Memory Specificity, but Not Perceptual Load, Affects Susceptibility to Misleading Information

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Memory specificity, but not perceptual load, affects susceptibility to misleading information

Francesca R. Farina1,2,* & Ciara M. Greene1

1School of Psychology, University College Dublin, Ireland.

2Trinity College Institute of Neuroscience, Trinity College Dublin, Ireland.

Note: This paper has not yet been peer-reviewed. Please contact the authors before citing.

Correspondence may be sent to Francesca Farina, Trinity College Institute of Neuroscience,

Trinity College Dublin, Ireland. Email: [email protected]

1 Abstract

The purpose of this study was to examine the role of perceptual load in eyewitness memory accuracy and susceptibility to misinformation at immediate and delayed recall. Despite its relevance to real-world situations, previous research in this area is limited. A secondary aim was to establish whether trait-based memory specificity can protect against susceptibility to misinformation. Participants (n=264) viewed a 1-minute video depicting a crime and completed a memory questionnaire immediately afterwards and one week later. Memory specificity was measured via an online version of the Autobiographical Memory Test (AMT). We found a strong misinformation effect, but no effect of perceptual load on memory accuracy or suggestibility at either timepoint. Memory specificity was a significant predictor of accuracy for both neutrally phrased and leading questions, though the effect was weaker after a one-week delay. Results suggest that specific autobiographical memory, but not perceptual load, enhances eyewitness memory and protects against misinformation.

Keywords Perceptual load; memory specificity; eyewitness; misinformation.

2 General Audience Summary The misinformation effect is a memory impairment for a past event that occurs when a person is presented with leading information. Leading information can distort the original details of a memory and produce false memories. False memories have serious consequences, particularly in the context of eyewitness testimony. Identifying the conditions under which individuals will be more (or less) vulnerable to misinformation is therefore important. Perceptual load, defined as the perceptual demands imposed by a task, has been suggested as a key factor influencing eyewitness memory. Despite its relevance to real-world situations, previous work in this area is limited. The aim of our study was to examine the role of perceptual load in eyewitness memory accuracy and susceptibility to misinformation at immediate and delayed recall. A secondary aim was to establish whether memory specificity protects against susceptibility to misinformation. Memory specificity refers to the detail with which personally experienced events are remembered. Research has shown that increasing specificity for the event being remembered enhances memory performance and reduces misinformation effects. However, no work has investigated whether a natural ability to recall specific memories also improves memory performance. Participants (n=264) viewed a 1-minute video depicting a crime and completed a memory questionnaire immediately afterwards and one week later. Memory specificity was measured using an online version of the Autobiographical Memory Test. We found a strong effect of leading questions, but no effect of perceptual load on memory accuracy or suggestibility at either timepoint. Memory specificity predicted accuracy for both neutrally phrased and leading questions, although the effect was weaker after a one- week delay. Our results offer preliminary evidence that having a specific memory recall style protects against misinformation. If memory specificity does in fact moderate vulnerability to leading information, it may be possible to reduce misinformation effects through memory specificity training.

3 MEMORY SPECIFICITY, BUT NOT PERCEPTUAL LOAD, AFFECTS SUSCEPTIBILITY TO MISLEADING INFORMATION. The misinformation effect is a memory impairment for a past event that occurs when a person is presented with leading information (Loftus, 2005). Many studies have shown that post-event information can distort the original details of a memory and, in some cases, lead to the creation of an entirely false memory (see Loftus, 2005). False memories have serious, real-world consequences, particularly in the context of eyewitness testimony (Lacy & Stark, 2013). Misremembering details of a crime can significantly impact the outcome of a legal trial and has, in the past, led to wrongful convictions (Garrett, 2011). Thus, identifying the conditions under which an individual will be more (or less) vulnerable to misinformation is highly important. One cognitive factor known to influence susceptibility to leading information is the amount of attentional resources available during memory encoding (Frenda, Nichols & Loftus, 2011). Evidence shows that people are more likely to accept misleading information when their attention is restricted, for example, by performing two tasks at once (Lane, 2006; Otgaar, Peters & Howe, 2012; Wright & Livingston-Raper, 2002). Limiting attention in this way is thought to interfere with encoding processes, resulting in less detailed memories. In such cases, an individual will be more likely to rely on external information when reconstructing an event, which may leave them open to suggestion (Hyman, Wulff & Thomas, 2018). Perceptual load, defined as the perceptual demands imposed by a task (Lavie, 2005), has recently been suggested as a key determinant of attentional processing in eyewitness testimony (Murphy & Greene, 2016). Perceptual load theory was originally proposed by Lavie and Tsal (1994) as an explanation for why attentional selection occurs early in some contexts but not in others. Load theory states that when there is a large amount information to take in (high perceptual load), attentional resources will be used up quickly and only the most salient stimuli will be processed. Conversely, when there is less information to process (low perceptual load), attention is unrestricted, allowing all available stimuli to be perceived. Despite being proposed over 20 years ago, few studies have investigated the role of perceptual load in eyewitness memory and susceptibility to misinformation (Greene, Murphy & Januszewski, 2017; Greene & Schachter, 2018; Murphy & Greene, 2016, Experiment 1). In these studies, participants witnessed a video depicting a robbery in which the level of perceptual load was high or low (operationalised as the

