A computational approach to the revelation effect

Publication date: June 2020Source: Journal of Memory and Language, Volume 112Author(s): Martin Brandt, André Aßfalg, Ann-Kathrin Zaiser, Daniel M. BernsteinAbstractInterrupting a sequence of episodic recognition decisions by a problem-solving task will change the hit and false alarm rate for the following item in a recognition test (Watkins & Peynircioglu, 1990). The mechanisms of this revelation effect have not yet been understood completely. We offer a new explanation based on the global matching model MINERVA 2 (Hintzman, 1984, 1986, 1988). The main mechanism in our approach is that the interrupting problem-solving task eliminates some context features in the retrieval cue for the next recognition decision. Assuming a constant decision criterion, this shifts the means of the underlying familiarity distributions and produces a revelation effect. The means of the familiarity distributions decrease for low-frequency stimuli but can shift to more positive values for high-frequency stimuli. We show how this approach explains established empirical findings. We also test new predictions within three experiments. The first two experiments show that the revelation effect disappears if context features are made more available at test. The third experiment confirms the prediction that the revelation effect increases as a function of pre-experimental frequency. Overall, our approach explains findings that have been difficult to explain so far, provides a framework for new predi...
Source: Journal of Memory and Language - Category: Speech-Language Pathology Source Type: research