The typology of lexicalizations in natural languages is highly skewed: some meanings repeatedly receive their own expression as individual morphemes or words in language after language, while many other meanings rarely or never do. For example, while many languages have monomorphemic counterparts of English some and all, no known language has a monomorphemic quantifier that means all or none or a quantifier that asserts that its two arguments are of the same cardinality. It seems tempting to reason from this typological skew to properties of stored representations. However, it is not generally safe to assume that if something is typologically unattested then it simply cannot be represented or learned. The representational system for stored denotations is just one of several interacting factors that affect the typology, and other factors such as communicative pressure and learnability are likely to shape patterns of lexicalization. In this paper we propose to reason from the typology to stored representations by modeling the representational framework, communicative pressure, and learnability directly within an evolutionary model, building on work by Brochhagen, Franke at al (https://onlinelibrary.wiley.com/doi/full/10.1111/cogs.12681). Our empirical focus is a lexicalization asymmetry noted by Horn (1972) in the domain of logical operators and framed within the Aristotelian Square of Opposition. We show that, on certain assumptions, Horn's lexicalization pattern depends on very particular representational costs in the lexicon: it arises if the storage costs for every and some are lower than those for not every and not some but not otherwise.
In order to run tests, clone the repository and cd into the
rmd_square folder.
cd into the root directory of the project, create a virtual environment and install the requirements.
cd RMD
python3 -m venv env
source env/bin/activate
pip3 install -r requirements.txtFor running the experiments, cd into the project folder RMD and execute main.py + path_to_yaml:
python3 main.py path_to_yaml.yamlFor example:
python3 main.py configs/example.yamlYou can find the example file example.yaml in the configs folder. Use this one or copy it and adjust it for your needs.
The results get stored in a folder (name specified in the yaml).
You can define your own cost functions if you set cost in the yaml to new_approach and adjust the cost function in message_cost.py at new_approach.
The folder structure is as follows:
RMD
├── configs
│ └── example.yaml
├── irrelevant
│ └── process_predefined_lexica.py
├── main.py
├── py_scripts
│ ├── checks.py
│ ├── __init__.py
│ ├── lexica.py
│ ├── message_costs.py
│ ├── mutation_matrix.py
│ ├── mutual_utility.py
│ ├── player.py
│ ├── plots
│ │ ├── __init__.py
│ │ ├── plot_progress.py
│ │ ├── proportion_target_types.py
│ │ └── x_best_prag_lit.py
│ ├── rmd.py
│ ├── uegaki_utility.py
│ └── utils.py
├── README.md
└── requirements.txt