4 number of task-irrelevant items in the scene). They then completed a memory questionnaire containing leading or non-leading questions about the event. In one study (Greene & Schachter, 2018), a subset of participants underwent an episodic specificity interview (ESI) before being exposed to misleading information. The ESI was designed to reduce the misinformation effect by eliciting specific memories about the video. Finally, the questionnaire was administered to all participants again after one week to examine the influence of load on delayed recall. Across all three studies, participants in the high load condition exhibited worse memory performance than those in the low load condition (Greene et al., 2017; Greene & Schachter, 2018; Murphy & Greene, 2016). This effect was particularly strong for peripheral details (e.g. identifying a background “witness” character), consistent with the idea that high load narrows attentional focus (Caparos & Linnell, 2010). Results concerning the effect of perceptual load on susceptibility to misinformation were more varied. Murphy and Greene (2016) reported that high load enhanced participants’ suggestibility at immediate recall but not at one-week follow-up, indicating that the effects of perceptual load may be time-dependent. Conversely, Greene and Schacter (2018) found no effect of perceptual load on sensitivity to leading questions at immediate recall. Load did, however, influence memory at one-week follow-up. Specifically, the high-load group were more likely to misidentify a witness than the low-load group when tested again after one week. This effect was reduced for participants who completed the ESI compared to those who completed a control interview, suggesting that detailed recollection of a memory protects against the misinformation effect when perceptual demands are high. Studies to date have yielded mixed results regarding the effects of perceptual load on eyewitness testimony. The aim of the current study was to clarify whether perceptual load influences memory and susceptibility to misinformation at immediate and delayed recall. We hypothesised that high load would impair memory performance and exacerbate the misinformation effect at both timepoints. A secondary aim was to establish whether trait-based memory specificity protects against susceptibility to misinformation. Memory specificity refers to the detail with which personally experienced events are remembered (Williams et al., 2007). The ability to retrieve specific memories is thought to rely on executive control processes that facilitate the maintenance of information in working memory and inhibit irrelevant information (Takano, Moriya & Raes, 2017). Memory

5 specificity has been investigated extensively in various contexts, particularly in relation to the memory deficits typically observed in mood disorders such as depression (see Williams et al., 2007, for a review). However, no research has been conducted to determine whether memory specificity improves eyewitness memory accuracy and reduces suggestibility. Greene and Schacter (2018) showed that increasing specificity for the event being remembered enhances recall accuracy and reduces misinformation effects. A large body of research on a similar protocol, the Cognitive Interview, has also suggested that structured interviews aimed at increasing memory specificity enhance recall of true events (Fisher & Geiselman, 1992; Memon, 2006), though the effects on false recall are inconsistent across studies (e.g. Gabbert, Hope, Fisher, & Jamieson, 2012; LaPaglia, Wilford, Rivard, Chan, & Fisher, 2014; Memon, Zaragoza, Clifford, & Kidd, 2010). Results suggest that individuals with a specific memory recall style may be more inclined to recall details of the target stimulus, and less inclined to accept misleading information, than those with a general recall style. In the present study, we examined the relationship between memory specificity, accuracy and suggestibility. We hypothesised that participants with higher trait memory specificity would be more accurate, and less easily led, than those with lower specificity at both timepoints. We further hypothesised that the effect of trait memory specificity would be enhanced under high load, when attentional resources are strained.

