False Memories in the DRM Paradigm

False Memories in the DRM Paradigm Age-Related Differences in Lure Activation and Source Monitoring Katrina Sugrue,1 Deryn Strange,2 and Harlene Hayne1 1

University of Otago, Dunedin, New Zealand John Jay College of Criminal Justice, City University of New York, NY

2

Abstract. Prior research using the Deese-Roediger-McDermott paradigm has shown that participants are more likely to report the critical lures when long lists are presented. In this experiment, we evaluated two potential explanations for this list-length effect. Ten-year-old children and adults studied 7- or 14-word lists. After recalling each list, participants were then asked to report any other words that they had thought about, but had not reported, during the recall phase. We found that long lists were more likely to activate the critical lure and that short lists did not facilitate source monitoring. On the basis of our findings, we conclude that, for both age groups, the list-length effect was due primarily to list-related differences in activation of the critical lure. Keywords: DRM, activation, source monitoring, developmental

Roediger and McDermott (1995) resurrected a procedure originally employed by Deese (1959) who showed that adults who study a list of words (e.g., sour, candy, sugar, bitter, and good) often falsely report a related but unpresented word, the critical lure (i.e., sweet). Now known as the DRM (Deese-Roediger-McDermott) paradigm, there has been tremendous interest in the factors that influence the magnitude of the DRM illusion in both children and adults. For example, in our earlier research, we found that the length of the studied list matters (Sugrue & Hayne, 2006). Specifically, we found that both children and adults were more likely to report the critical lure if they were presented 14-word (long) rather than 7-word (short) lists. This list-length effect has been reported in a number of different laboratory procedures (see e.g., Gallo & Roediger, 2003; Hutchinson & Balota, 2005; Libby & Neisser, 2001; Marsh & Bower, 2004; Robinson & Roediger, 1997; Watson, Balota, & Roediger, 2003). In this experiment, we draw on Roediger, McDermott, and colleagues’ activation-monitoring theory (AMT) of the DRM illusion (e.g., McDermott & Watson, 2001; Meade, Watson, Balota, & Roediger, 2007; Roediger, Balota, & Watson, 2001; Roediger & McDermott, 2000; Roediger, Watson, McDermott, & Gallo, 2001), to help us understand the mechanism responsible for the list-length effect in children and adults. The AMT proposed by Roediger, McDermott, and others integrates the aspects of Underwood’s (1965) implicit associative response theory and spreading activation (Collins & Loftus, 1975), with ideas adapted from Johnson and colleagues source-monitoring framework (Johnson, Hashtroudi, & Lindsay, 1993; Johnson & Raye, 1981). In Experimental Psychology 2009; Vol. 56(5):354–360 DOI: 10.1027/1618-3169.56.5.354

short, Roediger, Balota, et al., 2001 proposed that it is the activation and monitoring processes that occur during encoding and retrieval that result in the DRM illusion. According to their theory, hearing a list of related words (e.g., sour, candy, sugar, bitter, and good) activates the unpresented critical lure (i.e., sweet) in semantic memory. When activation of the critical lure is sufficiently strong, participants experience the critical lure in much the same way as they experience studied items. This process is thought to be automatic and therefore not amenable to conscious control. As a result, if participants do not engage in a more deliberative monitoring process, they may fail to identify the source of the information, wrongly claiming that the critical lure appeared on the presented list. In other words, they will commit a source-monitoring error (Johnson & Raye, 1981; Johnson et al., 1993). Drawing on AMT then, there are two possible explanations for why people tend to make more errors when they are presented with longer DRM lists. First, long lists may be more likely to activate the critical lure, and consequently, produce a stronger false memory effect. Indeed, in a straightforward manipulation of the list length, Robinson and Roediger (1997) demonstrated that it is the total associative strength of a list that best predicts whether the critical lure will be recalled; the longer the list, the greater the total associative strength of that list (see also, Gallo, 2006; Roediger, Watson, et al., 2001). Second, people may also make more errors with longer DRM lists because short lists allow participants to make better use of their source-monitoring abilities. For example, relative to longer lists, there is a reduced cognitive load ! 2009 Hogrefe & Huber Publishers

