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Why Empirical Forgetting Curves Deviate from Actual Forgetting Rates: A Distribution Model of Forgetting
Nate Kornell1, Robert A Bjork2
1Department of Psychology, Williams College, 18 Hoxsey Street, Williamstown, MA 01267, USA.
Behavioral Sciences (Basel, Switzerland)
|July 29, 2025
Summary
Forgetting curves are not direct measures of memory decay. Memory strength distribution significantly impacts recall rates, affecting how quickly individual memories are forgotten over time.
Area of Science:
- Cognitive Psychology
- Neuroscience
- Educational Psychology
Background:
- Forgetting research traditionally models recall decay using power or exponential functions.
- Empirical forgetting curves are often assumed to directly reflect individual memory forgetting rates.
Purpose of the Study:
- To propose and validate a memory distribution model explaining the divergence between forgetting curves and item forgetting rates.
- To investigate the influence of memory strength distribution on recall performance over time.
Main Methods:
- Development of a theoretical distribution model of memory strengths relative to a recall threshold.
- Analysis of existing evidence and a new experiment involving participants learning word pairs to a criterion.
Main Results:
- The distribution model explains why forgetting curves can appear linear or concave, especially at high percentages of correct recall.
- Empirical data supported the model's predictions regarding the relationship between memory strength distribution and forgetting curves.
Conclusions:
- Forgetting curves are influenced by the distribution of memory strengths, not solely by individual memory decay rates.
- Educational strategies should consider memory strength distribution for long-term retention, as memories just above threshold are not durable.
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