FALSE MEMORY INCREASED BY SELF-GENERATION IN COMPARISONS OF RECOGNITION AND RECALL. Andrew M. McCullough

Transcription

1 FALSE MEMORY INCREASED BY SELF-GENERATION IN COMPARISONS OF RECOGNITION AND RECALL Andrew M. McCullough A Thesis Submitted to the University of North Carolina Wilmington in Partial Fulfillment of the Requirements for the Degree of Master of Arts Department of Psychology University of North Carolina Wilmington 2010 Approved by Advisory Committee Julian R. Keith Dale J. Cohen Jeffrey P. Toth Chair Accepted by h Dean, Graduate School

2 TABLE OF CONTENTS ABSTRACT... iv LIST OF TABLES... v LIST OF FIGURES... vi INTRODUCTION... 1 COMPARING RECOGNITION AND RECALL... 4 THEORIES OF RECOGNITION AND RECALL... 7 Theories of Recognition... 9 Theories of Recall Metacognitive Processes Directly Comparing Recognition and Recall False Memory in Recognition and Recall OVERVIEW OF EXPERIMENTS EXPERIMENT Memory and Detection Theory Current Predictions Methods Participants and design Materials Procedure Results and Discussion Confidence ratings ii

3 Sensitivity and bias ROC-based measures EXPERIMENT Current Predictions Methods Participants, design, and procedure Results and Discussion Type 1 analyses Type 2 confidence Metamemory parameters ROC-based measures GENERAL DISCUSSION TABLES FIGURES REFERENCES iii

4 ABSTRACT The overarching goal of this research was to explore the relation between recognition and recall. A more proximate goal was to investigate a recent report of elevated false memories in cued recall relative to recognition. Two experiments are presented that compared performance on matched tests of recognition and cued recall, and replicated this increase in false memory in confidence ratings (Experiment 1) and binary judgments (Experiment 2). Type-1 signal detection analyses of memory performance (Experiments 1) revealed changes in both sensitivity and response bias. In Experiment 2, the effect on type-1 sensitivity was observed with no effect on response bias. Type-2 analyses (Experiment 2) revealed no effects of test condition on metamemory for studied-item trials, but significant effects on metamemory confidence bias for unstudied-item trials. These results clarify prior studies, showing that self-generation in cued recall impairs memory sensitivity, and imply that previous, inconclusive results were obscured by metacognitive processes. iv

5 LIST OF TABLES Table Page 1. Proportions of Recollect, Familiar, and New Judgments for Recognition and Cued Recall Type-1 Signal Detection Measures and Parameters for Recognition and Cued Recall Type-1 Signal Detection Measures and Parameters for Recognition and Cued Recall in Experiment Mean Type-2 Confidence Type-2 Signal Detection Measures and Parameters v

6 LIST OF FIGURES Figure Page 1. Classification model for Type-1 responses and theoretical signal detection distributions General interpretations of memory data according to signal detection theory Receiver operating characteristic curves for recognition and cued recall in experiment Classification model for Type-2 responses and theoretical signal detection distributions Type-2 receiver operating characteristic curves for recognition and cued recall in experiment vi

7 FALSE MEMORY INCREASED BY SELF-GENERATION IN COMPARISONS OF RECOGNITION AND RECALL INTRODUCTION Two methods for measuring memory performance are recognition and recall tasks, and both are analogous to everyday uses of memory. While recognition refers to an awareness of having previously experienced some target information, recall requires the (re-)production of previously experienced information. In recognition tasks, people are presented with an item (e.g., a word, a face, a melody) and are asked to judge whether they have experienced that item in the past, usually in some specific context (i.e., a study list). For recall tasks, people are reminded of a specific past event and asked to produce (i.e., self-generate) some items that occurred within it. Everyday life provides abundant examples of this contrast between recall and recognition. After attending a weekend party, for example, one might be asked by a friend who was unable to attend, "who was at the party?" (a recall question), followed by, "was John there?" (a recognition question). The distinction between recall and recognition is also critical for the scientific study of memory. However, although both are generally viewed as conscious, "explicit" forms of retrieval (Cabeza et al., 1997) they are often studied separately, and have engendered different methods, phenomena, and theories (Haist, Shimamura, & Squire, 1992). The ubiquity of the distinction between recall and recognition raises two important issues. The first concerns the extent to which recognition and recall engage similar underlying processes. As described more fully below, attempts to understand the relation between these two ways of expressing memory has a long history in psychology; however, it is difficult to find clear statements in the literature about exactly how these two tests are related. The second issue

