Effects of retention intervals on receiver operating characteristics in artificial grammar learning

Richard J. Tunney*, Gilbert Bezzina

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

Current theories of memory suggest that recognition is composed of separate processes of familiarity and recollection (e.g. [Yonelinas, A. P. (2002). The nature of recollection and familiarity: a review of 30 years of research. Journal of Memory and Language, 46, 441-517]). A key feature of these two processes is that they decay, or are forgotten at different rates. The dual-process model has also been useful in understanding artificial grammar learning. We obtained evidence for recollection and familiarity in artificial grammar learning by analyses of receiver operating characteristics (ROC). Furthermore we found that these were dissociated by retention intervals of 14 days. The slope of the zROC curves deviated reliably from 1 immediately after study and increased towards 1 suggesting that recollection contributed to recognition decisions but declined over the 14-day period leaving familiarity as the only basis for recognition. These data show similar patterns to those observed in word-recognition [Gardiner, J. M., & Java, R. I. (1991). Forgetting in recognition memory with and without recollective experience. Memory & Cognition, 19, 617-623; Tunney, R. J. (submitted for publication). Changes in the subjective experience of recognition over time suggest independent processes] and confirm the view that recollection and familiarity are implicated in artificial grammar learning. Moreover, the data confirm the finding that recollection and familiarity-based memory show different patterns of forgetting.

Original languageEnglish
Pages (from-to)37-50
Number of pages14
JournalActa Psychologica
Volume125
Issue number1
DOIs
Publication statusPublished - 1 May 2007

Fingerprint

ROC Curve
Learning
Recognition (Psychology)
Retention (Psychology)
Artificial Grammar Learning
Recollection
Familiarity
Receiver Operating Characteristic
Cognition
Publications
Language
Forgetting

Keywords

  • Artificial grammar
  • Confidence
  • Forgetting
  • Implicit learning

Cite this

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title = "Effects of retention intervals on receiver operating characteristics in artificial grammar learning",
abstract = "Current theories of memory suggest that recognition is composed of separate processes of familiarity and recollection (e.g. [Yonelinas, A. P. (2002). The nature of recollection and familiarity: a review of 30 years of research. Journal of Memory and Language, 46, 441-517]). A key feature of these two processes is that they decay, or are forgotten at different rates. The dual-process model has also been useful in understanding artificial grammar learning. We obtained evidence for recollection and familiarity in artificial grammar learning by analyses of receiver operating characteristics (ROC). Furthermore we found that these were dissociated by retention intervals of 14 days. The slope of the zROC curves deviated reliably from 1 immediately after study and increased towards 1 suggesting that recollection contributed to recognition decisions but declined over the 14-day period leaving familiarity as the only basis for recognition. These data show similar patterns to those observed in word-recognition [Gardiner, J. M., & Java, R. I. (1991). Forgetting in recognition memory with and without recollective experience. Memory & Cognition, 19, 617-623; Tunney, R. J. (submitted for publication). Changes in the subjective experience of recognition over time suggest independent processes] and confirm the view that recollection and familiarity are implicated in artificial grammar learning. Moreover, the data confirm the finding that recollection and familiarity-based memory show different patterns of forgetting.",
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Effects of retention intervals on receiver operating characteristics in artificial grammar learning. / Tunney, Richard J.; Bezzina, Gilbert.

In: Acta Psychologica, Vol. 125, No. 1, 01.05.2007, p. 37-50.

Research output: Contribution to journalArticle

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