Behavioral Analyses

Standard analyses for free recall data

jaxcmr provides functions for computing standard behavioral measures from free recall experiments. Each analysis can be applied to both empirical data and model simulations.## Getting Started- Importing Data - Loading recall datasets- Scoring Data - Computing basic metrics- Custom Analyses - Implementing your own## Analysis Categories### Serial PositionHow recall probability varies with study position.| Analysis | Description ||———-|————-|| SPC | Serial Position Curve - recall probability by position || PNR | Probability of Nth Recall - what position is recalled first, second, etc. |### Temporal ContiguityHow recall transitions relate to study order.| Analysis | Description ||———-|————-|| CRP | Conditional Response Probability - transition probabilities by lag |### Category EffectsEffects of item categories on recall.| Analysis | Description ||———-|————-|| CatCRP | Category CRP - within vs. between category transitions |### Repetition EffectsAnalyses for experiments with repeated items.| Analysis | Description ||———-|————-|| RepCRP | Repetition CRP - transitions involving repeated items |### Error AnalysesPatterns of recall errors.| Analysis | Description ||———-|————-|| Intrusions | Extra-list intrusion rates || Omissions | Items not recalled |## Using AnalysesAll analysis functions follow a similar pattern:

Code

from jaxcmr.analyses.spc import spc, plot_spc

# Compute analysis
result = spc(data, list_length, trial_mask=None)

# Or use the plotting function
plot_spc(
    datasets=[data, sim],
    trial_masks=[mask, mask],
    labels=["Data", "Model"],
)

Comparing Model and Data

Most plotting functions accept multiple datasets for comparison:

Code

from jaxcmr.analyses.crp import plot_crp

plot_crp(
    datasets=[simulation, empirical_data],
    trial_masks=[sim_mask, data_mask],
    labels=["CMR Model", "Observed"],
)