Modeling the continuous recognition paradigm to determine how retrieval can impact subsequent retrievals

Area of Science:

• Cognitive Psychology
• Computational Neuroscience
• Memory Research

Background:

• Retrieval during memory testing can impair memory through interference (item-noise), reduced accessibility (context drift), and speed-accuracy trade-offs.
• The continuous recognition paradigm dissociates these effects: context drift causes the lag effect, and item-noise causes the test position effect (TPE).

Purpose of the Study:

• To investigate the roles of item-noise, context drift, and decision factors in memory retrieval.
• To model these influences using a novel continuous recognition dataset and advanced computational methods.

Main Methods:

• Utilized a novel continuous recognition dataset.
• Applied variants of the Osth et al. (2018) global matching model.
• Employed hierarchical Bayesian methods to fit single-trial choice and response times, incorporating multiple context scales and sequential effects.

Main Results:

• Item-noise was identified as the primary driver of the test position effect (TPE).
• Context drift was found to diminish the magnitude of the TPE by reducing the impact of item-noise.
• Minor, participant-specific effects were observed for speed-accuracy trade-offs.

Conclusions:

• Item-noise and context drift play distinct roles in memory retrieval dynamics within the continuous recognition paradigm.
• Computational modeling provides a powerful framework for dissecting complex memory processes.
• Understanding these retrieval mechanisms is crucial for advancing memory theory and potential interventions.