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Related Experiment Video

Updated: Jul 6, 2026

A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers

A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers

Published on: January 5, 2018

An improved algorithm for predicting free recalls.

Donald Laming¹

¹Department of Experimental Psychology, University of Cambridge, Downing Street, Cambridge, England CB2 3EB, UK. drjl@hermes.cam.ac.uk

Cognitive Psychology

|March 11, 2008

Summary

An improved algorithm can predict free recall sequences using prior stimuli and rehearsals. This predictive model offers insights into memory and rehearsal processes, outperforming models that ignore rehearsal data.

Related Experiment Videos

Last Updated: Jul 6, 2026

A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers

A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers

Published on: January 5, 2018

Area of Science:

Cognitive Psychology
Experimental Psychology
Memory Research

Background:

Prior research by Laming (2006) demonstrated the predictability of free recall sequences from stimuli and rehearsals.
Existing models often focus solely on recall sequences, neglecting the role of overt rehearsal.

Purpose of the Study:

To introduce and validate an enhanced predictive algorithm for free recall.
To compare the predictive power of the new algorithm against existing free recall models.
To explore the implications of incorporating rehearsal data into predictive models of memory.

Main Methods:

Development of an improved predictive algorithm incorporating rehearsal data.
Re-analysis of two existing free recall datasets using the enhanced algorithm.
Comparative analysis of the algorithm's predictions against three conventional free recall models.

Main Results:

The improved algorithm demonstrated a useful degree of precision in predicting entire recall sequences.
The predictive algorithm's performance was evaluated against established models of free recall.
The study identified implications for the assumptions of current models based on the utility of rehearsal data.

Conclusions:

Predictive algorithms that incorporate rehearsal data offer a more comprehensive understanding of free recall.
The findings challenge certain assumptions in existing free recall models that do not account for rehearsal.
This work advances the modeling of memory recall by integrating overt behavioral data.