RUNNING HEAD: RECOGNITION MEMORY

Transcription

1 RUNNING HEAD: RECOGNITION MEMORY Recognition Memory: A Review of the Critical Findings and an Integrated Proposal for Relating Them Kenneth J. Malmberg University of South Florida Send Correspondence to: Kenneth J. Malmberg Department of Psychology, PCD 4118G University of South Florida 4202 East Fowler Ave. Tampa, FL (813) malmberg@cas.usf.edu

2 Abstract The author reviews the findings that are critical for discriminating between single-process and dual-process models of recognition memory. These findings include the results of single-item recognition, plurality discrimination, and associative recognition experiments under both free responding and signalto-respond test conditions. They also include the results of several confidence ratings (i.e., ROC) and remember-know experiments. The analysis of these findings is not amenable to any simple model of recognition memory. The author concludes that recognition memory is a complex system and that different tasks are performed in different ways. He proposes a new meta-level framework that organizes these models under the theoretical construct of efficiency. The central assumption is that subjects adopt recognition strategies that they believe will produce a desired level of accuracy in the shortest amount of time. The meta-level framework is shown to account the critical findings from the different varieties of recognition tasks. 2

3 1. INTRODUCTION. Because past experience guides future behavior, an understanding of the ability to know what one has experienced is central to understanding an individual s motivations and behaviors. I might ask you if you saw your neighbor yesterday. Presumably, one can accurately determine whether this is so even though the neighbor has been encountered many times in similar contexts. Discriminating among events that one experienced and events that one did not experience is referred to as recognition memory. In the laboratory, recognition memory is usually investigated by presenting subjects with items to remember, and memory for these items is tested with items that were studied (targets) and items that were not (foils). Sometimes these stimuli have been encountered in thousands of different prior contexts. For instance, the to-be-remembered items are often words presented to the subject for just a few hundred milliseconds. Nevertheless, the discrimination of targets and foils can be highly accurate. In recent years, the nature of recognition memory has been one of the most debated issues in psychological research and several reviews have appeared (Clark & Gronlund, 1996; Diana, Reder, Arndt, & Park, 2006; Dunn, 2004; Wixted, 2004; 2007; Yonelinas, 2002). The question is whether recognition is based on a single random variable or two random variables, and in all cases, the goal of the recent reviews has been to advocate for either a single-process or a dual-process model of recognition. To whit, some reviews argue for the sufficiency of a dual-process model: Models of recognition: A review of arguments in favor or a dual-process account (Diana et al., 2006). Other reviews defend the single-process position: In defense of the signal detection 3

4 interpretation of remember/know judgments (Wixted and Stretch, 2004). Still other reviews assumed which is the correct model: The nature of recollection and familiarity: A review of 30 years of research (Yonelinas, 2002). Many of the conclusions about the nature of recognition memory drawn from these reviews are undoubtedly with merit. However, the sufficiency of the arguments do not directly imply their necessity, and the various defenses of one position versus the other has not produced a consensus on which model is the correct model. In my view, a fair reading of them produces a sense that each side can argue points that are consistent with their position, and what is needed is broader assessment of recognition memory in order to highlight both the strengths and the weaknesses of these arguments. In this article, I will review a relatively broad range of empirical phenomenon. Given the vast literature, I cannot hope to cover everything, of course, and that is not my intent. Rather, this review focuses primarily on wellestablished behavioral observations of humans and the models that attempt to organize them (Wixted, 2004 provides a review of the cognitive neuroscience literature; Eichenbaum & Cohen, 2001, for a review the animal literature). I am specifically interested in discussing the findings that provide critical challenges to the models. Even so, this review is a rather comprehensive, and by bringing in a wide variety of important findings, the picture that develops is less amenable to an overly simplified account that assumes that recognition tasks are always performed in the same way. This is good because it forces one to acknowledge the limitations of the models and inspires one to organize the models themselves, much as individual models organize various findings. Here, I attempt to place 4

5 what is currently known about recognition memory in a meta-level framework, which can be compared to a global memory-framework. 1 Finally, I will propose what I believe is a novel means of integrating single-process and dual-process accounts into a coherent framework that in some ways blurs their traditional distinctions and can probably account for a wider range of empirical findings than any other approach to organizing the recognition literature. 2. RECALL, RECOGNITION, RECOLLECTION, AND FAMILIARITY. Let us start at the beginning, as I promise that we will come full circle. Philosophers have long considered the possibility that memory consists of learned associations (e.g., Locke, 1690/1961), and during the first half of the twentieth century, memory research was largely focused on associative learning in human and animals. The human memory research utilized recall tasks to assess how interference between related associations affected memory for past events and how one learned new sequences of events, items, etc. (McGoech, 1942). The discrimination of events that occurred and did not occur was not within the scope of the early human memory research. Recognition was also of little interest to animal learning researchers because they utilized various conditioning procedures in order to understand how new associations are acquired and because there was no animal model for episodic recognition. Thus, 1 A global-memory approach to modeling should not be confused with a global-matching approach to familiarity. The later refers to a class models that assume that familiarity is positively related to the similarity of a retrieval cue to information stored in a large number of memory traces (e.g., Clark & Gronlund, 1997), whereas global memory theories attempt to account for the interaction of a several variables with the performance of a variety of tasks (e.g., Estes, 1994; Gillund & Shiffrin, 1984; Murdock, 1982; Nosofsky and Zaki, 1998). 5