8 concerns false memories. That is, to what extent are these two forms of memory susceptible to errors and distortion? The study of false memory has become a central issue in both cognitive psychology and cognitive neuroscience (e.g., Schacter & Tulving, 1994), not only because of its relevance in applied contexts such as eye-witness testimony (see Loftus, 1975), but also because it is viewed as providing clues to the more general operation of memory, both accurate and false (see Jacoby & Rhodes, 2006; Johnson, Hashtroudi, & Lindsay, 1993; Schacter, Norman, Koutstaal, 1998). The purpose of the present research was to address the two issues raised above. Two experiments are presented that directly compared recall and recognition in an attempt to better understand the relation between the two, and to investigate whether the generation process underlying recall makes this form of retrieval more susceptible to false memory. The research is based on unpublished studies by Toth (2005) and McCullough & Toth (2008), both of which found evidence for a high degree of similarity in performance on matched tests of recall and recognition, as well as evidence that, relative to recognition, recall increases false memory. Importantly, those studies employed a "remember/know" methodology (Tulving, 1985) that, while used extensively in the memory literature (see Gardiner, Ramponi, & Richardson-Klavehn, 2002; Gardiner & Richardson-Klavehn, 2000), has been criticized on theoretical grounds (Dunn, 2004; Rotello, Macmillan, Reeder, & Wong, 2005; Wixted & Stretch, 2004). Thus, one specific goal of the present research was to replicate those prior findings using a more standard memorytesting technique, confidence ratings. A second goal of the present research was to replicate and extend our prior findings by analyzing memory performance using Receiver Operator Characteristic (ROC) curves and other measures from both type-1 and type-2 Signal Detection Theory (SDT). Type-1 SDT is 2

9 commonly used in the analysis of recognition memory performance (for a review, see Yonelinas & Parks, 2007) where it provides measures of both sensitivity (i.e., the ability to discriminate between studied and unstudied items) and response bias (i.e., the overall tendency of a person to respond old ). The application of type-1 SDT to recall is less common, given the participants role in producing both studied and unstudied test items (Green & Swets, 1966), but there are exceptions (see Higham, Perfect, & Bruno, 2009). The present research examined recall in a paradigm that encouraged production of unstudied items, and therefore allows SDT-based measures to be computed. Although type-1 SDT provides a solid theoretical basis for analyzing memory performance, it does not incorporate metacognitive processes that have recently been argued to be critical for regulating mnemonic accuracy (Koriat & Goldsmith, 1996; Koriat, Goldsmith, Pansky, 2000; Perfect & Schwartz, 2002). However, Higham has recently shown how type-2 SDT can be used to measure metacognitive processes, both in recall (Higham & Tam, 2005) and recognition (Higham et al., 2009). In the context of memory, type-2 SDT provides two metacognitive measures of performance that are useful for describing the relationship between response accuracy and confidence (for a review of type-2 SDT, see Galvin, Podd, Drga, & Whitmore, 2003). Monitoring (analogous to sensitivity) reflects a person's ability to discriminate between correct and incorrect responses, while confidence bias (analogous to response bias) reflects the tendency to accept their answers as correct. The present research also employed type- 2 SDT methods in order to compare the metacognitive processes associated with recall and recognition, and to investigate their role in the production of false memories. The remainder of this introduction is designed to more fully explain and motivate the current research. I start by describing the research by Toth (2005) and McCullough and Toth 3

10 (2008) in more detail. I then provide a theoretical background for the two main issues addressed in the current research, that is, the relation between recognition and recall, and findings of false memories on these tests. One specific false memory phenomenon the revelation effect is described in particular detail because it bears a high similarity to the effects explored in the present research, and thusly motivated the use of ROC curves and SDT for these investigations. I conclude this introduction with an overview of the experiments. Details about the application of type-1 SDT to recognition and recall are provided in the introduction to Experiment 1; details about type-2 SDT are provided in the introduction to Experiment 2. COMPARING RECOGNITION AND RECALL IN THE REMEMBER/KNOW TASK Toth (2005) compared recognition and recall in six experiments using variants of the remember/know procedure initially introduced by Tulving (1985; see also Gardiner & Richardson-Klavehn, 2000). Experiment 1 by Toth provides an example of this procedure, along with the mixed-test design that was adapted for the current research. In this experiment, participants studied a list of common, 5-letter English words (e.g., truck, smart ), and were then given a memory test that included both recognition and recall trials. On recognition trials, participants were shown studied or nonstudied words which they were asked to classify as Recollected (R), Familiar (F), or New (N). Participants were told to classify a word as R when they could clearly remember details surrounding their initial study of the word, F when the word was familiar but they could not remember any specific study details, and N when the word neither elicited details nor was familiar. 1 On recall trials, participants were presented with word stems (e.g., tru--, sma-- ) which they were told to use as cues for recalling words studied 1 Rather than Familiar, many researchers use the term Know to denote items that are familiar but lack episodic detail. 4

11 earlier, guessing if necessary. After completing the stem, they then provided an R/F/N judgment, just as they had for recognition trials. The data from this experiment, mean proportions of R, F, and N judgments for recognition and recall trials, are shown in Table 1a. Note that, for recognition trials, participants provided a response to every studied item, and an equal number of unstudied items, and thus these proportions sum to 1.0. For the recall trials, in contrast, participants did not have the opportunity to respond to every item in the stimulus set because they sometimes failed to generate these words. The recall data is thus presented in two ways, as unconditionalized and conditionalized proportions. The unconditionalized proportions (bottom row) represent the proportions of R, F, and N responses out of all possible trials (i.e., out of all possible studied and unstudied items cued in recall); thus, these values sum to the mean proportions of studied and unstudied items that were output in response to all of the recall cues. For the conditionalized proportions, the number of R, F, and N responses is divided by the number of studied or unstudied words output. 2 Note that this correction is analogous to that used by Koriat and colleagues to compute memory accuracy (see Koriat & Goldsmith, 1994, 1996). More relevant for present purposes, it puts the recall data on the same scale as recognition (i.e., the conditionalized recall proportions sum to 1.0) thereby allowing a direct comparison of performance in the two test types. Two interesting results emerged from this experiment. First, looking at studied items (left panel of Table 1a), one can see that performance was nearly identical between recognition and conditionalized recall. These data suggest the processes underlying recognition and recall are highly similar, differing primarily in initial access to (i.e., the source of generation of) 2 As in the priming literature, restricting analyses to the critical set of target words allows the effects of studying an item to be compared to a baseline of outputting the same item when it had not been studied. 5

