quail.load_example_data

quail.load_example_data(dataset='automatic')

Loads example data

The automatic and manual example data are eggs containing 30 subjects who completed a free recall experiment as described here: https://psyarxiv.com/psh48/. The subjects studied 8 lists of 16 words each and then performed a free recall test.

The naturalistic example data is is an egg containing 17 subjects who viewed and verbally recounted an episode of the BBC series Sherlock, as described here: https://www.nature.com/articles/nn.4450. We fit a topic model to hand-annotated text-descriptions of scenes from the video and used the model to transform both the scene descriptions and manual transcriptions of each subject’s verbal recall. We then used a Hidden Markov Model to segment the video model and the recall models, by subject, into k events.

The cmr example data contains behavioral data from Polyn, Norman & Kahana (2009), “A Context Maintenance and Retrieval Model of Organizational Processes in Free Recall”, Psychological Review, Vol. 116 (1), 129-156. The dataset contains 45 subjects who studied lists of 24 words each using either SIZE or ANIMACY encoding tasks. List types include: SIZE-only (listType=0), ANIMACY-only (listType=1), and task-shift lists (listType=2) where participants alternated between encoding tasks. Features include: item (word), task (SIZE or ANIMACY), temporal (serial position), and wordpool_idx (index into the original wordpool).

The murd62 example data contains behavioral data from Murdock (1962), “The serial position effect of free recall”, Journal of Experimental Psychology, 64(5), 482-488. The dataset contains 7200 trials across 6 experimental conditions varying in list length (10, 15, 20, 30, or 40 items) and presentation rate (1 or 2 seconds per item). Conditions: LL10-2s, LL15-2s, LL20-1s, LL20-2s, LL30-1s, LL40-1s. Features include: item, temporal (serial position), list_length, rate, and condition.

The frfr example data contains behavioral data from a series of free recall experiments investigating how different word features affect memory organization. The dataset contains 452 subjects across 11 experimental conditions: feature-rich (all features varied), category, color, length, first-letter, location, size, adaptive, reduced, reduced-early, and reduced-late. Each subject studied 16 lists of 16 words. Features include: item, category, color, location, size, firstLetter, wordLength, temporal (serial position), condition (experiment type), and list_type (early or late lists). Reference: Heusser, A.C., Fitzpatrick, P.C., & Manning, J.R. (2018). How is experience transformed into memory? bioRxiv. https://doi.org/10.1101/409987

Parameters:

datasetstr

The dataset to load. Can be ‘automatic’, ‘manual’, ‘naturalistic’, ‘cmr’, ‘murd62’, or ‘frfr’. The free recall audio recordings for the ‘automatic’ dataset was transcribed by Google Cloud Speech and the ‘manual’ dataset was transcribed by humans. The ‘naturalistic’ dataset was transcribed by humans and transformed as described above. The ‘cmr’ dataset is from Polyn, Norman & Kahana (2009). The ‘murd62’ dataset is from Murdock (1962). The ‘frfr’ dataset is from Heusser, Fitzpatrick & Manning (2018).

Returns:

dataquail.Egg

Example data