6 recognition was a largely unexplored faculty (but see Achilles, 1920; Mulhall, 1925; Müller, 1913 cited by Mandler et al., 1969; Strong, 1912). Today, investigations of recognition memory are commonly reported in both the human and animal memory literatures. The increasing interest in recognition memory is demonstrated in Figure 1, which plots the cumulative number of citations obtained from PsychInfo since 1880 based on a recent search of PsychInfo with the keywords recognition memory. It turned up an astounding 1161 unique articles published since (For those who are counting, that amounts to almost one new article every other day.) The total number of articles generated for recognition memory was How can one justify the intellectual and economic resources being devoted to recognition memory and why is it becoming an increasingly popular topic of investigation? (In other words, what is the value of this review?) Recognition tasks are related to many daily activities. One class of recognition tasks is similar to a true-false question on an exam. Accordingly, one is presented a stimulus and asked if it was encountered in a specific context. This is referred to as a yes-no or old-new task. A closely related task is a rating task, whereby subjects are asked to provide a scalar judgment that represents his or her confidence that a stimulus was studied. Some of these ratings are usually associated with a yes response and the remaining ratings are associated with a no response; each rating represents a different degree of confidence that the stimulus was studied. Recognition accuracy for the yes-no and ratings tasks is a positive function of the difference between the probability of responding yes to previously encountered items versus the probability of responding yes to items not previously 6

7 encountered. These are referred to as hit rates and false-alarm rates, respectively. A different form of recognition test is a multiple choice test. Accordingly, one might be shown two stimuli and asked which one was presented in a specific context. This is referred to this as a two-alternative forced-choice task. In this case, recognition accuracy is simply the probability of choosing the correct alternative. Measurement procedures that utilize data from these tasks were developed for independently measuring the contributions to task performance of the sensitivity (or accuracy) of memory and the bias in the recognition decision (Green & Swets, 1966). This made recognition memory data easier to interpret and helped improve its use as a tool for investigating human memory. An empirical reason why recognition became more popular is that recognition is viewed by many as a simpler task than, say, serial recall, pairassociate recall, or free recall. Thus, researchers following a reductionist approach believed that more specific questions about the nature of associations could be answered by focusing on what they believed was a simpler task (cf. Crowder, 1976). Support for the assumption that recognition is simpler to perform than recall primarily comes from the fact that recognition almost always is more accurate than recall, perhaps because accurate recognition does not necessarily require the production of specific information from memory and recall does. We now know, however, that there are some conditions under which items that cannot be recognized can be recalled (e.g., Tulving & Thomson, 1972), and the question of whether or not recognition is based on a generative process is in fact the bone of contention in the current literature. Indeed, one of the main 7

8 conclusions of this review is that recognition is often at least a complex as recall. Nevertheless, the investigation of recognition memory is more popular than ever. Why? The current up-tick in the popularity of recognition investigations is probably not due to single factor, but it is in no small part attributable to the fact that the recognition task in its variety of forms is amenable to the methodologies used to relate brain activity to behavior. Investigating the neurological basis of performance by utilizing fmri, PET, or EEG methods is much more difficult for recall than recognition, particularly during retrieval. Understanding recognition memory is no more important than understating recall in order to develop theories that relate the brain to behavior, but investigating recognition is more convenient given the available brain recording technologies. Thus, more recognition findings have been reported as the use of brain imaging technologies has increased. Recognition memory accuracy also does not decrease with age. Hence, understanding the nature of recognition might hold some of the keys to understanding the relationship between age and memory. Lastly, there now exist procedures for investigating recognition memory in animals, such as the delayedmatch-to-sample procedure (Eichenbaum & Cohen, 2001), and this allows researchers to build bridges between the theoretical gap between animal and human models of episodic memory. 3. RECOGNITION AS THE DETECTION OF AN INTERNAL SIGNAL EMBEDDED IN NOISE 8