12 relevant target information. Once this difference is removed, by conditionalizing recall proportions on output of test items, recognition and recall performance are nearly identical. Despite this evidence of similarity between processes underlying recognition and recall (see also, Hamilton & Rajaram, 2003), the data revealed an important difference between test formats. In particular, the proportion of F responses to unstudied words (right panel of Table 1a) was much higher in conditionalized recall than in recognition. This elevation in false alarms for cued recall, replicated in five additional experiments by Toth (2005), suggests that the process of generating items increased their subjective familiarity, compared to when those items were presented externally (i.e., by the computer) on the recognition memory trials. McCullough and Toth (2008) replicated the above findings using a novel sample (UNCW undergraduates), a novel set of stimuli, and a slightly different test procedure asking participants to first make an old/new (i.e., studied/unstudied) judgment, followed by a recollect/familiar classification only for the items judged old. As shown in Table 1b, both patterns described above were replicated. That is, for studied items, there was a close correspondence of response proportions across test type (all differences n.s.) while, for unstudied items, there was a significant increase in F responses in recall as compared to recognition. 3 This consistent finding is particularly interesting when one considers that the set of (generated) items in which the false alarms increase are all words that are counterbalanced to recognition trials. Consequently, the procedure allows one to conclude that something inherent to self-generation is increasing false memory in cued recall. What explains the increased false alarm rate in cued recall observed by Toth (2005) and McCullough and Toth (2008)? One possibility is that, relative to recognition, self-generation of 3 Note that conditionalized recall proportions are necessarily interdependent (i.e., constrained to sum to 1.0); thus, the increased proportion of F responses is accompanied by a reduced proportion of N responses. 6

13 items in recall increases the processing fluency, and thus the perceived familiarity, of these items. Fluency has been identified as an important factor affecting recognition memory and other judgments (Jacoby & Whitehouse, 1989; see also Oppenheimer, 2008; Whittlesea, 1993; Whittlesea & Williams, 2001). However, research shows that processing fluency affects response criterion placement (Whittlesea; see also Jacoby & Whitehouse), suggesting that effects would be similar for studied and unstudied items. The consistent finding that hit rates do not differ between recognition and cued recall (Toth; McCullough & Toth) refutes a simple fluency-based explanation of the increased false alarm rate. The finding that an independent variable affects unstudied, but not studied, items is unusual and relevant to theories of memory (see Jacoby, Shimizu, Daniels, & Rhodes, 2005). One potential way of explaining this pattern is to assume that self-generation in recall produced a shift in the strength or familiarity distributions underlying old and/or new items, perhaps in addition to a change in response bias. Indeed, an explanation of this sort has been put forth to explain a similar pattern of data (i.e., a memory effect selective to unstudied items) in the revelation effect literature (Verde & Rotello, 2004). One of the goals of the present research is to evaluate whether a similar distribution-shift explanation can explain the difference in false alarm rates for recall vs. recognition. I return to this issue in the False Memory section below. First, however, I provide some background on the nature of the processes thought to underlie recognition and recall. THEORIES OF RECOGNITION AND RECALL What are the processes that underlie recognition and recall performance? An early idea was that the two forms of retrieval reflected the same memory process activation of an 7

14 underlying memory trace and that performance differences between the two simply reflected the superiority of recognition in activating the trace (Hunt & Ellis, 2004). This idea was consistent with the fact that recognition is generally easier than recall, and often results in higher memory scores. It is also consistent with Tulving's (1983) claim that recognition is similar to recall, but involves a highly specific copy cue for retrieval. However, subsequent research cast doubt on at least a simple form of this single-process hypothesis, which predicts that (a) recognition will always be higher than recall, and (b) recognition and recall will be affected similarly by manipulations that affect memory strength. Both of these predictions have been refuted. Specifically, recall can exceed recognition, as in the case of recognition failure of recallable words (Tulving & Thomson, 1973; see also, Watkins, 1974), and some experimental variables, such as word frequency (see Gregg, 1976), have opposite effects on recognition and recall performance. Given these findings, most of the research over the last two decades has progressed as independent domains and there are few clear statements about the relationship between recognition and recall. However, from those decades of research, a variety of theories have been proposed to explain each of these types of retrieval separately. In the following subsections, I provide brief overviews of these theories. I then describe recent research and theory on the role of metacognitive processes in memory tasks. These developments provide motivation for the current Experiment 2, which used type-2 SDT to explore metacognitive processes in recognition and recall. I conclude this section by describing research that has directly compared recognition and recall, noting the advantages and disadvantages of the mixed-test approach used in the present research. 8

