The ignored alternative: An application of Luce's low-threshold model to recognition memory

David Kellen, Edgar Erdfelder, Kenneth J. Malmberg, Chad Dubé, Amy H. Criss

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


Recent years have seen an increased interest in models of recognition memory's decision stage. However, only a relatively narrow set of candidate models has been considered thus far, with comparisons being typically restricted to signal detection and high-threshold models. Here, we consider a third alternative, Luce's (1963) low-threshold model (LTM). We evaluated the LTM's predictions for existing Yes–No receiver-operating characteristic data (Dube et al., 2012) as well as data from K-alternative ranking tasks (Kellen and Klauer, 2014). The LTM, which to this point has been largely ignored in the recognition memory literature, turns out to perform at least as well as the most popular model in this domain, the Gaussian signal detection model. These results suggest future work concerning the decision stage of recognition should consider the LTM in addition to the continuous and discrete-state models that have dominated the literature so far.

Original languageEnglish (US)
Pages (from-to)86-95
Number of pages10
JournalJournal of Mathematical Psychology
StatePublished - Dec 1 2016


  • High threshold models
  • Low threshold models
  • ROCs
  • Recognition
  • Signal detection theory

ASJC Scopus subject areas

  • General Psychology
  • Applied Mathematics


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