Academic journal article Psychonomic Bulletin & Review

Toward a Complete Decision Model of Item and Source Recognition

Academic journal article Psychonomic Bulletin & Review

Toward a Complete Decision Model of Item and Source Recognition

Article excerpt

In a recognition memory test, subjects may be asked to decide whether a test item is old or new (item recognition) or to decide among alternative sources from which it might have been drawn for study (source recognition). Confidence-rating-based receiver operating characteristic (ROC) curves for these superficially similar tasks are quite different, leading to the inference of correspondingly different decision processes. A complete account of source and item recognition will require a single model that can be fit to the entire data set. We postulated a detection-theoretic decision space whose dimensions, in the style of Banks (2000), are item strength and the relative strengths of the two sources. A model that assumes decision boundaries of constant likelihood ratios, source guessing for unrecognized items, and nonoptimal allocation of attention can account for data from three canonical data sets without assuming any processes specifically devoted to recollection. Observed and predicted ROCs for one of these data sets are given in the article, and ROCs for the other two may be downloaded from the Psychonomic Society's Archive of Norms, Stimuli, and Data,

Recognition of episodic memories divides naturally into two parts: recognition that a test item has been encountered previously, and assessment of the source of that memory. The importance of source memory is obvious in many applications: The familiarity of a crime suspect has very different implications, depending on whether the memory arose from a crime scene or a chance encounter in a store; the potency of a memory of childhood abuse depends on whether the source is an early event or later discussions of the possibility of such an event.

Typical item and source experiments are parallel in structure. In item recognition, subjects study a list (of words, pictures, etc.) and are then tested with probes that may be targets (from the study list) or lures (unstudied). Each test probe is identified as "old" or "new," and confidence ratings may be added. In source recognition, subjects study two (or more) lists and are tested with probes that may be from either list. Each test item is identified as coming from List 1 or List 2, and confidence ratings may again be added.

Item and source recognition have both been studied using signal detection theory (SDT) techniques and models. Insight into the memory representation and decision processes can be obtained from receiver operating characteristic (ROC) curves, constructed by treating confidence ratings as different levels of response bias. In item recognition, correct responses to targets, called hits, are plotted against incorrect responses to lures, called false alarms. In source recognition, correct responses to items from one list, considered to be hits, are plotted against incorrect responses to items from the other list, considered to be false alarms.

In this article, we first summarize ROC data drawn from both item and source recognition. On the basis of the quite different ROC shapes these two tasks generate, theorists have inferred distinct underlying processes for each of them. We argue that such separatist modeling distorts conclusions and that a unified model of item and source recognition is needed. Two such models, both based on two-dimensional SDT, have been proposed, but these do not provide a good description of the existing data. We build stepwise on these models, adding likelihood-based criteria, source guessing for unrecognized items, and a degree of inattention. The final model provides an excellent fit to three canonical data sets.


Form of Item and Source ROCS

To appreciate the distinctive characteristics of source ROCs, it is helpful to contrast them with their item recognition (old-new) counterparts. A representative item ROC is plotted on both probability and z-transformed coordinates in Figure 1. …

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