15 Theories of Recognition Early theories proposed a single, evaluative process to underlie recognition (see Bernbach, 1967), such that test items are evaluated along a psychological scale of evidence often characterized as memory strength or familiarity. This "single-process" view of recognition is embodied in Global-Matching Models, such as SAM (Gillund & Shiffrin, 1984) and MINERVA 2 (Hintzman, 1988), and in the application of detection theory (Green & Swets, 1966) to recognition (e.g., Donaldson, 1992; Donaldson & Good, 1996; MacMillan & Creelman, 2005; Mickes, Wixted, Wais, 2007). However, numerous studies suggest that, in addition to a sense of memory strength or familiarity, recognition can also invoke a process of controlled retrieval that results in lucid memory of contextual details from the encoding event (e.g., Tulving & Thomson, 1973; see also Jacoby et al., 2005; Mandler, 1980). Stated differently, there is evidence that recognition cannot be fully described as involving only a process of evaluation, because encoding details or source information may also be retrieved or generated from memory (Gardiner, Ramponi, & Richardson-Klavehn, 1998; Gardiner, Richardson-Klavehn, & Ramponi, 1998; Johnson et al., 1993; Vilberg & Rugg, 2007). The above findings have led to the development of dual-process models of recognition (e.g., Jacoby, 1991; Jacoby & Dallas, 1981; Mandler, 1980) which propose a controlled-retrieval or recollection process that works in parallel with a more strength-based process of familiarity evaluation. In fact, hybrid theories such as the dual-process/signal-detection model have been supported by ROC data from recognition tasks in normal (Yonelinas, 1994) and amnesiac populations (Yonelinas, Kroll, Dobbins, Lazzara, & Knight, 1998), as well as by a wealth of neuro-imaging data (Eichenbaum, Yonelinas, & Ranganath, 2007; Elfman, Parks, & Yonelinas, 2008; Vilberg & Rugg, 2007). 9

16 The current project will not settle the ongoing debate between single-process and dual-process theories of recognition, as a variety of research has revealed compelling evidence on both sides (cf. Parks & Yonelinas, 2007; Wixted, 2007). In later sections, I describe the motivation of the use of signal detection theory (SDT) to analyze memory performance. Given the application of SDT, the experiments are most easily viewed in terms of a single, strengthbased recognition process. At the same time, the use of type-2 SDT in Experiment 2 tacitly assumes the involvement of metacognitive processes in task performance, thus, going beyond a simple single-process view (see also Bellezza, 2003). Moreover, in the General Discussion, I consider interpretations of the present data if a controlled retrieval process (e.g., Recollection) was assumed to affect performance, as is proposed by dual-process theories. Theories of Recall As with recognition, a variety of theories have been developed from studies of recall. One of the earliest accounts suggested that two main processes underlie recall performance: generation and recognition. First, potential memory information is covertly generated, and then generated candidates undergo a recognition-like process of evaluation (e.g., Bodner, Masson, Caldwell, 2000). These Generate-Recognize (GR) theories can be traced to Kintsch (1970; see also Anderson & Bower, 1972; Bahrick, 1970), and in simplest form, predict that a recognition test probe will incite better performance than any recall cue. However, as Jacoby & Hollingshead (1990) discuss, Tulving & colleagues (e.g., Tulving & Thomson, 1973) refuted early GR theories based on experimental findings that, in some situations, recall can exceed recognition. Such limitations of early GR theories led to proposals that recall may be accomplished by the Direct Retrieval (DR) of information from memory (e.g., Guynn & McDaniel, 1999; Jacoby, 1998). Theories of DR posit that the cues available for recall can constrain processing such that 10

17 only relevant information is retrieved, irrespective of whether retrieval is controlled or automatic. Admittedly, the basic theories of GR and DR are just starting points for exploring recall performance, and both have evolved into many forms. Though the models may seem to be in opposition, there is a general consensus that both strategies are used in different situations (Curran & Hintzman, 1995; Jacoby, 1998). Variations of GR theories have been proposed that do not succumb to the refutations by Tulving and colleagues described above. Moreover, recent models propose that DR could be described as a contextually-constrained version of GR (e.g., Higham & Tam, 2005; Jacoby & Hollingshead, 1990). That is, if recall cues are sufficient to specify a particular prior experience (e.g., a study list), those cues could limit the generation process to information from only that specific experience. Such a theory implies that the varied strategies employed across the wide spectrum of recall tasks are not easily divided into two distinct categories. Most relevant to the current research, Toth (2005, Experiment 2) compared GR and DR strategies in cued recall by directly manipulating test instructions, but observed little difference between the two conditions. This result, coupled with the high degree of correspondence across test format between R/F/N judgments for studied items, led Toth to conclude that, at least for the stimuli and retrieval cues he employed, recall performance was most consistent with a GR theory (see also Jacoby & Hollingshead, 1990). The present experiments will also bear on the issue of how best to characterize cued recall performance. Metacognitive Processes in Recognition and Recall Metacognition refers to thoughts, feelings, and beliefs about one s own cognitive processing; metamemory describes metacognitive phenomena related to remembering. Metamemory is typically investigated by collecting some type of performance judgment during 11