Sugrue et al.: Age-Related Differences in the DRM

associated with keeping the items on a short list in memory. In addition, research has shown that source-monitoring errors are most likely to occur when there are no defining features upon which to reject the lure words (e.g., Israel & Schacter, 1997; Smith & Hunt, 1998). From this perspective, shorter lists allow for greater distinctiveness of the list items which might help people to recall that although they thought of the critical lure during the presentation of the list, it was not actually included in the original list (Johnson et al., 1993; Roediger, Watson, et al., 2001). In this experiment, we attempted to tease apart these two potential explanations for the effect of list length on rates of false recall and to determine whether the explanation would be the same or different in children and adults. From a developmental perspective, there is reason to expect that the explanation for the list-length effect will be the same for children and adults. For example, research by Howe and his colleagues suggests that like adults, children’s false memories are the result of associative activation of the critical lure. Indeed, according to Howe, the age-related increase in the rate of false recall that is typically observed in the DRM is explained by the increase in the automaticity with which children experience activation of the critical lure as they get older (see Howe, 2005, 2006; Howe, Gagnon, & Thouas, 2008). On the other hand, however, there are also reasons to expect that children and adults may be differentially affected by the length of the DRM lists. First, Ghetti, Qin, and Goodman (2002) have suggested that rather than increasing activation, long lists may actually weaken activation of the critical lure in children, either because they contain more unfamiliar terms or because the task becomes more cognitively complex. Moreover, we know that children, in particular, have difficulty distinguishing between internal activation (thinking about the critical target) and external activation (hearing the critical target being spoken by the experimenter), and as such, the reduced cognitive load of short lists may not be enough to improve their source-monitoring ability (Foley & Johnson, 1985; Foley, Johnson, & Raye, 1983; Foley & Ratner, 1998; Lindsay, Johnson, & Kwon, 1991). To examine the source of the list-length effect in children and adults, we added a post-recall phase to the standard DRM methodology. In the first phase of the experiment we used the standard DRM procedure: children and adults were asked to study and later recall words from eight standard DRM lists. In the second phase, participants were asked to report any other words that they thought about during presentation or recall of the study lists but did not report. The addition of a post-recall phase provides a unique opportunity to determine whether failure to activate the critical lure or successful source monitoring provides a better explanation for why the false memory illusion sometimes fails to occur. For example, if the study list does not activate the critical lure, then participants will fail to report that critical lure during both the initial recall test and the post-recall phase. On the other hand, if the study list does cue retrieval of the critical lure, but the participant remembers that the lure was not actually spoken by the experimenter, then they ! 2009 Hogrefe & Huber Publishers

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should report the critical lure when they are given the opportunity to do so during the post-recall test.

Method Participants Fifty-four 10-year-old children (M = 10.41 years; SD = 0.58 years; 21 males) and 54 adults (M = 21.01 years; SD = 0.43 years; 14 males) participated in the study. The children were recruited from two local primary schools in Dunedin, New Zealand. All children had parental consent to participate and they all received a movie voucher for participating. The adults could use their participation to satisfy a small portion of an internal assessment requirement for an Introductory ssychology course.

Design The experiment was a 2 (age: adults and children) · 2 (list length: 7- and 14-word) · 2 (test phase: recall and postrecall) mixed-factor design. Half the adult and child participants were randomly assigned to the 7-word condition, while the remainder were assigned to the 14-word condition. Test phase varied within-subjects.

Materials The eight lists that elicited the highest levels of false recall in Stadler, Roediger, and McDermott’s (1999) norming study (i.e., the chair, doctor, rough, sleep, smell, smoke, sweet, and window lists) were used in this experiment. The lists contained either 7 or 14 words and were presented in the order of strongest to weakest associative strength. Participants were randomly assigned to list-length condition.

Procedure Participants were first given a practice trial (with four unrelated words) to orientate them to the task. Next, participants heard the eight lists read aloud at a rate of one word every 1.5 s. Immediately following the presentation of each list, participants were given 1 min to recall the words (aloud) before the next list was read, what we refer to as the recall phase. The presentation of the lists was randomized for each participant and the sessions were audiotaped so that the words that the participants recalled could be transcribed. At the conclusion of the recall phase, participants were asked: ‘‘While I was reading out that list, or while you were recalling the words back to me, were there any other words that you thought of, but didn’t say, because you didn’t think they were on the list?’’ We refer to this phase of testing as the post-recall phase. Note that the instructions that we gave Experimental Psychology 2009; Vol. 56(5):354–360

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Table 1. The mean number of semantically-related intrusions and other intrusions for each age group, as a function of list length. Standard deviations in parentheses Children Semantically-related intrusions Other intrusions

Adults

Short lists

Long lists

Short lists

Long lists

1.44 (1.76) 0.88 (1.12)

1.65 (1.72) 1.96 (1.99)

0.59 (0.64) 0.37 (0.74)

