Homogeneity of item material boosts the list length effect in recognition memory: A global matching perspective.

2019 ◽  
Vol 45 (5) ◽  
pp. 834-850 ◽  
Author(s):  
Martin Brandt ◽  
Ann-Kathrin Zaiser ◽  
Martin Schnuerch
2008 ◽  
Vol 59 (3) ◽  
pp. 361-376 ◽  
Author(s):  
Simon Dennis ◽  
Michael D. Lee ◽  
Angela Kinnell

2015 ◽  
Vol 85 ◽  
pp. 27-41 ◽  
Author(s):  
Jeffrey Annis ◽  
Joshua Guy Lenes ◽  
Holly A. Westfall ◽  
Amy H. Criss ◽  
Kenneth J. Malmberg

Author(s):  
Tyler M. Ensor ◽  
Dominic Guitard ◽  
Tamra J. Bireta ◽  
William E. Hockley ◽  
Aimée M. Surprenant

2019 ◽  
Author(s):  
Julian Fox ◽  
Simon Dennis ◽  
Adam F Osth

There has been a longstanding debate concerning whether interference in recognition memory is attributable to other items on the study list (i.e., item-noise) or to prior memories (i.e., context-noise and background-noise). Recently, Osth and Dennis (2015) devised a global matching model that could estimate the magnitude of each interference contribution and they found that context-noise and background-noise were dominant in recognition. In the present investigation, data from a list length experiment were analysed using variants of the Osth, Jansson, Dennis and Heathcote (2018) model, that integrates the memory retrieval components of the Osth and Dennis (2015) model with the diffusion decision model (Ratcliff, 1978) to jointly account for choice probabilities and RT distributions. The standard version of the model, like existing recognition models, treated each condition as if no proactive interference had accumulated over the session. A more comprehensive version of the model allowed both study and test items from prior conditions to contribute proactive interference (PI) to future conditions. While the standard model estimated a dominance of background-noise, the PI model estimated a dominance of item-noise, reversing the conclusions made by Osth and Dennis (2015). Along with list length, the experimental design provided a measure of the test position effect (TPE). While the standard model attributed the TPE to context drift, the PI model attributed the TPE to both context drift and increases in item-noise.


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