mixtur: An R package for designing, analysing, and modelling continuous report visual short-term memory studies

Area of Science:

• Cognitive Psychology
• Computational Neuroscience
• Psychometrics

Background:

• Visual short-term memory (vSTM) is crucial for cognitive function.
• Continuous-report tasks are standard for measuring vSTM precision.
• Mixture models (two- and three-component) are prominent for analyzing vSTM data, accounting for target responses, guessing, and binding errors.

Purpose of the Study:

• Introduce mixtur, an open-source R package for fitting mixture models to continuous report data.
• Provide researchers with a tool to analyze vSTM performance and memory errors.
• Offer guidance on experimental design parameters for mixture modeling.

Main Methods:

• Developed mixtur, an R package implementing two- and three-component mixture models.
• Conducted simulations to evaluate parameter and model recovery.
• Generated recommendations for trial numbers, set sizes, and stimulus similarity.

Main Results:

• The mixtur package facilitates the fitting of established mixture models for vSTM research.
• Simulation results offer practical guidelines for researchers designing continuous-report experiments.
• The package supports analysis of various models, including slots and slots-plus-averaging.

Conclusions:

• mixtur lowers the barrier to entry for mixture modeling in vSTM research.
• The package can be extended to incorporate more complex explanatory models of memory.
• This tool empowers researchers to more effectively quantify memory precision and error types.