9 Recognition memory occupies a prominent place in the current zeitgeist, and this research has produced important new findings. Despite the empirical progress, no consensus exists about how to explain them, as the wealth of recent review articles attest, and nowhere is debate more hotly contested than in traditional cognitive research. During the early years of recognition memory research, several models of recognition that were based on the simple assumptions of signal-detection theory were developed (Green & Swets, 1966; e.g., Banks, 1970 Bernbach, 1970; Kintsch, 1967; Lockhart & Murdock, 1970): The recognition judgment is based on a comparison of a continuous random variable obtained from memory to a criterion (see the upper panel of Figure 2). The random variable is often conceptualized as familiarity when applied to recognition memory, and the average familiarity of a target is greater than the average familiarity of a foil since targets were studied in the specified context. The placement of the criterion is determined by the subject. When set at high or strict levels, hits and false-alarms tend to be low compared to when the criterion is set at low or lenient levels. A dynamic version of the signal detection model was developed by Ratcliff (1978) to discriminate between the effects of bias and sensitivity on the accuracy and latency of recognition judgments. According to the Ratcliff s random walk model (and its continuous version the diffusion model), positive and negative evidence from the underlying distributions is sampled and accumulated over time at a certain variable rate. The evidence is compared to two decision criteria: a higher old criterion and lower new criterion. When one of the criteria is exceeded, a response is made. Increasing the difference between the amount of 9

10 positive and negative evidence that accumulates improves sensitivity and decreases latency. On the other hand, increasing the distance between the old and new decision bounds improves accuracy but with a time cost. Bias is modeled by the assumption that the subject determines the point at which evidence begins to accumulate. Increases in bias to respond old increase the hit rate and the false-alarm rate, decrease the latency of old responses, and increase the latency of new responses. With a few notable exceptions, the recognition memory literature has been concerned only with how different factors affect the accuracy of recognition memory. As such, the recognition literature has virtually ignored the random walk model. The detection models have been successful measurement tools, but they are limited by their simplicity. Because the assumptions underlying signaldetection models do not explain how the familiarity or strength of an item (or an association) is determined, however, their primary utility is their ability to measure how different factors influence sensitivity (Murdock, 2006). Still, the number of signal-detection models of recognition is growing. Signal detection models have recently been extended to discriminate between the contributions of item and associative familiarity (Kelley & Wixted, 2001) and specific and global strength (Rotello, Macmillan, & Reeder, 2004). Other models have been developed to account for the effects of strengthening operations and similarity on recognition memory (Heathcote, Raymond, & Dunn, 2006). And they have been extended to account for source memory (Qin, Raye, & Johnson, 2001; Slotnik, Kelin, & Shimamura, 2000) and relationship between recognition memory and source memory (Banks, 2001). 10

11 In the early 1980 s, several researchers developed models of the processes and representations involved in producing familiarity. These models are referred to as global-matching models, and they were very successful for several years (Gillund & Shiffrin, 1984; Hintzman, 1988; Humphries, Bain, & Pike, 1989; Murdock, 1982). Each can be considered as an instance of the signal detection class of models because the basis of the recognition judgment is a continuous random-variable. Accordingly, familiarity is obtained by comparing a temporary representation of the test item (i.e., retrieval cue) to the contents of memory. This typically involves a large number of traces. The familiarity that is associated with a retrieval cue is a positive function of the similarity between it and traces stored in memory. Because targets are relatively similar to at least one trace in memory, targets tend to be more familiar than foils, on average. One of the key advantages of the global-matching models is that they are but one part of a larger global-memory framework that accounts for a variety of episodic and semantic memory phenomena with just a few assumptions. Over time, however, a number of findings proved to be problematic for the globalmatching models. These included mirror effects (Glanzer & Adams, 1985); the shape of the recognition memory receiver operating characteristic (Ratcliff, Sheu, & Gronlund, 1992), the relationship between list-length and list-strength manipulations (Ratcliff, Clark, & Shiffrin, 1990). In recent years, several new global-matching models were developed including the bind-cue-and-decide model (BCDMEM, Dennis & Humphries, 2001), the retrieving effectively from memory model (REM; Shiffrin & Steyvers, 1997; 1998), the subjective likelihood 11

12 model (SLiM; McClelland & Chappell, 1998), and the theory of distributed associative memory model (TODAM; Murdock, 1997; 2006). BCDMEM, REM, and SliM belong to a subclass of global-matching models that are based on Bayesian computations. 2 These models are motivated by the assumption that cognitive systems have evolved to be optimal and adapt over time to their particular surroundings. Optimality is often cast in a Bayesian system, and the Bayesian computations of familiarity often mean that strengthening operations simultaneously increase the similarity between a target cue and its trace in memory and decreases the similarity between the target trace and other traces in memory. This is referred to as differentiation (Criss, 2006), and in one way or another, it allowed the new generation of global matching models to account for the findings that had proven difficult for the prior generation of models. Moreover, the Bayesian approach provides a principled reason for the location of the subjective criterion, and thus, the new generation of global-matching models can account for more data than their predecessors even as more restrictions are placed on the manner in which they do so. A downside of the development of the new generation of global-matching models has been the extreme focus on recognition memory, specifically single-item recognition. As a result, the models have, with some exceptions, become more specialized instead of more expansive in the coverage of the literature. 2 A different Bayesian approach to modeling recognition memory was taken by Glanzer and Adams (1990; Glanzer, Adams, Iverson, & Kim, 1993). Their attention/likelihood model assumed local access to memory produced a continuous random variable, and this model accounted for a number of findings, including mirror effects, but the local access assumption proved to be a severe limitation to its utility (Malmberg & Murnane, 2002). 12

