Familiarity breeds differentiation: a subjective-likelihood approach to the effects of experience in recognition memory.

By James L. McClelland, M. Chappell. 1998

With repeated exposure, people become better at identifying presented items and better at rejecting items that have not been presented. This differentiation effect is captured in a model consisting of item detectors that learn estimates of conditional probabilities of item features. The model is used to account for a number of findings in the recognition memory literature, including (a) the basic differentiation effect (strength-mirror effect), (b) the fact that adding items to a list reduces recognition accuracy (list-length effect) but extra study of some items does not reduce recognition accuracy for other items (null list-strength effect), (c) nonlinear effects of strengthening items on false recognition of similar distractors, (d) a number of different kinds of mirror effects, (e) appropriate z-ROC curves, and (f) one type of deviation from optimality exhibited in recognition experiments.

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Citations in the corpus

Generalization Through the Recurrent Interaction of Episodic Memories (2012, methodology): “They may, however, approximate varying degrees of knowledge of those true likelihoods (McClelland & Chappell, 1998).”
Output interference in recognition memory (2011, methodology): “Prior tests of the models Like several item-noise models (e.g., McClelland & Chappell, 1998; Shiffrin & Steyvers, 1997), BCDMEM was designed to predict list composition effects.”
The consequences of differentiation in episodic memory: Similarity and the strength based mirror effect (2006, methodology): “The differentiation models provide different accounts of this pattern: REM adopted a recall-to-reject mechanism but SLiM maintained the same familiarity-based process used for unrelated foils (Malmberg et al., 2004 & McClelland & Chappell, 1998, respectively).”
A dual-process account of the list-length and strength-based mirror effects in recognition (2003, methodology): “Several memory models (e.g., Gillund & Shiffrin, 1984; McClelland & Chappell, 1998; Shiffrin & Steyvers, 1997) predict a list-length effect for recognition.”
Modeling hippocampal and neocortical contributions to recognition memory: a complementary-learning-systems approach. (2003, background): “Recognition performance suffers because the noisy recall signal drowns out useful information that is present in the familiarity signal.”
Similarity, global matching, and judgments of frequency (2001, methodology): “And although McClelland and Chappell (1998, p. 730) do deal with data from the frequency-judgment task, they focus on the recognition decision, JOF 5 0 versus JOF > 0, and explicitly exclude from consideration the tendency for JOFs of 1, 2, 3, and so on to track increases in objective frequency.”
Decorative pictures and emotional design in multimedia learning (2016, background): “Classification of illustrations within multimedia learning environments. pictures and learning information could form a compound cue (Schneider & Logan, 2009 ) and lead to a more effective knowledge representation according to theories on neural networks (McClelland & Chappell, 1998).”
Brain potentials of recollection and familiarity (2000, background): “(B) Contours lines are plotted every 0.26 µV. fects attributed to distinct familiarity and recollection processes (e.g., Donaldson, 1996; Hirshman & Master, 1997; McClelland & Chappell, 1998; Ratcliff et al., 1995; Shiffrin & Steyvers, 1997).”