Efficient learning free recall dataset
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 15/100
- Issue type
- Feature
- Clarity
- Needs clarification
- Activity status
- Stale
- Domain
- data, machine-learning
Research direction
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.
Description
Dataset
Free recall data from a bunch of different free recall variants. Download link.
Things to do
-
Bayesian version of TCM, like this one
-
Maybe add a multiple timescales component?
-
Predict:
- Which words are recalled overall
- Clustering (memory fingerprints)
- Individual recalls
- Thought trajectories
-
could also model this dataset-- 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.
- Dominant language
- Jupyter Notebook
- Stars
- 17
- Forks
- 4
- PR merge metrics
- No merged PRs in 30d
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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