Efficient learning free recall dataset

# Dataset

Free recall data from a bunch of different free recall variants. [Download link](https://github.com/ContextLab/efficient-learning-data).

# Things to do

- Bayesian version of TCM, like [this one](https://www.dropbox.com/s/nruwawbey8wu8nr/SochEtal09.pdf)
- Maybe add a multiple timescales component?
- Predict:
- Which words are recalled overall
- Clustering (memory fingerprints)
- Individual recalls
- Thought trajectories

- could also model [this dataset](http://memory.psych.upenn.edu/files/pubs/HowaKaha99.data.tgz)-- [Howard, M. W. and Kahana, M. J. (1999). Contextual variability and serial position effects in free recall. Journal of Experimental Psychology: Learning, Memory, and Cognition, 25(4), 923–941.](http://memory.psych.upenn.edu/files/pubs/HowaKaha99.pdf)

Start by reviewing the linked efficient-learning-data dataset and the Soch et al. Bayesian TCM paper, then decide which prediction target and model scope to pursue. Done would mean a defined modeling plan and results for selected targets such as overall recall, clustering, individual recalls, or thought trajectories; the Howard and Kahana dataset is an additional possible scope.

Written by the indexing model from the issue text.