammar: Computational Experiments in 35 Languages](https://mlsanity.com/paper/2211.14160/) (2022)
- [Stability of Syntactic Dialect Classification Over Space and Time](https://mlsanity.com/paper/2209.04958/) (2022)
- [Exploring the Constructicon: Linguistic Analysis of a Computational CxG](https://mlsanity.com/paper/2301.12642/) (2023)
- [Syntactic Variation Across the Grammar: Modelling a Complex Adaptive System](https://mlsanity.com/paper/2309.11869/) (2023)
- [LLMs Learn Constructions That Humans Do Not Know](https://mlsanity.com/paper/2508.16837/) (2025)

## Abstract (arXiv)

This paper asks whether a distinction between production-based and perception-based grammar induction influences either (i) the growth curve of grammars and lexicons or (ii) the similarity between representations learned from independent sub-sets of a corpus. A production-based model is trained on the usage of a single individual, thus simulating the grammatical knowledge of a single speaker. A perception-based model is trained on an aggregation of many individuals, thus simulating grammatical generalizations learned from exposure to many different speakers. To ensure robustness, the experiments are replicated across two registers of written English, with four additional registers reserved as a control. A set of three computational experiments shows that production-based grammars are significantly different from perception-based grammars across all conditions, with a steeper growth curve that can be explained by substantial inter-individual grammatical differences.