Abstract
Manipulations of encoding strength and stimulus class can lead to a simultaneous increase in hits and decrease in false alarms for a given condition in a yes/no recognition memory test. Based on signal detection theory, the strengthbased ‘mirror effect’ is thought to involve a shift in response criterion/threshold (Type I), whereas the stimulus class effect derives from a specific ordering of the memory strength signals for presented items (Type II). We implemented both suggested mechanisms in a simple, competitive feed-forward neural network model with a learning rule related to Bayesian inference. In a single-process approach to recognition, the underlying decision axis as well as the response criteria/thresholds were derived from network activation. Initial results replicated findings in the literature and are a first step towards a more neurally explicit model of mirror effects in recognition memory tests.
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