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Related Experiment Video

Updated: May 25, 2026

The Deese-Roediger-McDermott (DRM) Task: A Simple Cognitive Paradigm to Investigate False Memories in the Laboratory

The Deese-Roediger-McDermott (DRM) Task: A Simple Cognitive Paradigm to Investigate False Memories in the Laboratory

Published on: January 31, 2017

Criterion setting and the dynamics of recognition memory.

Gregory E Cox¹, Richard M Shiffrin

¹Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA. grcox@indiana.edu

Topics in Cognitive Science

|January 19, 2012

Summary

This study introduces a dynamic recognition memory model that overcomes criterion setting challenges. It predicts recognition performance and reaction times by tracking familiarity changes over time.

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The Deese-Roediger-McDermott (DRM) Task: A Simple Cognitive Paradigm to Investigate False Memories in the Laboratory

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Area of Science:

Cognitive Psychology
Neuroscience
Computational Modeling

Background:

Traditional recognition memory models face challenges with criterion setting, especially with diverse item types.
Varying baseline familiarity and experience complicate accurate memory assessments.
Existing models struggle to predict both choice accuracy and reaction time simultaneously.

Purpose of the Study:

To present a novel dynamic model of recognition memory.
To address the problem of criterion setting in recognition tasks.
To generate joint predictions for recognition choice and reaction time.

Main Methods:

Developed a dynamic model where recognition decisions depend on familiarity changes during feature sampling.
Modeled decisions as a race between accumulators for positive (old) and negative (new) familiarity changes.
Simulated model performance across items with varying baseline familiarity.

Main Results:

The dynamic model successfully predicts recognition performance and reaction latency.
Model predictions remain realistic irrespective of item baseline familiarity.
The model offers a unified account for both choice and timing in recognition memory.

Conclusions:

The proposed dynamic model offers a robust solution to the criterion setting problem in recognition memory.
This approach provides a more comprehensive understanding of recognition processes by integrating familiarity dynamics.
The model's ability to predict both performance and latency enhances its utility in cognitive research.