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Using the environment to predict memory performance
John R Anderson1, Shawn Betts1, Jon M Fincham1
1Department of Psychology, Carnegie Mellon University.
Journal of Experimental Psychology. Learning, Memory, and Cognition
|February 12, 2026
Summary
Memory recall speed closely matches natural information patterns. A new algorithm predicts memory performance based on environmental probabilities, improving continuous recognition experiment analysis.
Area of Science:
- Cognitive Psychology
- Information Science
Background:
- Information presentation in memory experiments often differs from natural environments.
- Previous research indicates similarities in information patterns across diverse natural settings.
Purpose of the Study:
- To investigate the relationship between environmental probability of information occurrence and memory recognition fluency.
- To develop and validate a generalized algorithm for predicting continuous recognition memory performance based on environmental probabilities.
Main Methods:
- Experiment 1: Continuous recognition task with word order mirroring real-world data (tweets).
- Experiment 2: Testing a generalized prediction algorithm across natural, random, and spaced presentation orders.
- Application of the algorithm to an existing continuous recognition paradigm.
Main Results:
- Recognition fluency (inverse efficiency) strongly correlated with the environmental probability of word occurrence.
- The reciprocal square-root law accurately predicted memory performance.
- The generalized prediction algorithm successfully predicted results across different presentation orders.
Conclusions:
- Memory performance is significantly influenced by the natural probability of information encountered in the environment.
- A generalized algorithm can predict continuous recognition memory performance from environmental probabilities.
- Further research is needed to broadly apply environmental analyses to memory performance prediction.
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