1.26 (1.40) 0.59 (0.75)

our participants were based on the procedure originally developed by Bredart (2000), however, the timing and the exact wording of the question were modified for this experiment. In Bredart’s study, the post-recall phase occurred after all the lists had been recalled and the adult participants had provided confidence ratings for each of the words produced during the test. Because of the age differences in the number of words typically correctly recalled by adults and children (see e.g., Ghetti et al., 2002; Sugrue & Hayne, 2006), and because of the difficulty associated with trying to remember what one had thought about when the lists were originally presented, we included the post-recall phase immediately after each list was recalled.

main effect of age, F(1, 103) = 127.35, p < .01, gp2 = .55. Adults (M = 0.73 and SD = 0.14) recalled a greater proportion of correct words than children (M = 0.54 and SD = 0.16). There was also a significant main effect of list length, F(1, 103) = 221.31, p < .01, gp2 = .68. Participants in the short-list condition (M = 0.76 and SD = 0.12) correctly recalled a greater proportion of studied items than participants in the long-list condition (M = 0.51 and SD = 0.14). There was no Age · List Length interaction (F < 1).1

False Recall Recall Phase

Results We first calculated the proportion of correctly recalled items and categorized any intrusion errors as (a) the critical target; (b) semantically-related (e.g., patient, ambulance, or dentist for the doctor list); or (c) others (e.g., earlier list or unrelated intrusions; see Table 1). Next, when participants did not produce the critical lure during the recall phase, we determined whether they reported the critical lure during the post-recall phase. Consistent with the prior research on the DRM paradigm, we employed a strict coding system, whereby responses such as smoking, asleep, and sweets were not counted as instances of the critical lures, smoke, sleep, and sweet.

Correct Recall Table 2 displays the proportion of studied items correctly recalled (s1) and the absolute number of words recalled (line 2) by each age group as a function of list length. Using the proportion data, we conducted a 2 (age: children and adults) · 2 (list length: short lists and long lists) ANOVA (analysis of variance). This analysis revealed a significant 1

2

Figure 1 illustrates the proportion of critical lures recalled for each age group, as a function of list length, during the recall phase. Using these data, we conducted a 2 (age: children and adults) · 2 (list length: short lists and long lists) ANOVA. This analysis revealed a main effect of list length, F(1, 103) = 24.60, p < .01, gp2 = .19. Irrespective of the age, participants produced more critical lures in the long lists (M = 0.46 and SD = 0.22) than in the short lists (M = 0.25 and SD = 0.20). There were no other significant effects (age: F(1, 103) = 24.60, p < .01; Age · Length: F < 1).2 Post-Recall Phase Next, we were interested in the proportion of critical lures that were not recalled in the recall phase but were subsequently recalled in the post-recall phase, giving us an estimate of the participants’ accurate source monitoring. Thus, for each participant, we subtracted the number of lures participants recalled in the recall phase from eight (the number of DRM lists and thus the number of critical lures), leaving us with the number of lures participants had the opportunity to recall in the post-recall phase. From there, we calculated the proportion of critical lures that the participants actually produced in the post-recall phase. These data are displayed

This same pattern of results was also obtained when we analyzed the absolute number of studied items recalled. Overall, adults recalled more total words than did children, F(1, 103) = 127.35, p < .01, gp2 = .55; and participants of both ages recalled more words from the short lists relative to the long lists, F(1, 103) = 221.31, p < .01, gp2 = .68. Again, there was no Age · List Length interaction (F < 1). Note that relaxing the inclusion criteria for what constitutes a critical lure – including ‘‘asleep’’ as a derivation of ‘‘sleep’’ and ‘‘lollies’’ for ‘‘sweet’’ – does affect the results. A 2 (age: children and adults) · 2 (list length: short lists and long lists) ANOVA using this more lenient data reveals the same main effect of list length, F(1, 103) = 67.77, p < .01, gp2 = .32. In addition, however, there is a main effect of age, F(1, 103) = 4.13, p = .04. Collapsing across length, children reported more critical lures than adults (children: M = 0.41 and SD = 0.23; adults: M = 0.33 and SD = 0.23).

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Table 2. The proportion of correct recall of studied items (line 1) and the absolute number of words recalled (line 2) for each age group, as a function of list length. Standard deviations in parentheses Children Proportion correct Absolute number of words

Adults

Short lists

Long lists

Short lists

Long lists

0.67 (0.08) 4.72 (0.55)

0.41 (0.10) 5.74 (1.34)

0.84 (0.08) 5.90 (0.57)

0.61 (0.09) 8.60 (1.21)

Figure 1. Proportion of critical lures reported in the recall phase as a function of age and list length. Error bars represent the standard errors.

