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Related Experiment Video

Updated: Mar 10, 2026

A Real-world What-Where-When Memory Test

A Real-world What-Where-When Memory Test

Published on: May 16, 2017

Likelihood ratio sequential sampling models of recognition memory.

Adam F Osth¹, Simon Dennis², Andrew Heathcote³

¹University of Melbourne, Australia.

Cognitive Psychology

|December 6, 2016

Summary

This summary is machine-generated.

This study introduces a new model for recognition memory that explains both accuracy and response times. The likelihood ratio diffusion decision model (LR-DDM) offers a parsimonious explanation for memory regularities.

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Area of Science:

Cognitive Psychology
Computational Neuroscience

Background:

The mirror effect, a key regularity in recognition memory, is well-explained by likelihood ratio models.
Previous models have not fully incorporated response time (RT) distributions.

Purpose of the Study:

To develop and test a novel computational model of recognition memory that integrates choice and RT data.
To evaluate the efficacy of a likelihood ratio-based diffusion decision model (LR-DDM).

Main Methods:

Developed a linear approximation of the likelihood ratio transformation for computational tractability.
Integrated this approximation into a diffusion decision model (DDM) framework.
Implemented and compared the LR-DDM against a standard DDM using hierarchical Bayesian modeling on four datasets.

Main Results:

The LR-DDM successfully predicts recognition memory regularities, including those in RT distributions.
Model selection favored the LR-DDM due to its parsimony, requiring fewer parameters than the standard DDM.
The LR-DDM demonstrated a good fit to the empirical data.

Conclusions:

Log-likelihood based models provide an elegant and parsimonious explanation for recognition memory phenomena.
The LR-DDM offers a unified account of both accuracy and response times in recognition memory tasks.
This approach advances our understanding of the decision processes underlying recognition memory.