Abstract
We explore competing explanations for the reduction in false alarm rate observed when studied items are strengthened. Some models, such as Retrieving Effectively from Memory (REM; Shiffrin & Steyvers, 1997), attribute the false alarm rate reduction to differentiation, a process in which strengthening memory traces at study directly reduces the memory evidence for lure items. Models with no differentiation mechanism, such as the Bind-Cue-Decide Model of Episodic Memory (BCDMEM; Dennis & Humphreys, 2001), explain the false alarm rate reduction in terms of the strength of items expected at retrieval. To contrast these explanations, we separately manipulated item strength at encoding and retrieval. Participants studied mixed lists of weak and strong items. Weak items were always presented once. On separate lists, strong items were either presented twice (strong-2X) or five times (strong-5X). Within each strength condition, participants completed separate tests with mixed (strong and weak) targets, pure weak targets, or pure strong targets. They were correctly informed of the type of target on each test. Results showed that false alarm rates decreased from the strong-2X condition to the strong-5X condition for the mixed and pure-strong tests, but not for the pure-weak tests. That is, false alarm rates were determined by the strength of targets appearing on the test, not by the content of the study list. The results support BCDMEM's expectation-based explanation and not REM's differentiation-based explanation.
Original language | English (US) |
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Pages (from-to) | 18-34 |
Number of pages | 17 |
Journal | Journal of Memory and Language |
Volume | 63 |
Issue number | 1 |
DOIs | |
State | Published - Jul 2010 |
Keywords
- Bind-Cue-Decide Model of Episodic Memory
- Differentiation
- Retrieving Effectively from Memory
- Strength-based mirror effect
ASJC Scopus subject areas
- Neuropsychology and Physiological Psychology
- Language and Linguistics
- Experimental and Cognitive Psychology
- Linguistics and Language
- Artificial Intelligence