METHODS Participants Participants were 264 adults (194 female; mean age = 34.39, SD = 11.81, range = 18-70) recruited via the Prolific online crowdsourcing platform (https://prolific.ac/). Exclusion criteria were uncorrected vision impairment or history of memory deficits/problems. Participants provided informed consent in accordance with the Declaration of Helsinki and received a small monetary payment (£1.75-£2.60). The study was approved by the University College Dublin (UCD) Human Research Ethics Committee.

Design A mixed-factorial design was used with perceptual load (high, low) and question type (non- leading, leading) as between-groups factors and time (immediate, delayed) as a within- groups factor. Participants were randomly assigned to one of four groups: (i) high load non-

6 leading, (ii) high load leading, (iii) low load non-leading and (iv) low load leading (n = 66 per group).

Materials Eyewitness memory video A short (1-minute) film developed by the authors was used as the memory event. The video, which is based on previous misinformation paradigms (e.g. Takarangi, Parker & Garry, 2006), depicts a woman being robbed by a man. During the video, the woman is approached by a man asking for directions. The man then unexpectedly grabs the woman’s handbag and a brief altercation ensues, before the man runs away with the bag. Perceptual load was manipulated by the number of objects in the scene (e.g. objects on the tables, posters on the noticeboard, etc.). The high load version contained 110 items and the low load version contained 20 items, in addition to the furniture which remained constant across videos (see Figure 1).

Autobiographical memory test (AMT) Autobiographical memory specificity was assessed using an online version of the shortened Autobiographical Memory Test (AMT; Williams & Broadbent, 1986; de Decker, Hermans, Raes, & Eelen, 2003). The test contained 10 emotional cue words: five positive (‘happy’, ‘safe’, ‘interested’, ‘successful’, ‘surprised’) and five negative (‘sad’, ‘evil’, ‘awkward’, ‘emotionally hurt’, ‘lonely’). Positive and negative words were presented in alternating order. Participants were given a maximum of 60 seconds to recall a specific memory in response to each cue. A specific memory was defined as a unique event that took place over the course of a single day (or less), that was also more than seven days old. One neutral practice word (‘grass’) was administered before the test began to familiarise participants with the task parameters. Participants were provided with on-screen instructions for the duration of the test, stating that they should write down one specific memory for each cue. They were explicitly told that each memory should refer to one particular event lasting no more than a single day that occurred more than one week ago. Participants typed their responses into a text box below the cue word. The task automatically moved on to the next cue after 60 seconds.

7 Procedure After providing consent, participants watched either the high perceptual load orlow perceptual load version of the video. To ensure that the videos were matched for emotional characteristics, participants rated their valence and physiological arousal using two visual analogue scales ranging from 0 (‘positive’/’very relaxed’) to 100 (‘negative’/’very alert’). Participants were specifically asked to indicate how the video made them feel. Memory for the videos was tested immediately via a 14-item questionnaire containing either leading or neutrally phrased (non-leading) questions. The items included in each questionnaire may be seen in Table 1; both versions included five filler questions (Q1- Q5), and nine critical questions. These included two target-present line-ups featuring the victim (Q6) and thief (Q7) alongside four similar women and four similar men, respectively (see Figure 2). Q8-Q10 were non-leading or leading questions designed to test immediate memory (e.g. ‘Did the thief strike the woman?’ vs. ‘How many times did the thief strike the woman?’). Q11-Q14 were non-leading or leading questions designed to implant misinformation to be evaluated at follow-up (e.g. to insinuate that the woman was carrying a phone when in fact she was not). Leading questions were based on similar questions used in previous studies of the misinformation effect (Loftus & Palmer, 1974; Murphy & Greene, 2016). After completing the questionnaire, participants rated their confidence intheir memory on a 7-point Likert scale from 1 (‘not at all confident’) to 5 (‘very confident’). Finally, participants completed the Autobiographical Memory Test. Delayed memory recall was assessed one week later via a 12-item follow-up questionnaire containing three filler and nine critical questions (see Table 1). All participants received the same follow-up questionnaire via email. To assess the effects of perceptual load on false identification of persons of interest, participants were then presented with one of the non-target images from each line-up and asked if each person was the thief/victim they saw in the video.

