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)

首先审阅链接的 efficient-learning-data dataset 和 Soch et al. 的 Bayesian TCM 论文，然后决定要研究哪个预测目标和模型范围。如果已经为选定的目标（例如总体 recall、clustering、个体 recall 或思维轨迹）制定了明确的建模计划并取得结果，即可视为完成；Howard 和 Kahana dataset 是另一个可能的研究范围。

由索引模型根据 Issue 内容生成。