13 4. DUAL-PROCESS MODELS OF RECOGNITION In the late 1980 s and early the 1990 s, many single-process models had been disconfirmed and braining imaging technology was quickly becoming available. 3 The time seemed ripe for a radical change towards dual-process models. According to dual-process models, recognition is performed in two ways: Recognition is based on the familiarity of the retrieval cue or it is based on recollecting episodic details that are informative to the recognition decision. To justify the more complicated dual-process model, researchers sought to observe its contribution to performance. Researchers were particularly interested in developing ways of measuring the relative contributions of recollection and familiarity (e.g., Jacoby, 1991). To do so, it is necessary to describe how recollection and familiarity are different. The distinction was perhaps best analogized by Mandler (1980): Consider seeing a man on a bus whom you are sure that you have seen before; you know him in that sense. Such a recognition is usually followed by a search process asking, in effect, Where could I know him from? Who is he? The search process generates likely contexts (Do I know him from work; is he is a movie star, a TV commentator, the milkman?) Eventually the search may end with the insight, That s the butcher from the supermarket! [p ] 3 The dual-process model might be dated as early as 1913; Mandler et al. (1969) cite Muller (1913) as the originator of the dual-process model. 13

14 It is intuitively obvious that recollection and familiarity can affect recognition memory, and intuition might be sufficient for some purposes, but the differences between recollection and familiarity must be more rigorously defined in order to measure their contributions. Some models assume that recognition is based on a continuous random variable (familiarity) and a discrete random variable based on a threshold-like process (Malmberg, Holden, & Shiffrin, 2004; Reder et al., 2000; Yonelinas, 1994). Other dual-process models assume that there are two independent continuous random-variables that are combined to affect recognition decisions (Kelley & Wixted, 2001; Murdock, 2006; Rotello et al., 2004). Thus, the added complexity of the dual-process theory led to a variety of different models. It is instructive to note that traditional single-process investigations of recognition memory have been concerned with somewhat different issues than dual-process investigations. The earliest dual-process model was extensively investigated by R. C. Atkinson and his colleagues in the early 1970s. This model was particularly adept at accounting for the response latencies when items were learned to very high levels of accuracy. Hence, the foundations of dual-process theory were in accounting for dynamics of recognition memory, while singleprocess models have almost always ignored the latency of recognition memory. G. Mandler (1980; 1991; Mandler et al., 1969s) advocated the dual-process approach based on his observations that the organization of episodic memories influenced both recognition and recall and on the correlations between cuedrecall and associative recognition performance. Single-process investigations, on 14

15 the other hand, have typically been less interest in memory organization, and more interested in memory strength. Given these findings in the very early literature that did not immediately support the single-process familiarity-based model, why wasn t the debate settled at that point and why in fact was the global-matching paradigm dominating? To some extent, the equivocation was due to attrition; the leading figures on the dual-process side of the debate moved on to other interests. Another reason is that dual-process models were not as convenient as the single-process model. For instance, the dual-process model did not provide simple ways of distinguishing between sensitivity and bias, an issue that has been largely ignored to this day (but see Buchner & Erdfelder, 1995; Yonelinas & Jacoby, 1996). Perhaps most importantly, however, there were many more findings that seemed to be more difficult to explain with the framework of the dual-process model. Several of these are noted are noted in subsequent sections of this article. Unlike the random walk model, the dual-process model was not ignored. Rather, it was usually explicitly rejected. G. Gillund and R. M. Shiffrin (1984) evaluated the arguments in favor of and against the dual-process model. They found no compelling reason to pursue the more complicated dual-process approach, and they determined that the global-matching approach was simpler and more expansive in its coverage of the literature. One of the reasons for making this conclusion might have been the historical lack of attention paid to the temporal dynamics of recognition memory (cf. Atkinson s work), although the retrieval dynamics that Gillund and Shiffrin observed were actually consistent with the single-process approach. Nevertheless, the relationship between 15

