Defining and structure of cross-situational learning (XSL) datasets. | 0. Importing a Dataset | 1. Structure of an experimental condition | 2. Viewing an experiment's structure | 3. Included experimental conditions | Running, fitting, and defining XSL models. | 4. Run model with given parameters through a single dataset and pull SSE. | 5. Run a model with given parameters through multiple datasets and pull SSE. | 6. Evaluating model performance | 7. Using the helper functions | 8. Model fitting | 9. Available models | 10. Prior learning: training trials or a start matrix | 11. Testing generalization on held-out datasets

Running a model | Scoring against the gold lexicon | The intentional Bayesian model | "Fitting": sweeping the lexicon-size prior | The FM corpus and its coded intentions | A quick model comparison | Fitting to a corpus | References

Defining and structure of cross-situational learning (XSL) datasets. | 0. Importing a Dataset | 1. Structure of an experimental condition | 2. Viewing an experiment's structure | 3. Included experimental conditions | Running, fitting, and defining XSL models. | 4. Run model with given parameters through a single dataset and pull SSE. | 5. Run a model with given parameters through multiple datasets and pull SSE. | 6. Evaluating model performance | 7. Using the helper functions | 8. Model fitting | 9. Available models | 10. Prior learning: training trials or a start matrix | 11. Testing generalization on held-out datasets

Running a model | Scoring against the gold lexicon | The intentional Bayesian model | "Fitting": sweeping the lexicon-size prior | The FM corpus and its coded intentions | A quick model comparison | Fitting to a corpus | References