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 backgroundnoise, 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.

A critical constraint on models of item recognition comes from the list strength paradigm, in which a proportion of items are strengthened to observe the effect on the non-strengthened items. In item recognition, it has been widely established that increasing list strength does not impair performance, in that performance of a set of items is unaffected by the strength of the other items on the list. However, to date the effects of list strength manipulations have not been measured in the source memory task. We conducted three source memory experiments where items studied in two sources were presented in a pure weak list, where all items were presented once, and a mixed list, where half of the items in both sources were presented four times. Each experiment varied the nature of the testing format. In Experiment 1, in which each study list was only tested on one task (item recognition or source memory), a list strength effect was found in source memory while a null effect was found for item recognition. Experiments 2 and 3 showed robust null list strength effects when either the test phase (Experiment 2) or the analysis (Experiment 3) was restricted to recognized items. An extension of the Osth and Dennis (2015) model was able to account for the results in both tasks in all experiments by assuming that unrecognized items elicit guess responses in the source memory task and that there was low interference among the studied items. The results were also found to be consistent with a variant of the retrieving effectively from memory model (REM;Shiffrin & Steyvers, 1997) that uses ensemble representations.Keywords: recognition memory; source memory; global matching modelsThe list strength effect in source memory: Data and a global matching model A major distinction in episodic memory research concerns the difference between information about learned content and the context in which it occurred. A common memory failure illustrating this distinction is that people remember a fact or detail but have no memory for where they learned the information. The relationship between memory for content and context is studied in the laboratory using the item recognition and source memory paradigms. In the item recognition paradigm, participants study a list of items and at test are asked to discriminate between studied items (targets) and unstudied items (lures). The source memory paradigm presents participants with a set of items in different sources, such as different font colors, studied locations, or sensory modalities. At test, participants judge which source studied items were presented in.A number of computational models of decision making have been developed to explain the relations between item and source memory (e.g.; Banks, 2000;Batchelder & Riefer, 1990; DeCarlo, 2003;Yonelinas, 1999; Hautus, Macmillan, & Rotello, 2008; Glanzer, Hilford, & Kim, 2004;Klauer & Kellen, 2010;Slotnick & Dodson, 2005). These models fall into several frameworks including multivariate signal detection theory, in which participants make ...

A critical constraint on models of item recognition comes from the list strength paradigm, in which a proportion of items are strengthened to observe the effect on the non-strengthened items. In item recognition, it has been widely established that increasing list strength does not impair performance, in that performance of a set of items is unaffected by the strength of the other items on the list. However, to date the effects of list strength manipulations have not been measured in the source memory task. We conducted three source memory experiments where items studied in two sources were presented in a pure weak list, where all items were presented once, and a mixed list, where half of the items in both sources were presented four times. Each experiment varied the nature of the testing format. In Experiment 1, in which each study list was only tested on one task (item recognition or source memory), a list strength effect was found in source memory while a null effect was found for item recognition. Experiments 2 and 3 showed robust null list strength effects when either the test phase (Experiment 2) or the analysis (Experiment 3) was restricted to recognized items. An extension of the Osth and Dennis (2015) model was able to account for the results in both tasks in all experiments by assuming that unrecognized items elicit guess responses in the source memory task and that there was low interference among the studied items. The results were also found to be consistent with a variant of the retrieving effectively from memory model (REM; Shiffrin & Steyvers, 1997) that uses ensemble representations.

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