16 accuracy and latency was important to B. B. Murdock (1982; cf. Ratcliff & Murdock, 1976), and he too preferred the global-matching model for his TODAM. A few years later, A. P. Yonelinas (1994; 1997; 1999) capitalized on the newly rediscovered utility of ROC analyses that was central to testing the first generation of global-matching models (Ratcliff et al., 1992, 1994). In doing so, he developed a new paradigm for distinguishing between single-process and dualprocess models. The production of ROC curves has since become a virtual cottage industry (Glanzer, Kim, Hilford, & Adams, 1999; Heatchcote, 2003; Hilford, Glanzer, Kim, & DeCarlo, 2002; Van Zandt, 2000; Yonelinas, 2002), and thus many of the arguments used to support one view versus the other hinge upon them. The ROC analysis also has enormous implications for how one interprets data from another important dual-process paradigm, remember-know recognition. 5. A COMPARISON OF RATINGS AND REMEMBER-KNOW MEASURES OF RECOLLECTION A primary goal of dual-process theory is to develop measures of the contribution of recollection to recognition performance. For instance, a receiver operating characteristic or ROC analysis involves the collection of pairs of hit and false-alarm rates at different levels of response bias, and there are a variety of ways to do so (Macmillan & Creelman, 1991; see Malmberg, 2002 for some factors that lead to the choice of methods). 4 The most common method utilizes a 4 Malmberg (2002) advocated the use of yes-no ROCs rather than ratings ROCs as a means of testing models of recognition memory. One reason that yes-no ROCs are not commonly used is that generating yes-no ROCs is less efficient. Another reason is because process-pure dual- 16

17 confidence rating task. Take, for instance, the single-process signal-detection model of the rating task shown in the middle panel of Figure 2. There are 6 levels confidence from which the subject can choose, and the subjective rating is determined by setting five criteria to partition the familiarity scale. The confidence rating corresponding to the region of the decision scale in which a sample from the familiarity distribution falls is the response given by subject. The critical assumption is that confidence is a proxy for bias (Egan, 1958): High levels of confidence require greater amounts of evidence than do lower levels of confidence. Ratings based on familiarity in dual-process models are made in the same manner. However, the highest confidence old response is often reserved for recognition decisions based on recollection (Joordens & Hockley, 2001; Reder et al., 2000; Yonelinas, 1994). A. P. Yonelinas (2002) related ROC analyses to another procedure that was being used within the framework of dual-process to measure recollection. This procedure was first advocated by E. Tulving (1983) and made by popular by his former student J. Gardiner (1988). Tulving assumed that there were conscious and unconscious contributions to recognition memory. If so and if the subject is aware of the current state, then one could simply ask the subject what his basis for the recognition judgment was. This has become known as the remember-know procedure. If the subjects base their judgments on recollection of a prior encounter with the stimulus, then they should answer, remember, according to dual-process models. If subjects base their judgments on a feeling of process theories have trouble interpreting the influence response bias on the use of the ratings response that is usually reserved for recollection-based recognition (cf. Hirshman & Henzler, 1998). 17

18 knowing that an item was encountered in the specified context, then they should answer, know. Even though the limitations of metacognitive introspections are well-known and under most conditions they are not very accurate measures of mnemonic abilities or states (Nelson & Narens, 1990), it is still an empirical question as to whether the dual-process measures of recollection that are derived from remember-know judgments are valid (Nelson, 1996). The key question is whether the remember-know measure reflects a state of recollection. According to single-process models, for instance, W. Donaldson (1996) proposed a signal-detection model to describe remember-know performance; the subject uses two criteria, where a familiarity value exceeding the strictest one results in a remember response, a familiarity value falling between the two criteria leads to a know response, and familiarity values less than the lenient yes-no criteria result in new responses. This model is shown in lower panel of Figure 2. According to the signal-detection model, rememberknow responses do not reflect different memory states. Rather, they reflect the amount of evidence on which the decision was based. A. P. Yonelinas (1994) noted that the single-process and dual-process models predicted different shapes of the recognition ROC curve, and he applied the ROC analyses for the first time to the debate between single-process and dual-process models. His conclusion was that the dual-process model provides a better account of the form the ROC. On the other hand, J. Dunn (2004) has shown that the signal-detection remember-know model is consistent with a large number of findings, and J. T. Wixted and V. Stretch (2004) agued that many 18

19 findings from the remember-know procedure actually favor the single-process model. Thus, there is evidence derived from ROC analyses and remember-know data that has been interpreted to favor the single-process model and evidence that has been used to support the dual-process model. Prior reviews of this literature have tended argue from position or the other. Here, I simply want to take a look at the bases for these arguments and evaluate them. The arguments in favor of the dual-process model are usually based on statistics that are derived from observed data, and an important question is whether the dual-process measures of recollection provide reliable results. The manner in which a measure of recollection is computed depends on whether data come from ratings or a remember-know experiment. The ratings measure is based on a statistic derived from a fit of the model to the ROC, whereas the remember-know measure is based on statistic derived from a transformation of metacognitive introspections. Thus, the critical issue for the many dual-process theories concerns whether the measures derived from the ratings and remember-know procedure produce consistent results. If the ratings and remember-know measures tap the same construct, as many assume they do, they should produce the same result. In what has been referred to as a process-pure version of the dual-process model (Rotello et al., 2004), the strictest old rating is reserved for responses based on recollection. In most cases, however, the probability of assigning the highest confidence to a foil is greater than zero, and an estimate of the contribution of recollection to performance must be obtained by fitting the dualprocess ratings model to the ROC. The best-fit of the model produces a 19