18 the study phase (e.g., judgments of learning) or test procedure (e.g., feelings of knowing, ratings of confidence-in-accuracy) (Hunt & Ellis, 2004). To paraphrase Dunlosky & Bjork (2008), any attempt to investigate memory or metamemory alone will likely fall short, as they are intricately linked. Moreover, the complex interplay of memory and metamemory is likely related to the ideas of consciousness, and particularly subjective awareness. Tulving (1985) reminded memory researchers that conscious remembering is an intrinsically subjective phenomenon, and encouraged the development of methods for exploring the subjective awareness of memory and other cognitive phenomena. This general view permeates contemporary theories of memory and metamemory, as evidenced by Koriat, Goldsmith, and Pansky s (2000) description of the human participant as an active agent that has at his/her disposal an arsenal of cognitive strategies and devices that can be flexibly applied in order to reach certain goals. The choice of such strategies as well as their online regulation is based on the subjective monitoring of these processes. As theorists have begun to explore how subjective awareness affects task performance, a great deal of effort has been directed at developing methods to better assess the separate roles of memory and metamemory processes in overall performance (e.g., Higham & Tam, 2005; Koriat & Goldsmith, 1994, 1996). Koriat and colleagues have designed a number of studies to elucidate mnemonic processes at the metamemory level. Noting that remembering in the real-world is often more concerned with accuracy than with quantity, Koriat & Goldsmith (1994) manipulated response option (i.e., free/forced) and test format (i.e., recognition/recall) to examine monitoring and control of memory accuracy. The authors developed a framework that distinguishes between measures of memory that are quantity-based and accuracy-based, noting that effects shown between recognition and recall are often confounded with response option. That is, forced 12

19 recognition (i.e., a quantity-based, or input-bound measure) is most frequently compared with free recall (i.e., an accuracy-based, or output-bound measure). The authors suggest that many comparisons of recognition and recall have focused on memory quantity, while overlooking memory accuracy. Subsequent research has shown that forcing output in recall tasks reduces memory accuracy without affecting memory quantity (Koriat & Goldsmith, 1996). Moreover, the increased accuracy in free recall is a function of a reduction in errors of commission, compared to forced report (Koriat & Goldsmith, 1996; Higham & Tam, 2005). In other words, when given an opportunity to withhold responses in recall tasks, participants use metacognitive processes to monitor generated items and control output of those items in an adaptive manner. While the current research differs considerably from studies comparing free and forced report procedures, the research described above does inform the current investigation of recognition and cued recall. In particular, the current research explored a reported increase in false alarms, or errors of commission, in cued recall. The current paradigm did not include a free-report phase, but Experiment 2 did explore metacognitive processes in recognition and cued recall using a paradigm that I describe below. So despite differences between the paradigms which preclude direct comparisons between the current results and studies comparing free- and forced-report procedures work by Koriat & colleagues (Koriat & Goldsmith, 1994, 1996; see also Higham & Tam, 2005) provides the foundation for basic predictions in the current Experiment 2. Higham and colleagues (2009) have examined metamemory in recognition, using a variant of signal detection theory (SDT). While type-1 SDT is commonly used to analyze recognition performance, Higham et al. suggest that type-2 SDT is well suited to examine the relation between memory accuracy and confidence. This relationship is necessarily 13

20 metacognitive, linking memory performance with processes that could allow for the monitoring and control of responding. I describe type-2 SDT in the overview to Experiment 2; for now, note that the current investigations are related to both of these methods for exploring metamemory. Specifically, the current Experiment 1 compares memory performance within subjects on tests of recognition and cued recall with a measure of memory accuracy analogous to that used by Koriat and Goldsmith (1996). Moreover, the current method uses confidence ratings, which allows for the application of Higham et al. s method of exploring metamemory with type-2 SDT (see Experiment 2). I describe the benefits of the current research more fully in the next section. Directly Comparing Recognition and Recall Given the extensive literatures on recall and recognition, it is surprising that there is not more research directly comparing these two forms of retrieval. A series of "successive testing" studies by Tulving and others - primarily directed at the phenomenon of recognition failure of recallable words - probably constitutes the most concerted effort to understand relations between recognition and recall. This research is broadly summarized by the empirically-based Tulving- Wiseman function (Tulving & Wiseman, 1975; see also Flexser & Tulving, 1978; Wiseman & Tulving, 1976) which predicts a moderate but consistent association between recognition and recall. However, it is important to note that the Tulving-Wiseman function does not directly relate performance on the tests, but rather, shows the predictive relation between recognition and the proportion of recallable words that will also be recognized. That is, the Tulving-Wiseman function was primarily designed to describe the phenomenon of recognition failure of recallable words. Moreover, that paradigm, in which the same items are used in successive tests of recognition and recall, has been strongly criticized on statistical grounds (e.g., Simpson s paradox, see Hintzman, 1980, 1992, 1993); it also has the drawback that responses to a memory 14