8 A

Figure 1: Screenshots of the high perceptual load video (A) and the low perceptual load video (B) showing the victim alone (left), the thief asking the victim for directions (middle) and the altercation with the handbag (right).

9 Table 1: List of filler (Q1-5) and critical (Q6-14) questions used in the non-leading, leading and follow-up questionnaires. Non-leading Leading Follow-up Q1 What colour was the flower on the table? - What colour was the flower on the table? Q2 How many red chairs were there? - How many red chairs did you see? Q3 What colour was the thief's jacket? - What colour was the thief's jacket? Q4 What colour were the woman's trousers? - Q5 Did the thief exit through the same door he - - entered through? Q6 Victim ID - Is this the thief from the video? Q7 Thief ID - Is this the victim from the video? Q8 Did you see a camera on the table? Did you see the camera on the table? Was there a camera on the table? Q9 Did the thief put anything in his pocket? What did the thief put in his pocket? Did the thief put anything in his pocket? Q10 Did the thief strike the woman? How many times did the thief strike the Did the thief strike the woman? woman? Q11 Was the woman carrying a phone? Did the woman put her phone in her bag or Was the woman carrying a phone? her pocket? Q12 Was the thief wearing a hood? Was the thief's hood up or down? Was the thief wearing a hood? Q13 Was it raining outside? Did you notice that it was raining outside? Was it raining outside? Q14 How long was the total interaction How long was the total interaction between How long was the total interaction between the thief and victim? E.g. 10, 15, the thief and victim? E.g. 1, 2, 3 mins between the thief and victim? 30s

10 A B C D E

A B C D E

Figure 2: Line-ups presented in questionnaires for the victim (Q6) and the thief (Q7). In each case, participants were asked to “Please indicate if one of the above people was the victim/thief. Write A, B, C, D, E or ‘none’ if it was none of the above.” The correct answer for the victim (top row) was B and the correct answer for the thief (bottom row) was D. In the follow-up questionnaire, participants were asked “Is this the victim/thief from the video?” and presented with option E as the victim and option A as the thief. Responses options were ‘yes’ and ‘no’.

RESULTS Mood ratings Independent samples t-tests were carried out to confirm whether the high and low load videos were matched for valence and arousal. No differences were found for valence, t(262) = 1.84, p = .07, or arousal, t(262) = 0.46, p = .64, indicating that the videos were indeed matched across conditions. Mean valence (0 = positive; 100 = negative) was 79.67 (± 16.43) for the high load video and 75.27 (± 21.95) for the low load video. Mean arousal (0 = relaxed; 100 = alert) was 77.14 (± 16.16) for the high load video and 76.19 (± 17.31) for the low load video.

11 Memory specificity Responses on the AMT were manually scored as ‘specific’ or ‘non-specific’. Specific memories were classified as events that occurred at a specific time and place within the course of one day (c.f. de Decker, Hermans, Raes, & Eelen, 2003). An example of a specific memory for the word happy would be “I felt happy when my friend gave me a Polaroid camera as a present during my birthday party last Saturday”. Non-specific memories were sub-divided into one of three categories: extended memories lasting more than one day (“I felt happy when I was on holidays last month”), categorical memories consisting of a thematic summary of an event, e.g. “I feel happy whenever I see my family”) or semantic associations (e.g. “my dog”). Other responses were categorised as non-memory (not referring to a past event) or non-response (no answer provided). The measure of memory specificity employed in the following analyses is the proportion of memories coded as ‘specific’. Table 2 shows the mean percentage of specific and non-specific memories reported by participants (averaged across all 10 cue words). Approximately 60% of responses were classified as specific memories and 33% were classified as non-specific. The remainder were cases where participants failed to provide a memory or response.

Table 2. Mean percentage of specific and non-specific memories reported in the AMT, with non-specific memories separated by category. Specific Non-Specific No memory No response Extended Categoric Semantic 59.73% 2.35% 5.57% 25.49% 4.43% 1.48% (± 5.08) (± 1.60) (± 2.75) (± 3.10) (± 3.79) (± 0.58)

Note: SD is shown in parentheses.

Memory accuracy for critical questions The purpose of this study was to determine if perceptual load and autobiographical memory specificity influence eyewitness memory recall and susceptibility to misinformation immediately and after a delay. Table 3 shows group memory performance and accuracy for critical and filler items at Time 1 (immediate) and Time 2 (7 days later). 52 participants did not complete the follow-up questionnaire, resulting in a sample of 212 for Time 2.