20 parameter estimate that corresponds to the point where the ROC intersects the old-item axis of ROC space (Yonelinas, 1994). Let us call this estimate, Rratings. It is displayed in Figure 3. If the dual-process theory measures of recollection are consistent, then Rratings derived from rating procedure should be the same as the measure of recollection obtained from the remember-know procedure. However, obtaining an estimate of the contribution recollection to performance using the remember-know method is more complicated. The remember-know task, as it was originally conceived, produces an estimate of recollection and familiarity-based responding by observing the probabilities of responding remember and know respectively. These probabilities correspond to a process-pure measure in the same way that the probability of responding old with the highest degree of confidence measures recollection for the ratings procedure. In practice, however, the process-pure standard is rarely achieved. W. Donaldson (1996; also see Wixted & Stretch, 2004) noted, for instance, that subjects tend to respond remember and know in response to both targets and foils (P Rem -target, P Rem -foil, P Know -target, P Know - foil). The tendency for subjects to use the remember response when foils are tested undermines both the process-pure assumption and the assumption that subjects are able to accurately judge the basis of their recognition response. The critical question is: why do subjects say that they remember studying items that were not studied? The response of many investigators is to assume that there is some error in remember-know responses, and they developed methods for transforming raw data in order to provide more accurate estimates of recollection and familiarity. If the ratings task and the remember-know task 20

21 tap the same information, the estimates of recollection derived from these tasks should be the same. Consider the ROC space shown in Figure 3. Three possible locations corresponding to P(remember) are plotted. Note that they all fall on the same ROC, and hence they should all produce the same estimates of recollection. These data also correspond to the three-point ROC obtained from a fouralternative ratings task (cf. Malmberg & Xu, 2006). Thus, both the ratings and remember-know estimates derived from the same ROC should be identical. In the case of the ROC shown in Figure 3, Rratings is estimated to be about.20 because this is where the ROC intersects the old-axis. How do the various dualprocess remember-know measures of recollection correspond to this estimate? Since 1988, several different remember-know estimates of recollection have been used. Some have advocated using the difference between PRem-target and Prem-foil as a measure of recollection (Yonelinas, 2002). In order to determine if the ratings measure (i.e.,.20) and this remember-know measure of recollection are the same, consider the middle point in ROC space shown in Figure 4. PRem-target and Prem-foil are about.57 and.10, respectively. Thus, the estimate of recollection obtained from the remember-know procedure would be.47, which is about two and half times as great as the estimate obtained from the rating method. Yonelinas, Kroll, Dobbins, Lazzara, and Knight (1998) advocated normalizing the difference between PRem-target and Prem-foil by (1 Rrem-foil). In this case, the remember-know estimate of recollection is:.47/(1 -.10) =.52, which is even less in line with the estimate derived from fitting the 21

22 dual-process model to the ratings ROC. The dual-process estimates of recollection derived from the ratings and remember-know task are not the same. Since the measures of recollection offered by the dual-process theory do not jibe, it is important determine whether the other assumptions in this framework are valid. For instance, C. M. Rotello et al. (2004) argued against the process-pure assumption, and hence also the against the validity of the ratings estimate of recollection. Indeed, several experiments have reported that the P(remember) is affected in predicable ways by factors that can influence bias (e.g., Hirshman & Henzler, 1998). What would happen to our estimates of recollection derived from the ratings and remember-know procedure if the process-pure assumption is violated? Consider the more extreme points in ROC space depicted in Figure 3: (.85,.45) and (.40,.05). These points fall on the same ROC and hence differ only in bias. Therefore, these points should produce the same remember- know estimate of recollection as the middle point on the ROC (i.e.,.47) if the transformations simply eliminate the noise in the data. For these points, however, the estimates of recollection using the Yonelinas (2002) method are.40 and.35, which are again very different from the estimate of.20 obtained from the rating method, and they are also different from the estimate of.47 obtained from the middle point on the ROC. The remember-know estimates are also affected by bias in a non-linear way with respect to the minor diagonal when you consider the estimates of recollection for all three points:.35,.47, and.40. The Yonelinas et al. (1998) method does not fair much better. It produces estimates of recollection for the two extreme points of.37 and

23 The estimates of recollection based on the ratings and remember-know methods are inconsistent. More specifically, the remember-know measures generally overestimate the contribution of recollection relative to the dualprocess ratings measures of recollection. At this point, it is unknown whether one is an accurate measure or whether both measures are inaccurate. These comparisons of the dual-process measures of recollection are based on the assumption that the rating and remember-know ROC are the same. This is assumption was investigated by C. Rotello et al. (2004), who reported a metaanalyses of 100 s of ratings and remember-know ROCs. From this meta-analysis, they concluded that slope of the ratings ROC was less than 1.0 and the slope of remember-know ROC was not. In other words, C. Rotello et al. concluded that the ratings ROC was asymmetrical and the remember-know ROC was symmetrical. If this is true, these findings are equally problematic for both single-process and dual-process accounts of recognition memory. C. Rotello et al. s conclusion, however, seems inconsistent with the analysis provided by J. Dunn (2004; also Malmberg, Zeelenberg, & Shiffrin, 2004), who showed that the single-process signal-detection provided a superior fit to the data used in the Rotello et al. s meta-analysis. Wixted and Stretch (2004) argued that the difference between the slopes of the ratings and remember-know ROCs might have been due to differences in the amount of decision noise that contribute to their performance. Malmberg and Xu (2006; and Heathcote 2003; Ratcliff et al., 1994) showed how decision noise could affect the shape of the ROC. More importantly, Malmberg and Xu noted that the remember-know and ratings ROCs used by Rotello et al. were based on data 23