21 probe in one test format (e.g., recall) may influence responses to that same item in a subsequent test format (e.g., recognition). Aside from the recognition failure studies by Tulving and colleagues, the majority of studies directly comparing performance across test format find that recognition is typically better than recall (Aggleton & Shaw, 1996). Studies have also revealed dissociations between recognition and recall performance, for example, as a function of word frequency (see Gregg, 1976; Reder, Anderson, & Bjork, 1974). A number of studies of neurological patients have shown that amnesiacs recall performance is often impaired to a much greater degree than is their recognition performance (e.g., Haist, et al., 1992; Warrington & Weiskrantz, 1968), but again, these results may have been influenced by the quantity/accuracy distinction described above (see Koriat & Goldsmith, 1994, 1996). Craik (1979) has used these patterns to suggest that, while recognition performance is typically better, the critical difference between recognition and recall tasks is the amount of effortful, self-initiated processing required. As described above, the present study is an extension of Toth s (2005) direct comparisons of recognition and cued recall, which used a remember/know (R/K) procedure (Tulving, 1985). Tulving s first use of the R/K procedure compared four memory tasks recognition, free recall, and two forms of cued recall and showed that states of subjective awareness vary across tasks. Hamilton & Rajaram (2003) replicated his work, using a between-groups design to obviate the criticisms of successive testing, and specifically examined recollective experience across tests. In four experiments, the authors observed no difference between recognition and cued recall for the proportion of retrieved items that were also given remember responses (cf. Tables 1 and 2). Note that, Hamilton and Rajaram compared the dependent measure Remember/total Retrieved, which is analogous to the transformation of cued recall data presented by Toth. 15

22 The current paradigm alleviates a number of concerns that have plagued previous comparisons of recognition and recall. Many prior studies employed a successive-testing procedure, in which participants take recognition and recall tests for the same material in succession. By randomizing recognition and recall trials within a single test list, the current mixed-test paradigm avoids confounds that limit successive-testing procedures, including effects of test order, response strategy changes between tests, and memory interference from having already been tested on an item (in the other format). Additionally, because all stimuli are counterbalanced across test format and study condition, the paradigm allows for within-subject comparisons of recognition and recall performance. The final advantage of the current paradigm is the simplicity with which recognition and recall are scaled for comparison. The current research uses a confidence rating method to explore effects of memory that were previously examined with a R/K methodology. Yonelinas (2001) empirically compared the R/K and confidence rating methods (as well as the process-dissociation procedure, Jacoby, 1991), and found strong evidence that measures derived from each methodology converged upon similar results about underlying mnemonic processes. His study suggests that experimental results will generalize across test methodologies, and thusly motivated the current research. Nevertheless, the current paradigm does not come without limitations. Most notably, there are items tested in recognition that are not tested in recall (i.e., not generated to a cue), thus raising questions about the assumptions underlying the application of SDT. One of the goals of the present research is to address this issue by directly comparing SDT parameters derived from recall to those derived from recognition, in which all items are tested. A major goal of this comparison is to better understand the elevated false memory rate observed by Toth (2005) and McCullough & Toth (2008). As described in the next section, the current project will inform 16

23 false memory research, as the presently-explored effect may reflect the operation of a novel mechanism underlying false memory (namely, self-generation). False Memory in Recognition and Recall False memory has become a central issue in memory research, both for applied reasons and because false memory effects are thought to reveal fundamental characteristics of the processes underlying normal, accurate memory performance (for a review, see Schacter et al., 1998). There are a handful of mechanisms thought to underlie the occurrence of false memory (Schacter, 1999), including spreading activation (as shown in the Deese-Roediger-McDermott paradigm; Roediger & McDermott, 1995), fluency misattributions (as shown in the false fame effect; Jacoby, Woloshyn, & Kelley, 1989), and source confusion (as shown in the misinformation effect; Loftus, 1975). The revelation effect, an increase in false recognition of revealed items (Watkins & Peynirciouglu, 1990), has recently been used to constrain theories of memory (e.g., Verde & Rotello, 2004). In this line of research, performance on standard recognition trials is compared to trials in which test words are revealed from some degraded form (e.g., presenting test words letter-by-letter, or as an anagram along with a solution code). Using a variety of revelation tasks, many researchers have shown increases in false memory compared to standard recognition tasks, but failed to elucidate the underlying nature of this effect (see Verde & Rotello, 2003). Verde & Rotello (2004) divided two decades of revelation studies into two categories, astutely noting a critical difference. In some studies the revealed item was different than the actual recognition test item (cf. Westerman & Greene, 1996), while in other studies the revelation manipulation was applied to the test items themselves (cf. Peynirciouglu & Tekcan, 1993). The authors thus explored the effects of unrelated-item and same-item revelation tasks, 17

24 using Receiver Operating Characteristic (ROC) curves to compare each condition to standard recognition. 4 Verde & Rotello (2004) found that, compared to standard recognition, the unrelated-item revelation condition produced an increase in old responses to both studied and unstudied items. In contrast, same-item revelation was found to increase old responses only for unstudied items. Under a signal detection framework, these results suggest that revealing an unrelated item prior to presenting a recognition (test) probe causes a liberal change in response bias, but that revealing the test item itself (prior to recognition judgment) causes a true decrement to memory sensitivity (Verde & Rotello, 2004). In the following section, I describe these theoretical explanations in relation to the current research. Importantly, the authors used confidence-based ROCs to elucidate memory phenomena that could not be understood via simpler applications of SDT. Perhaps most important, the effect of the same-item revelation condition is similar to the effect shown by Toth (2005), as both effects are shown only with unstudied items, and neither effect is well described by any of the mechanisms of false memory noted above. Thus, the methods and analyses presented by Verde & Rotello (2004) guided the current project for two specific reasons: (a) the afore-mentioned similarities in methods and findings, and (b) the opportunity to explore the effects of self-generation using confidence based-roc curves. The application of confidence-based ROC curves (i.e., type-1 SDT) is common in memory research, and is described in the following section. 4 The authors had previously shown that more common measures of performance (e.g., d ) are unable to account for the revelation effect, and argued that ROC-based d a is a better measure of memory sensitivity (Verde & Rotello, 2003). 18