Figure 2. Proportion of critical lures reported in the postrecall phase as a function of age and list length. Error bars represent the standard errors.

in Figure 2 as a function of age group and list length. Using these data, we conducted a 2 (age: children and adults) · 2 (list length: short lists and long lists) ANOVA, which revealed no significant effects (age: F(1, 103) = 3.31, p = .07; list length: F < 1; Age · List Length: F < 1).3

children and adults correctly recalled a greater proportion of words from the short lists relative to the long lists. Moreover, we replicated the earlier research and found that participants who studied the long lists were more susceptible to false memories than were participants who studied short lists (see e.g., Gallo & Roediger, 2003; Hutchinson & Balota, 2005; Libby & Neisser, 2001; Marsh & Bower, 2004; Robinson & Roediger, 1997; Watson et al., 2003). Our primary goal in this experiment, however, was to assess the relative contributions of activation and source monitoring – the two components of the AMT – in the list-length effect and to determine whether the mechanism was the same for children and adults (e.g., McDermott & Watson, 2001; Meade et al., 2007; Roediger, Balota, et al., 2001; Roediger & McDermott, 2000; Roediger, Watson, et al. 2001). To recap our findings relevant to that goal, participants who were tested with longer lists were more likely than participants who were tested with shorter lists to report the critical lure during the recall phase of the test. In addition, in the postrecall phase of the test, participants who were tested with short lists were no more likely than participants who were tested with long lists to report the critical lure. Therefore, on the basis of our findings, we conclude that the list-length effect is due primarily to list-related differences in activation of the critical lure, not to list-related differences in source monitoring. Moreover, at least by age 10 (the age of our

Total Activation Finally, we calculated a total activation score for each participant, summing the proportion of lures they recalled in both the recall and post-recall phases. Using this data, we conducted a 2 (age: children and adults) · 2 (list length: short lists and long lists) ANOVA, which revealed a main effect of list length, F(1, 103) = 9.19, p = .01, gp2 = .08. Irrespective of age, participants produced more critical lures in the long lists than in the short lists (short lists: M = 0.43 and SD = 0.31; long lists: M = 0.61 and SD = 0.30). There were no other significant effects (age: F < 1; Age · Length: F(1, 103) = 1.31, p = .26).

Discussion Taken together, we found that adults correctly recalled a greater proportion of words relative to children, and both 3

Once again, relaxing the inclusion criteria for critical lures does affect the results. A 2 (age: children and adults) · 2 (list length: short lists and long lists) ANOVA using the more lenient data removes the age effect, F(1, 103) = 1.67, p = .20. There are no other significant effects (length: F(1, 103) = 3.11, p = .08; Length · Age interaction: F < 1).



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child participants), the mechanism responsible for the listlength effect appears the same; our total activation analysis showed that adults were no more likely than children to experience activation of the critical lure. We are not alone in concluding that it is the activation of the critical lure that is most important in the list-length effect. Although failures in source monitoring play an integral role in the DRM illusion, it seems that longer lists are simply more effective in activating the critical lure than shorter lists. Indeed, there now appears to be a convergence in the literature on the idea that associative strength, and thus activation, is most predictive of adults’ false recall rates in the DRM illusion. (e.g., Gallo, 2006; Hutchinson & Balota, 2005; Kimball, Smith, & Kahana, 2007). Moreover, research by Howe and colleagues makes a compelling case for the idea that associative strength is the key component driving children’s false recall rates as well (Howe, 2005, 2006; Howe et al., 2008). Of course, it is important to note that we did not observe a main effect of age, nor did we see an Age · List Length interaction in false recall. We acknowledge that these results are somewhat inconsistent with the extant literature (see e.g., Sugrue & Hayne, 2006). In our prior research, for example, we found that adults were more likely to report the critical lures than were children; here, on the other hand, we did not find an age-related difference in performance. It is possible that the explanation for this inconsistency lies in the nature of our experimental procedure. In the present experiment, for example, the instructions for the post-recall phase may have acted as an implicit warning against making errors, a warning that is likely to have been more effective for adults than children. Indeed, research shows that in DRM procedures where participants receive information about false memories, source monitoring is improved, reducing false memories (e.g., Gallo, Roberts, & Seamon, 1997; Gallo, Roediger, & McDermott, 2001; McDermott & Roediger, 1998). Of course, further research would need to be undertaken to test this explanation. Moreover, research from our lab suggests that there is a great deal of variability in the rate of false recall in 10-year-old children, which appears to be related to their socioeconomic status (Sugrue, 2005; see also Howe, Cicchetti, Toth, & Cerrito, 2004), an issue that clearly warrants further investigation. The post-recall phase in this experiment was based on a procedure originally described by Bredart (2000). Interestingly, the adults in Bredart’s study were more likely to produce the critical lure in the post-recall phase than were the adults in our study. We believe this difference is explained by the differences in the construction of the study materials between the two studies. Bredart’s study materials consisted of eight lists of 10 words and the critical lure for each word corresponded to a famous person (e.g., Adolf Hitler and Peter Pan). We suspect that having famous people as the lure words facilitated source-monitoring because these items are more distinctive than the lure words typically used on the DRM task (e.g., chair and doctor). Consistent with this explanation, Roediger, Watson, et al. (2001) found a negative correlation between the distinctiveness of the critical lure and false recall. They argued that highly distinctive critical lures facilitate the monitoring process because Experimental Psychology 2009; Vol. 56(5):354–360