24 averaged over subjects. They showed how the shapes of the ratings and remember-know can be differentially distorted by the averaging process, and they conducted model analysis and empirical studies that were inconsistent Rotello et al. s conclusion and consistent with the conclusion that the rating and rememberknow ROCs are the same. Hence, there is little support for the assumptions underlying the dualprocess measures of recollection, and therefore they do not provide a sound basis for arguments in favor or against any particular model. This, of course, does not necessarily mean that all arguments against the dual-process model are correct, however. For instance, Wixted and Stretch (2004) delivered what appeared at the time to be a devastating blow to dual-process accounts of the remember-know and ratings procedures by demonstrating that high-confidence old responses and remember responses are actually faster than low-confidence old responses and know responses. They showed that this was true for both targets and foils, and thus it was difficult for the dual-process model to explain why a slower recollective process would produce faster responses. R. Diana et al. (2006) disputed this claim by assuming that recollection was always attempt prior to making a response in their dual-process models. As the default source of the ratings and remember-know decisions, recollective responses should be faster than familiarity-based responses. Wixted and Stretch (2004) also suggested that such findings were easily explained by single-process signal detection models in that all high confidence responses should be relatively fast, regardless of whether the stimulus is a target or foil. The problem for this interpretation of the data is that no such signal- 24

25 detection model exists. For instance, the random walk model is a model of yesno recognition, and it has nothing to say about the latencies of ratings or remember-know judgments. Thus, it is unclear what signal-detection model was used to generate the predictions described by Wixted and Stretch. Ratcliff (1978) did speculate that the random walk model could be used to model confidence ratings by assuming that the subject sets a set of internal time deadlines, and those test trials that produce relatively fast yes-no decisions are also given relatively high confidence-judgments. But this just kicks the can down the road, because the single-process signal-detection model must assume in order to account for ratings and remember-know latencies that these judgments are not based on familiarity or mnemonic evidence. If so, confidence is now a proxy for latency and not for bias, and the single-process interpretation of the ratings and remember-know data is fundamentally altered. In summary, the single-process signal-detection model provides a simple, parsimonious measure of sensitivity, but it comes up in short in its ability to account for the accuracy and the latency of confidence ratings and rememberknow judgments. However, the inability of the single-process model to account for the latencies of these judgments is not major strike against it. Investigations of the nature recognition are interested in recognition accuracy and not confidence or judgments concerning remembering-versus-knowing. Indeed, the random walk model provides a reasonable account of the accuracy and the latency of yes-no recognition under a variety of conditions (Ratcliff, 1978), and the focus of the field on confidence and remember-know procedures has diverted our attention from with what memory research typically concerns itself. 25

26 On the other hand, the dual-process measures of recollection lack the necessary consistency to be convincingly valid. When the evidence is take together, the dual-process assumption that the confidence ratings and remember-know measures tap the same underlying aspects of memory is not supported, and at the same time there is no evidence that the performance of the ratings and remember-know tasks are based on different information. Let us understand that this conclusion is not that the single-process model is correct and the dual-process model is incorrect. We do not whether this true. Rather, the estimates of recollection derived from dual-process theory cannot be regarded as strong bases for arguments for (or against) the dual-process interpretation of recognition memory. 6. REMEMBERING AND KNOWING IN GLOBAL-MATCHING MODELS The single-process signal-detection model provides an accurate and parsimonious interpretation of accuracy of ratings and remember-know performance. Others have noted, however, that these models are not models of familiarity (Murdock, 2006). This is true, and therefore it is important that models of familiarity exist, that they are consistent with the measurements derived from signal-detection analysis, and that these models can explain how different factors affect remember-know performance. To the extent that there are viable models of familiarity, the signal detection position is supported. The relationship between detection-level measurement models and global-matching process model is often ignored in the literature. 26