25 OVERVIEW OF EXPERIMENTS The goals of the current research are to replicate and further examine the effect of false familiarity due to self-generation. While the increase in false memory in cued recall has only been shown with the remember/know method, the confidence rating method may confer measurement advantages (see Donaldson & Good, 1996), and has probably shaped theories of memory more so than any other experimental methodology (Parks & Yonelinas, 2007). Moreover, Yonelinas (2001) has shown that the remember/know method captures similar mnemonic phenomena as the confidence rating method, and the latter has recently helped illuminate a similar effect of false memory (Verde & Rotello, 2004). Thus, the current research employed a confidence rating method and two analytic approaches based in signal detection theory (SDT) to investigate the relation between recognition and cued recall, and the increase in false memory shown in cued recall (Toth, 2005). Experiment 1 employed analyses based in type- 1 SDT to compare recognition and cued recall, collecting confidence ratings rather than R/F/N judgments, as done previously by Toth. By subjecting the effect of false familiarity due to selfgeneration to rigorous signal detection analyses, the present research seeks to bridge the gap between this effect and other mechanisms of false memory. As discussed above, metamemory parameters have permeated theories of memory performance. Thus, Experiment 2 employed analyses based in type-2 SDT to investigate metamemory processes in the current paradigm. Prior to each experiment, I describe each method and relevant predictions in detail. EXPERIMENT 1 The goals of Experiment 1 are twofold: (a) to replicate the two patterns observed by Toth (2005) and McCullough & Toth (2008), that is, comparable performance on studied items and 19

26 increased false alarms in cued recall, and (b) to further elucidate the nature of the false memory effect by employing type-1 signal detection theory to compare ROC curves. The first important question this experiment will answer is whether the increase in false memory will extend to confidence ratings. The second theoretical issue concerns the relation between present and past investigations of the false memory effect (i.e., exactly how will the cued recall-based increase in false alarms manifest in confidence ratings?). A third issue addressed by this experiment is how the present effect relates to other false memory phenomena, such as the revelation effect. Before presenting specific predictions, I first describe the application of signal detection theory to memory research and briefly explore theoretical interpretations of memory phenomena. I then present the methods and results of Experiment 1. Memory and Detection Theory Signal Detection Theory (SDT) has proven a useful statistical framework for collecting and analyzing recognition data. Many factors led quite naturally to the use of SDT in characterizing recognition memory; most notably, (a) the recognition test situation is analogous to a signal detection task, with the signal being oldness or familiarity ; and (b) recognition performance is well capture by the 2x2 combination of test-probe status (studied, unstudied) and the participant s response ( old, new ). These combinations yield the standard SDT measures of hits, misses, false alarms, and correct rejections (see Figure 1a). Theoretically, as shown in Figure 1b, two distributions of items (studied, unstudied) lie on an axis of increasing evidence, trace strength, or familiarity. The overlapping distributions are presumably separated by the effects of the study experience. Participants are thought to place a response criterion (C) along the strength axis, dividing the distributions into four areas that correspond to the dependent measures. That is, the proportions of responses ( old, new ) to studied and unstudied items 20

27 correspond to the four areas under the theoretical distributions. Useful measures of sensitivity (e.g., d ) and response bias (e.g., B D ), whose formulas are derived from the underlying theory, can then be calculated (see Macmillan & Creelman, 2005). Sensitivity (i.e., the ability to discriminate studied and unstudied items) is indexed by the distance between the means of the distributions, while response bias (i.e., the tendency to respond old ) is commonly measured relative to the point where the curves intersect. Recall data is generally not analyzed with SDT, due to the fact that, in most recall paradigms, misses and correct rejections are not reported (i.e. participants withhold them). As such, proportional hit and false alarm rates cannot be computed (Green & Swets, 1966). Critical to the present research, proportional hit and false alarm rates can be calculated, specifically, because participants provide misses and correct rejections (i.e., new responses to relevant items generated in cued recall). In recent literature, SDT has proven particularly useful in exploring a false memory phenomenon. Verde and Rotello (2004) used a confidence rating procedure to explore the nature of the revelation effect. Figure 2b depicts Verde and Rotello s theoretical interpretation of the same-item revelation condition. As compared to standard recognition (Figure 2a) in which the study experience has separated the distributions by two (arbitrarily drawn) units in the revelation condition, the act of revealing the test item is thought to shift the distribution of unstudied items to the right along the axis of evidence. Thus, sensitivity (as indexed by the distance between the means of the distributions) is reduced, and response bias (as measured relative to the intersection of the two curves) can become more liberal. In comparing Figures 2a and 2b, note that the area representing false alarms differs, while the area for hits remains constant. Thus, a shift in the unstudied-item distribution could also explain the findings of increased false alarms in cued recall, compared to recognition (Toth, 2005). 21