12 Memory accuracy at Time 1 was defined as percentage of correct responses to the three critical questions (Q8-10). Mean accuracy was 74.75% (SD = 27.6%). A linear regression model was constructed in which perceptual load (high or low), question type (leading or non-leading) and mean-centred memory specificity score were entered as predictors and memory accuracy at Time 1 was the dependent variable. The two-way interactions between (1) memory specificity and perceptual load, (2) memory specificity and question type and (3) question type and load were added in a second block, and the three- way interaction was added in a third. The first model was significant (R2 = .44, F(3,260) = 68.93, p < .001), and question type and memory specificity score were both found to be significant predictors; accuracy was reduced for participants who received leading questions rather than non-leading questions (β = -.66, p < .001) and increased for those with better autobiographical memory specificity (β = .17, p < .001). Follow-up analysis showed that memory specificity significantly improved memory accuracy for both neutrally phrased (n = 132; β = .19, p = .03) and leading questions (n = 132; β = .25, p = .004). Contrary to our hypotheses, there was no main effect of perceptual load (β = .06, p = .68). The addition of the interactions did not improve the model fit (Model 2: R2 change = .005; F(3,257) = 0.82, p = .48; Model 3: R2 change = 0, F(1, 256) = 0.22, p = .64) and none of the interactions were found to be significant predictors of accuracy (all p’s > .18). Memory accuracy at Time 2 was defined as percentage of correct responses to the three original critical questions (Q8-Q10) plus the three questions designed to implant false information for follow-up (Q11-Q13). Mean memory accuracy on the six critical questions at Time 2 was 66.78% (SD = 25.81%). A linear regression model was built as for Time 1; the main effects of load, question type and memory specificity were entered in Model 1, with the 2-way interactions added in Model 2 and the 3-way interaction added in Model 3. Once more, the first model was significant (R2 = .27, F(3, 208) = 5.41, p = .001) and question type (β = -.24, p < .001) and memory specificity score (β = .15, p = .03) were significant predictors of accuracy; the presence of leading questions impaired memory while a more specific memory recall style was associated with improved memory accuracy. There was no effect of perceptual load (β = -.005, p = .94) and no significant interaction effects (Model 2: R2 change = .008; F(3, 205) = 0.6, p = .62; Model 3: R2 change = .001, F(1, 204) = 0.17, p = .68). Follow- up analysis revealed a borderline effect of specificity on accuracy in response to leading

13 questions (n = 107; β = .18, p = .06) and no significant effect on accuracy in response to non- leading questions (n = 105; β = .13, p = .18). A similar analysis was conducted to investigate the effects of load, question type and memory specificity on participants’ estimates of the length of the interaction between thief and victim (Q14). At Time 1, the model was highly significant (R2 = .57, F(3,259) = 40.46, p < .001), and effects of both question type (β = .56, p < .001) and specificity (β = -.14, p = .007) were observed, such that estimates of the duration were longer for those who received the leading variant of the question (which suggested a duration of minutes rather than seconds), and estimates were shorter for those with higher memory specificity scores. A significant interaction between specificity and question type was also observed (β = -.43, p = .007); follow-up analyses indicated that memory specificity only affected responses to the leading variant of the question. The effect of the leading question on participants’ estimate of the duration was reduced in participants with higher memory specificity scores (β = -.25, p = .005). Estimates of the duration of the interaction were reassessed at Time 2, but no significant effects of load, question type or specificity were observed (R2 = .15, F(3, 205) = 1.52, p = .21).

Identification of Thief and Victim Finally, the effect of perceptual load on eyewitness identification was evaluated. Six participants did not attempt to identify the thief and victim, leaving a total sample of 258. Overall, participants were better at recognising the victim than the thief at Time 1 (73% versus 53% correct), t(257) = 5.07, p < .001. However, perceptual load had no effect on participants’ ability to identify the victim (t(256) = 1.27, p = .21) or the thief (t(256) = 1.36, p = .18) from their respective line-ups (see Table 4). At Time 2, participants were presented with one of the foils from each line-up (option E for the victim and option A for the thief) and asked if this was the person they saw in the video. Participants who had incorrectly identified the foil options as the victim or thief at Time 1 were excluded from analyses at Time 2. In total, 19 participants were removed from the victim analysis and 17 from the thief analysis. No effects of perceptual load were found on false identification of the victim (t(191) = 1.27, p = .21) or the thief at follow-up (t(193) = 1.27, p = .21; see Table 4).

