Academic journal article Psychonomic Bulletin & Review

Toward a Complete Decision Model of Item and Source Recognition: A Discrete-State Approach

Academic journal article Psychonomic Bulletin & Review

Toward a Complete Decision Model of Item and Source Recognition: A Discrete-State Approach

Article excerpt

In source-monitoring experiments, participants study items from two sources (A and B). At test, they are presented Source A items, Source B items, and new items. They are asked to decide whether a test item is old or new (item memory) and whether it is a Source A or a Source B item (source memory). Hautus, Macmillan, and Rotello (2008) developed models, couched in a bivariate signal detection framework, that account for item and source memory across several data sets collected in a confidence-rating response format. The present article enlarges the set of candidate models with a discrete-state model. The model is a straightforward extension of Bayen, Murnane, and Erdfelder's (1996) multinomial model of source discrimination to confidence ratings. On the basis of the evaluation criteria adopted by Hautus et al., it provides a better account of the data than do Hautus et al.'s models.

Being able to recognize previously acquired information is one of the best-studied abilities in the memory literature, an ability often referred to as item memory (for a review, see Malmberg, 2008). But being able to recognize something that was previously encountered represents only a part of a more elaborated faculty. People can often also remember the origin and specific characteristics of previously encountered stimuli, an ability that is often referred to as source memory (Johnson, Hashtroudi, & Lindsay, 1993). Experiments on source memory involve the discrimination of one or more characteristics of previously studied items, such as whether a given item was spoken by a female voice (Source A) or by a male voice (Source B). Just as for item memory, source judgments can be collected using a binary responses format (e.g., respond "male" or "female") or by means of a rating scale expressing graded degrees of confidence in a source ascription.

Source memory is usually not studied separately from item memory (for an exception, see DeCarlo, 2003a). Prior to the source judgment, participants typically judge whether the item was previously presented or not. Traditionally, source judgments are requested only for items judged old. The joint assessment of item and source memory has two main advantages. First, it permits a comprehensive assessment of memory performance in different populations, such as young and healthy individuals (e.g., Dodson & Shimamura, 2000; Johnson, Kounios, & Reeder, 1994), elderly individuals (e.g., Spencer & Raz, 1995), and specific clinical populations (e.g., Multhaup & Balota, 1997). Second, it allows one to investigate how the processes underlying both judgments interact-for example, in shaping guessing biases (Bayen, Nakamura, Dupuis, & Yang, 2000; Johnson & Raye, 1981; Meiser, Sattler, & von Hecker, 2007; Riefer, Hu, & Batchelder, 1994)-affecting overall performance.

One approach to studying source memory is the sourcemonitoring model proposed by Batchelder and colleagues (Batchelder & Riefer, 1990; Batchelder, Riefer, & Hu, 1994; Riefer et al., 1994), a discrete-state model that has successfully been used to distinguish source memory from both item memory and several guessing processes involved (but see Kinchla, 1994). This model has been further developed and extended to deal with different aspects of item and source memory, such as distractor detection (Bayen, Murnane, & Erdfelder, 1996), multiple source dimensions (Klauer, Ehrenberg, & Wegener, 2003; Meiser, 2005; Meiser & Bröder, 2002), and partial memory states (Dodson, Holland, & Shimamura, 1998; Klauer & Wegener, 1998).

Distinguishing Between Source Memory Models by Means of ROC Analyses

More recently, the discrete-state model has been considered less adequate than models based on continuous processes, such as signal detection models (Glanzer, Hilford, & Kim, 2004), or than hybrid models that assume a combination of continuous and threshold processes (Yonelinas, 1997, 1999). …

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