28 Figure 2c presents a theoretical depiction of an alternative data pattern, for comparison to the standard recognition depiction (Figure 2a) and the most plausible interpretation of the effects of self-generation (Figure 2b). Figure 2c represents a fluency-based hypothesis, which suggests that the process of generating items increases the processing fluency of those items. Note in this depiction how an increase in fluency would shift both distributions and affect the hit and false alarm rates to similar degrees, thus manifesting as a change in response bias. Figure 2c also depicts a criterion-shift hypothesis, which suggests that the act of generating test items in cued recall causes participants to adopt a more conservative response criterion. Much like the fluency hypothesis, a simple criterion shift would affect the hit and false alarm rates similarly, and thus a simple effect increase of processing fluency or change in response bias does not fit the observed effects of self-generation. Keep in mind, these are only basic interpretations. For example, selfgeneration might increase the familiarity of studied and unstudied items to different degrees, or it could affect response criterion placement in addition to moving one or both of the distributions. While signal detection theory describes recognition performance well, the application of SDT to memory can be intricate, particularly in regard to the assumptions underlying various parameters. Though the debate is beyond the scope of this thesis, many theorists support empirical methods based in SDT, but caution against interpretations based solely on SDT parameters, particularly those that don t reflect performance across a range of response criteria (e.g., Donaldson & Good, 1996; Verde & Rotello, 2004; Yonelinas, 1994, 2001; Yonelinas & Parks, 2007). A confidence-based receiver operating characteristic (ROC) curve is a plot of proportions of responses to studied items against proportions of responses to unstudied items across a range of response confidence. ROCs are typically created by having participants make memory decisions on a scale of confidence (e.g., 1 = sure new to 6 = sure studied) and plotting 22

29 cumulative response proportions for studied vs. unstudied items at each level of confidence. That is, the proportion of 6 responses to studied items is plotted against the proportion of 6 responses to unstudied items. Next, the cumulative proportion of 5 and 6 responses to studied items is plotted against the same cumulative proportion for unstudied items, and so on through the proportions for confidence level 1. 5 Because the resultant curve reflects memory performance across a range of subjective confidence, measures derived from ROCs have been supported over simpler measures (see Verde & Rotello, 2003). However, despite the utility of ROC analyses, ROC-derived parameters also require theoretical assumptions, and so do not necessarily obviate the criticisms of applying detection theory to memory research. Nonetheless, a major goal of this research project was to investigate the false memory effect via SDT. Because debate continues over which is most appropriate, I calculated a number of measures of sensitivity. Current Predictions In previous research, Toth (2005) observed increased false memory in cued recall, relative to recognition. The current Experiment 1 seeks to replicate the effect in a confidence rating procedure, which allows for a number of predictions at varying levels of analysis. Toward the primary goal of replication, the current empirical design tests two predictions. The first prediction, Hypothesis 1a, is that mean confidence for studied items will not differ significantly across test formats, whereas mean confidence for unstudied items will be significantly higher in cued recall relative to recognition. In prior studies, the effect of false memory was revealed by comparing proportions of responses that are analogous to the SDT-based measures of hit rate (HR) and false alarm rate (FAR). Thus, for Hypothesis 1b, I predict that proportions of high 5 Note that the cumulative proportion of confidence judgments at the lowest level of confidence includes all trials, assuming participants must provide a confidence judgment for every test trial. 23

30 confidence responses to studied items (i.e., simple HR) will be equal across test formats, but that proportions of high confidence responses to unstudied items (i.e., simple FAR) will be significantly increased in cued recall. 6 The two predictions above are proposed to most directly replicate the results of Toth (2005); however, a secondary goal of this project was to vigorously explore the false memory effect using SDT. While Hypotheses 1a and 1b will satisfy the goal of replication, the former is not a signal detection analysis, and the latter is based upon measures that have been criticized in studies of memory (e.g., Yonelinas, 2001). Drawing on the signal detection interpretations described above, increased confidence selective to unstudied items (in cued recall) is indicative of a decrease in memory sensitivity (see Figure 2a). Thus, for Hypothesis 2, I predict that sensitivity will be significantly reduced in cued recall, compared to recognition. There are a number of ways to test this prediction, which I discuss below. In addition to a change in sensitivity, Toth observed a change in response bias in five of eight conditions, such that responding was significantly more liberal in cued recall relative to recognition. The effect was highly variable in size, and, in some cases, was found despite observing a significant effect for only unstudied items. Thus, for Hypothesis 3, I predict that responding will be liberal in cued recall, compared to recognition. As the primary effect of self-generation is not thought to be on response bias, I predict only a trend toward more liberal responding in cued recall, as measured by B D (Donaldson, 1992). In prior studies, Toth (2005) observed a significant effect of test format on sensitivity in seven of eight conditions. However, because those studies employed a remember/know methodology, only the simplest measures of sensitivity could be computed. However, the SDT- 6 To compute simple hit and false alarm rates from confidence ratings, a common method was used in which the response scale (1-6) is bifurcated to represent responses of new (1-3) and old (4-6). 24