14 Table 3: Mean accuracy for all items, critical items and filler items at immediate and delayed recall for each group, and time estimates of the interaction between the victim and thief. High Load Low Load Leading Non-leading Leading Non-leading Immediate recall Total accuracy 52.46% (± 15.07) 66.29% (± 13.51) 51.70% (± 15.95) 67.42% (± 16.24) Filler (Q1-5) 52.32% (± 18.46) 52.12% (± 23.36) 49.80% (± 21.67) 52.12% (± 22.28) Critical (Q8-10) 55.56% (± 23.63) 90.91% (± 18.07) 58.59% (± 26.19) 93.94% (± 15.37) Time estimate (Q14) 55.53s (±32.90) 20.74s (±16.46) 58.02s (±41.37) 20.32s (±7.94)

Delayed recall Total accuracy 55.56% (± 18.36) 61.93% (± 18.77) 54.94% (± 19.48) 61.66% (± 19.36) Filler (Q1-3) 44.03% (± 24.26) 45.06% (± 26.82) 43.83% (± 21.30) 37.91% (± 27.50) Critical (Q11-13) 61.32 (± 24.84) 70.37% (± 26.04) 60.49% (± 26.56) 73.53% (± 24.98) Time estimate (Q14) 44.81s (± 40.32) 44.60s (± 56.33) 57.88s (± 51.75) 35.96s (± 31.93)

Note: Standard deviation (SD) is shown in parentheses

15 Table 4: Percentage of correct identifications of the victim and thief at Time 1 (immediate recall) and frequency of false identifications at Time 2 (delayed recall). High Load Low Load Immediate recall Victim ID 76% (± 42) 69% (± 46) Thief ID 57% (± 49) 49% (± 50)

Delayed recall Victim ID 39% (± 49) 32% (± 47) Thief ID 67% (± 48) 65% (± 48) Note: SD is shown in parentheses.

DISCUSSION We examined the role of perceptual load in eyewitness memory accuracy and susceptibility to misinformation at immediate and delayed recall. Despite its relevance to real-world situations, previous research in this area is limited to a handful of studies. In particular, the influence of load on memory recall at longer time intervals is unclear, with conflicting evidence being reported (Greene & Schacter, 2018; Murphy & Greene, 2016). Contrary to our hypothesis, we found no effect of load on memory accuracy or suggestibility at immediate recall or one-week follow-up. Load also had no effect on identification of the ‘victim’ or ‘thief’ at either timepoint. We did, however, find a strong misinformation effect in line with previous research (Loftus, 2005). Participants who received misleading questions performed significantly worse than those who received neutrally phrased questions. This effect was more pronounced at immediate recall (57% vs 92% accuracy) than delayed recall (61% vs 72% accuracy). Our second aim was to investigate the impact of trait-based memory specificity on eyewitness accuracy and susceptibility to misinformation. Previous research has shown that enhancing the level of detail with which a target event is recalled can improve memory performance in attentionally demanding situations (Greene & Schacter, 2018). However, no research has been carried out on whether an individual’s ability to recall detailed memories can protect against misinformation. Memory specificity was here found to be a significant predictor of accuracy for both neutrally phrased and leading questions, suggesting that a

