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A context noise model of episodic word recognition
1Key Centre for Human Factors and Applied Cognitive Psychology, University of Queensland, Brisbane, Queensland 4072, Australia. s.dennis@humanfactors.uq.edu.au
Psychological Review
|May 31, 2001
Summary
The bind cue decide model, a Bayesian context noise model, explains recognition memory interference. It accounts for item noise and dual-processing data, offering a viable alternative for understanding memory retrieval.
Area of Science:
- Cognitive Psychology
- Neuroscience
- Computational Modeling
Background:
- Recognition memory research traditionally considers interference from study list items (item noise) or retrieval contexts (context noise).
- Existing models struggle to fully integrate findings from both item noise and dual-processing perspectives.
Purpose of the Study:
- Introduce the bind cue decide (BCD) model, a Bayesian context noise model for episodic memory.
- Demonstrate the BCD model's ability to account for empirical data explained by item noise and dual-processing approaches.
Main Methods:
- Developed a Bayesian computational model (BCD) based on context noise principles.
- Applied the BCD model to re-interpret data concerning list strength, list length, word frequency, concreteness, item similarity, and context diagnosticity.
Main Results:
- The BCD model successfully explains list strength and length effects, as well as the mirror effect for word frequency and concreteness.
- The model also accounts for findings from process dissociation studies, including effects of list length, temporal separation, strength, and context diagnosticity.
Conclusions:
- The context noise approach, as embodied by the BCD model, provides a unified and viable framework for understanding recognition memory.
- This Bayesian context noise model offers a compelling alternative to existing item noise and dual-processing explanations of memory interference.