Free Recall Analysis¶
Utilities for working with free recall data.
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psifr.fr.block_index(list_labels)¶ Get index of each block in a list.
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psifr.fr.check_data(df)¶ Run checks on free recall data.
- Parameters
df (pandas.DataFrame) –
- Contains one row for each trial (study and recall). Must have fields:
- subjectnumber or str
Subject identifier.
- listnumber
List identifier. This applies to both study and recall trials.
- trial_typestr
Type of trial; may be ‘study’ or ‘recall’.
- positionnumber
Position within the study list or recall sequence.
- itemstr
Item that was either presented or recalled on this trial.
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psifr.fr.get_recall_index(df, list_cols=None)¶ Get recall input position index by list.
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psifr.fr.get_study_value(df, column, list_cols=None)¶ Get study column value by list.
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psifr.fr.lag_crp(df, test_values=None, test=None, first_output=None)¶ Lag-CRP for multiple subjects.
- Parameters
df (pandas.DataFrame) – Merged study and recall data. See merge_lists. List length is assumed to be the same for all lists within each subject. Must have fields: subject, list, input, output, recalled. Input position must be defined such that the first serial position is 1, not 0.
test_values (pandas.Series or column name, optional) – Column with labels to use when testing transitions for inclusion.
test (callable, optional) – Callable that takes in previous and current item values and returns True for transitions that should be included.
first_output (int, optional) – First output position to include when calculating transition probabilities. Used to exclude initial outputs. Default is to start at the first recall on each list.
- Returns
results – Has fields:
- subjecthashable
Results are separated by each subject.
- lagint
Lag of input position between two adjacent recalls.
- probfloat
Probability of each lag transition.
- actualint
Total of actual made transitions at each lag.
- possibleint
Total of times each lag was possible, given the prior input position and the remaining items to be recalled.
- Return type
pandas.DataFrame
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psifr.fr.merge_lists(study, recall, merge_keys=None, list_keys=None, study_keys=None, recall_keys=None, position_key='position')¶ Merge study and recall events together for each list.
- Parameters
study (pandas.DataFrame) – Information about all study events. Should have one row for each study event.
recall (pandas.DataFrame) – Information about all recall events. Should have one row for each recall attempt.
merge_keys (list, optional) – Columns to use to designate events to merge. Default is [‘subject’, ‘list’, ‘item’], which will merge events related to the same item, but only within list.
list_keys (list, optional) – Columns that apply to both study and recall events.
study_keys (list, optional) – Columns that only apply to study events.
recall_keys (list, optional) – Columns that only apply to recall events.
position_key (str, optional) – Column indicating the position of each item in either the study list or the recall sequence.
- Returns
merged – Merged information about study and recall events. Each row corresponds to one unique input/output pair.
The following columns will be added:
- inputint
Position of each item in the input list (i.e., serial position).
- outputint
Position of each item in the recall sequence.
- recalledbool
True for rows with an associated recall event.
- repeatint
Number of times this recall event has been repeated (0 for the first recall of an item).
- intrusionbool
True for recalls that do not correspond to any study event.
- Return type
pandas.DataFrame