16 tendency towards more specific autobiographical memory enhances eyewitness memory and, crucially, protects against misinformation. The effect on participants’ estimates of the duration of the thief and victim’s interaction was restricted to the leading condition, indicating that the influence of the misinformation was reduced for participants with better memory specificity. Having a specific memory recall style continued to offer protection against misinformation after a one-week delay, but the effect was weaker and not seen in every comparison. This result is not unexpected given that memories generally become less detailed with time. Overall, our results suggest that participants with better memory specificity are more likely to notice a mismatch between their own memory and post-event information. Combined with our previous report that a post-event interview designed to enhance episodic specificity reduces susceptibility to misinformation (Greene & Schacter, 2018), this finding has important implications for eyewitness memory research. Memory specificity training (MEST) has previously been shown to enhance specific recall in patient samples with memory deficits (Erten & Brown, 2018; Raes, Williams & Hermans, 2009). Based on our findings, we suggest that MEST may also have potential for reducing vulnerability to misinformation in healthy populations. Surprisingly, participants in the high load condition performed as well as those in the low load condition on the memory questionnaire. This contrasts with earlier findings, where high load impaired eyewitness memory retrieval (Greene & Schacter, 2018; Greene et al., 2017; Murphy & Greene, 2016). One possible explanation is that we failed to successfully manipulate load here. Specifically, the high load video we used may not have been perceptually demanding enough to constitute ‘high load’. Alternatively, the high load condition may not have been sufficiently more demanding than the low load condition to produce a difference between the groups. Indeed, previous researchers have noted the difficulty of defining and operationalising ‘perceptual load’ and inconsistencies inthe literature have been highlighted (Benoni & Tsal, 2013; Murphy, Groeger & Greene, 2016). In this study, load was operationalised as the number of task-irrelevant items in the scene (cf. Murphy & Greene, 2016). Compared to previous studies, our high load video contained more items (110 vs 51 items) and the ratio of high to low load items was greater (5.5 to 1 vs 4 to 1). By comparison then, the attentional demands imposed by our high load video

17 should have produced a stronger effect than previously observed. Thus, it seems unlikely that our results could be accounted for by a failure to manipulate load. Another explanation is that load effects were not captured by the memory questionnaire we used. Previous work has shown that the impact of load on attention is strongest for details that are ‘peripheral’ to the event being witnessed, e.g. a toy car placed in the background of a scene or a witness who appears briefly (Greene & Schacter, 2018; Greene et al., 2017; Murphy & Greene, 2016). In this study, most of the questions referred to ‘central’ aspects of the event; that is, details that occurred near the main characters (e.g. ‘what did the thief put in his pocket?’). Only two questions were concerned with peripheral details (‘did you see the camera on the table?’ and ‘did you notice that it was raining outside?’). Further, both characters were central to the target event and stayed on-screen for several seconds. Because of this, it is possible that participants were more likely to attend to and encode many of the details that were probed by the memory questionnaire, which resulted in better overall accuracy for both groups and reduced any load effects. One other important factor to consider is the emotional valence of the target event. In earlier load studies by Greene and colleagues, participants viewed a thief taking objects from an empty room as a witness passed by the window. In contrast, our video depicted a physical altercation between a thief and a victim followed by the victim’s bag being forcibly taken. The negative tone of the video was reflected in participants’ ratings following the video, which indicated high arousal and negative valence. Thus, it is possible that being in a state of high arousal heightened participants’ attentional focus, leading to improved memory encoding. Recent findings from Hoscheidt, LaBar, Ryan, Jacobs and Nadel (2014) support this argument; participants who rated a slideshow as highly arousing exhibited better memory performance and lower susceptibility to misinformation for the most aversive images. The authors concluded that high arousal caused by a negative event improved emotional memory and resistance to misinformation. Taken together with these results, we believe that the absence of load effects here can be explained by the centrality of the items and characters probed by the memory questionnaire and the alerting nature of the video itself. Our results highlight two key components of the perceptual load effect on eyewitness memory. Firstly, that perceptual load primarily impacts attention for peripheral or background information in a scene, and secondly, that attention is heightened in

18 response to negative events. It is important to note that ours is one of the only studies to use more complex, naturalistic stimuli to approximate eyewitness scenarios in which perceptual load is manipulated. More research is therefore needed to establish the real- world generalisability of load theory. Regarding memory specificity, our results offer preliminary evidence that having a specific memory recall style can protect against misinformation. To our knowledge, this study is also the first to demonstrate that the AMT can be used as an online measure of trait-based memory specificity. If memory specificity does in fact moderate vulnerability to leading information, it may be possible to reduce misinformation effects through memory specificity training.

Author Contributions FRF and CMG developed the study concept and design. Data collection was performed by FRF under the supervision of CMG. FRF and CMG performed data analysis and interpretation and wrote the manuscript. Both authors approved the final version of the manuscript for publication.

Acknowledgements This work was supported by an Irish Research Council grant to CMG. Anonymized data and materials have been made publicly available at the Open Science Framework (osf.io) and can be accessed at osf.io/2xp5f. Raw AMT responses will not be made public as they contain identifiable information.

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