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Related Experiment Video

Updated: Jun 4, 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

A model for recognition memory: REM-retrieving effectively from memory.

R M Shiffrin¹, M Steyvers

¹Psychology Department, Indiana University, 47405, Bloomington, IN, shiffrin@indiana.edu.

Psychonomic Bulletin & Review

|February 19, 2011

Summary

This summary is machine-generated.

A novel recognition memory model explains memory phenomena like the list-strength and mirror effects. It models memory traces as incomplete feature vectors, successfully predicting test item recognition probabilities.

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Using a Classroom-Based Deese Roediger McDermott Paradigm to Assess the Effects of Imagery on False Memories

Using a Classroom-Based Deese Roediger McDermott Paradigm to Assess the Effects of Imagery on False Memories

Published on: November 14, 2018

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Last Updated: Jun 4, 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

Using a Classroom-Based Deese Roediger McDermott Paradigm to Assess the Effects of Imagery on False Memories

Using a Classroom-Based Deese Roediger McDermott Paradigm to Assess the Effects of Imagery on False Memories

Published on: November 14, 2018

Area of Science:

Cognitive Psychology
Computational Neuroscience

Background:

Existing models struggle to explain core recognition memory phenomena.
There is a need for a unified theory of explicit, implicit, episodic, and generic memory.

Purpose of the Study:

Introduce a new computational model for recognition memory.
Integrate this model into a broader theory of memory.
Test the model's ability to predict challenging empirical findings.

Main Methods:

Developed a computational model storing episodic memory traces as feature vectors.
Applied the model to explain the list-strength effect, mirror effect, and ROC slope effect.
Calculated the probability of an item being recognized as 'old' based on stored feature representations.

Main Results:

The model successfully accounts for the list-strength effect.
The model explains the mirror effect.
The model accurately predicts the normal ROC slope effect in recognition memory.

Conclusions:

The proposed recognition memory model offers a robust framework for understanding memory.
The model's success with problematic phenomena suggests its potential for a unified memory theory.
Further development of the model promises more realistic predictions of memory behaviors.