27 In this section, I will discuss how the signal-detection and global-matching models are related and then I review the applications of REM and TODAM to the remember-know task. I will focus this discussion on models of the rememberknow task in order to simplify the arguments. In addition, because less is known about the remember-know models compared to rating models, this provides an opportunity to fill in some of the important gaps in the literature. The rating and remember-know models are very similar (see Figure 3), and most if not all of our discussion apply to both. Modeling remember-know performance in a global-matching model is somewhat different than modeling remember-know performance in a signaldetection framework. The signal detection analysis simply requires a fit of the model to the data, and from the fit, the data is interpreted as either being consistent or inconsistent with unimplemented assumptions of the model. That is, the means and variances of the underlying familiarity distributions are not known until the data are collected. For this reason, signal-detection models make relatively few testable predictions (cf. Murdock, 2006). Modeling recognition performance in global-matching models also usually requires a fit to the data, but to do so one must first describe how events are stored in memory and how information about these events is retrieved at test. One also needs to describe how different factors influence the encoding and retrieval of events. When these assumptions are formally implemented, qualitative and quantitative predictions can be derived by varying the parameters of the model before the data are collected. If the subsequent fit of the model to the data is good, then the behavior of the model can be precisely specified and the 27

28 behavior of the subject inferred from the model s behavior. If the assumptions of the global-matching model are incorrect, the model is disconfirmed. Even so, a signal detection analysis still might be consistent with the data (cf. Malmberg & Murnane, 2002). This suggests that signal detection models can be evaluated independently of models of familiarity, but this is not really the case. If the model of familiarity is disconfirmed, the signal-detection interpretation of the data is undermined because there might be no explanation for how familiarity is produced that is consistent with the signal detection interpretation of the data. The implementation of specific set of assumptions about how a factor might affect performance puts strong constraints on the model. Within a sufficiently rich theoretical framework, there are different ways that a factor can potentially affect performance. The modeler usually selects from these different alternatives one implementation of the model that is consistent with prior assumptions. Within a global-memory framework, one often initially seeks to provide parsimonious accounts for how different recognition tasks are affected by a given factor. For instance, to explain the relationship between yes-no recognition and remember-know recognition, the modeler would typically seek to only vary the model of the recognition task and not, say, the model of encoding. The parsimony that exists between models of different tasks will become increasingly important as we consider a wider range of findings. An advantage of modeling memory process is that requiring the modeler to describe how different factors affect familiarity puts constraints on how the models can be meaningfully tested. The constraints are determined in part by the data that they were designed to account for and in part by the development of a 28

29 consensus on which findings are theoretically important to explain. For instance, extant global-matching models were developed primarily to account for the performance of the yes-no and ratings tasks, and there is general agreement in the field that these models should be able to account for how list length, item strength, list-strength affect performance and how different factors produce mirror effects and affect the slope the zroc (Dennis & Humphreys, 2001; McClelland & Chappell, 1998; Murdock & Kahana, 1993; Shiffrin & Steyvers, 1997). SLIM, REM, BCDMEM and other global matching models can account for these benchmark findings. Since their development, it has become increasing important for them to also provide similar accounts of remember-know performance. However, the current generation of global-matching models has not been fully extended to account for the remember-know task performance. Two models have: REM (Malmberg, Zeelenberg, & Shiffrin, 2004) and TODAM (Murdock, 2006). It is important to consider these models because rememberknow data are sometimes used to undermine them (E.g., Diana et al., 2006; Macmillan & Rotello, 2006; Yonelinas, 2002). It is also important to recognize that the REM and TODAM accounts are very different and the existence of more than one model provides the potential for us to learn something important about the nature of familiarity. Thus, the conclusions that I have drawn in prior sections of this article are buttressed by the existence of viable models of familiarity. Malmberg, Zeelenberg, and Shiffrin (2004) described a REM model of remember-know task performance that is based on Donaldson s (1996) original detection model. As such, there exist two decision criteria to which the 29

30 familiarity obtained from memory is compared, and therefore remembering and knowing reflect different levels of familiarity. Such remember-know models seemed to be inconsistent with findings of Hirshman et al. (2003; Also see Balota et al., 2003), who varied normative word frequency and study time in subjects who were under the influence of midazolam or saline during study but not during test. The subjects in the saline condition produced better performance as study time increased and a mirror effect for high-frequency versus low-frequency words was obtained. In addition, higher levels of study time produced greater probabilities of remembering, as did low-frequency words. Under the influence of midazolam, the findings were different. The critical findings were that study time had very little influence on recognition accuracy, the low-frequency hit-rate advantage was reversed, but the low-frequency false-alarm rate advantage was unaffected by midazolam. In addition, the tendency to respond based on remembering was equivalent for high and low frequency words. Similar findings concerning the effect of aging on recognition memory have also been reported (Balota et al., 2003). These findings were interpreted by many to be (at the very least) consistent with the dual-process assumptions that low-frequency targets are recollected more often than high-frequency targets and that the low-frequency false-alarm rate advantage was based on the pre-experimental familiarity of the foils. In response, Malmberg, Zeelenberg, and Shiffrin (2004) provided four different explanations for Hirshman et al. s findings within the framework of a REM single-process remember-know model, the simplest of which is that midazolam affects the accuracy with which features representing events are 30