individuals can use the distinctive features associated with the lure word to discriminate it from the list items (see also, Gallo et al., 2001; Israel & Schacter, 1997; Kellogg, 2001; Smith & Hunt, 1998). With the Roediger, Watson, et al. (2001) study in mind, it might be possible to increase source-monitoring by children who are tested in the DRM paradigm if we made the lists more distinctive and more child-friendly (see e.g., Seamon, Luo, Schlegel, Greene, & Goldenberg, 2000). The original DRM lists were created using word association responses generated by adults. It seems likely that, for children, words such as physician, stethoscope, lawyer, and office would not be as highly associated with the critical lure doctor. Instead, children might associate other words such as hurt, injection, and help more strongly with this concept. Indeed, Metzger et al. (2008) created a set of child-generated lists based on the free associations of third-graders to the critical lures and found that the words they produced were different to those produced by adults. Moreover, when children were tested using these child-friendly DRM lists, their accurate recall matched that of adults, however, their error rate did not. In other words, they continued to produce fewer critical lures relative to adults. To explain their results, Metzger et al. (2008) drew on Fuzzy Trace theory (Brainerd & Reyna, 2002a, 2007; Reyna & Brainerd, 1995a, 1995b) and Howe’s (2005) elaboration of that theory. Specifically, they claimed that children’s performance on false and accurate memory differed in ways that suggested that the two components were driven by different mechanisms: gist and verbatim memory. That is, the familiarity of the child-generated DRM lists might have allowed children to rely more heavily on their verbatim memory. As a result, they could more readily engage in rehearsal that may have enhanced their performance during testing. Relying on verbatim memory may have also enabled the children to avoid falsely recalling the critical lure by using a process similar to recollection rejection (Brainerd & Reyna, 2002b) or recall-to-reject (Rotello, Macmillan, & Van Tassel, 2000). Put another way, the children might have experienced an improvement in their source-monitoring abilities when they were more familiar with the materials being studied (Foley & Johnson, 1985; Foley & Ratner, 1998; Foley et al., 1983; Lindsay et al., 1991). Unfortunately, on the basis of Metzger et al.’s data, we cannot determine the relative contributions of activation and source monitoring on the performance of children and adults, but future research using the post-recall phase used in this research may help to answer this question. In summary, our results suggest that activation rather than successful source monitoring is a better explanation for the list-length effect that we observed here and in our earlier research (Sugrue & Hayne, 2006). We acknowledge, however, that the role of source-monitoring might be enhanced, particularly in children, if participants are tested with more familiar materials. Combining the more childfriendly lists that Metzgar et al. constructed with a post-recall phase like that used in this study might determine whether source monitoring plays a greater role in preventing false memories when children are more familiar with the studied words. ! 2009 Hogrefe & Huber Publishers


Acknowledgment We are grateful for the support of the New Zealand Government through the Marsden Fund, administered by the Royal Society of New Zealand on behalf of the Marsden Fund Council. During the preparation of this manuscript, Deryn Strange was supported by a Postdoctoral Fellowship from the Foundation for Research Science and Technology.

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Received April 8, 2008 Revision received July 22, 2008 Accepted October 1, 2008 Deryn Strange John Jay College of Criminal Justice Department of Psychology 445 West 59th Street New York, NY, 10019 USA Tel. +1 212 484 1345 E-mail [email protected]
