README

This folder contains a python file with the necessary functions and data to carry out the experiment reported in Cassani et al (2016). Details about the experiment are included in the cited paper while the python (.py) file implements four different computational models of cross-situational word learning:

- Hebbian learning (Hebb, 1949)
- Naïve Discriminative Learning (Baayen et al, 2011)
- Probabilistic learning (Fazly et al, 2010)
- Hypothesis Testing Model (Trueswell et al, 2013)

while the input file that we provide reproduces the task from Ramscar et al (2013) in which cross-situational learning was investigated in children and adults using a simple paradigm - see the cited reference for details about the procedure and the findings therein. Details about the format of the input file are provided in the comments to the code.

For any question or doubt, please mail me at [email protected].

If you use this software for your own experiments, please cite this reference:

Cassani G., Grimm R., Gillis S., and Daelemans W. (2016) "Constraining the search space in cross-situational word learning: Different models make different predictions." In Papafragou, A., Grodner, D., Mirman, D., & Trueswell, J.C. (Eds.), Proceedings of the 38th Annual Meeting of the Cognitive Science Society, Philadelphia, PA.