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How to analyse accuracy, hits and false alarms in e-prime?

How to analyse accuracy, hits and false alarms in e-prime?



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It's the first time for me to do analyses with e-prime, and I have to analyse some data from an experiment. The data are, at the moment, in e-prime. I'd like to have the accuracy, the hits, and the false alarms for every subject. When I am in the anaylse window:

  1. Accuracy: I drop the subjects into the rows, and the Target ACC into the Data window. But do I take the ACC mean or the ACC count?

  2. Hits: Isn't it the same as accuracy? And, if not, what is the difference?

  3. False Alarms: do I just count them? How can I make it then comparable over different tasks (some tasks have more trials than the others).


Neuronal correlates of perception in early visual cortex

We used functional magnetic resonance imaging (fMRI) to measure activity in human early visual cortex (areas V1, V2 and V3) during a challenging contrast-detection task. Subjects attempted to detect the presence of slight contrast increments added to two kinds of background patterns. Behavioral responses were recorded so that the corresponding cortical activity could be grouped into the usual signal detection categories: hits, false alarms, misses and correct rejects. For both kinds of background patterns, the measured cortical activity was retinotopically specific. Hits and false alarms were associated with significantly more cortical activity than were correct rejects and misses. That false alarms evoked more activity than misses indicates that activity in early visual cortex corresponded to the subjects' percepts, rather than to the physically presented stimulus.


Using the yes/no recognition response pattern to detect memory malingering

Detection of feigned neurocognitive deficits is a challenge for neuropsychological assessment. We conducted two studies to examine whether memory malingering is characterized by an elevated proportion of false negatives during yes/no recognition testing and whether this could be a useful measure for assessment.

Methods

Study 1 examined 51 participants claiming compensation due to mental disorders, 51 patients with affective disorders not claiming compensation and 13 patients with established dementia. Claimants were sub-divided into suspected malingerers (n = 11) and non-malingerers (n = 40) according to the Test of Memory Malingering (TOMM). In study 2, non-clinical participants were instructed to either malinger memory deficits due to depression (n = 20), or to perform normally (n = 20).

Results

In study 1, suspected malingerers had more false negative responses on the recognition test than all other groups and false negative responding was correlated with Minnesota-Multiphasic Personality Inventory (MMPI) measures of deception.

In study 2, using a cut-off score derived from the clinical study, the number of false negative responses on the yes/no recognition test predicted group membership with comparable accuracy as the TOMM, combining both measures yielded the best classification. Upon interview, participants suspected the TOMM more often as a malingering test than the yes/no recognition test.

Conclusion

Results indicate that many malingers adopt a strategy of exaggerated false negative responding on a yes/no recognition memory test. This differentiates them from both dementia and affective disorder, recommending false negative responses as an efficient and inconspicuous screening measure of memory malingering.


DISCUSSION

The results of this experiment show that LTMs enhance attentional guidance during a perceptual discrimination task and influence neural signatures of target selection. Spatial expectations from LTM conferred behavioral benefits, as revealed by increased perceptual sensitivity and decreased RTs to targets appearing in remembered versus non-remembered locations. The d′ sensitivity index clearly shows that LTMs can influence perceptual analysis of the stimulus, thus confirming that top–down signals from LTM do more than change the response criterion through response biases. These findings replicate and extend previous results by Summerfield et al. (2006, 2011) by showing that predictions based on prior knowledge acquired from experience facilitate perceptual decisions about the presence of relevant objects when they are embedded within their natural scene contexts. The facilitation of RTs suggests that this perceptual benefit does not come at any speed cost. Instead, memory also speeds up responses to identify the target, leading to better perceptual discriminations within shorter latencies.

Target-related ERPs show that LTM can enhance neural processes related to target selection, as reflected by modulations of the N2pc component. This memory-driven modulation of target processing reveals a close and rapid interaction between memory and attention systems in the brain. We were able to identify an enhanced negative voltage over contralateral (versus ipsilateral) posterior electrodes with a similar time course as the N2pc. Importantly, memory cueing within complex scenes resulted in an interesting and unexpected finding: LTM-based spatial contextual memory cues reduced the magnitude of the N2pc in the valid condition.

Interestingly, the modulation of the N2pc by LTM cues differed qualitatively from what has been observed with spatial cueing of attention in typical visual search tasks. LTM for the target location in our task clearly and strongly attenuated the N2pc. In contrast, visual spatial cues in search tasks and in other types of perceptual attention tasks do not influence the N2pc (Brignani et al., 2010 Schankin & Schubö, 2010 Seiss et al., 2009 Kiss et al., 2008 Leblanc et al., 2008). Results from previous visual spatial cueing tasks have been interpreted as suggesting that the N2pc does not reflect the spatial guidance of attention (but see Woodman & Luck, 1999, 2003) or a selection process that is influenced by visual spatial attention. Instead, the N2pc appears to reflect a separate set of mechanisms related to feature-based selection processes guided by the identity of the target (Brignani et al., 2010 Kuo et al., 2009). The fact that we only observed the N2pc when the target stimulus was present in the scene reinforces the notion that the N2pc is linked to target-selection processes.

Furthermore, our results clearly point to possible differences in how memory cues and perceptual cues come to influence target selection processes. One possible explanation is that LTM for a specific target location within a scene primed the identification of the target attributes, diminishing the amount of visual analysis and suppression of distracting information required for effective target selection and identification. The cue in our task could activate specific memory traces for target/context configurations, facilitating the target selection and thus reducing the amount of resources required for the suppression of distracters. This interpretation is in line with the findings of Luck and Hillyard (1994), who reported that the N2pc is diminished when distracters are irrelevant or removed. Alternatively, these differences may stem from differences in modulations of neural signatures of target selection when targets are embedded in natural complex backgrounds versus simple visual backgrounds. Further experiments directly comparing ERPs produced when memory cues and perceptual cues guide attentional orienting within complex scenes are needed to settle this question.

Intriguingly, the attenuation of the N2pc by LTM also differs from what has been observed in previous experiments using ERPs in the contextual cueing paradigm. These have consistently reported a larger N2pc for targets appearing in repeated as opposed to novel displays (Schankin et al., 2011 Schankin & Schubö, 2009, 2010 Johnson et al., 2007). The discrepancy could result simply from the timings during which the selection processes can start to operate. It may be that, in general, appearance of a target within a learned context enhances the selection processes indexed by the N2pc, but when the context is preactivated some of the selection processes can proceed ahead of time, in anticipation of the target appearance. In our experiment, the participant is pre-exposed to the scene triggering the contextual memory for the target location. It is possible therefore to process the context–target association and engage neural processes relating to prioritising the target features and/or inhibiting the irrelevant features in the contextual background. In the contextual cueing paradigm, context and target occur only simultaneously, and all the work for prioritising the target features and suppressing distractor features needs to be carried out on-line. Evidence that selection has a head start in our memory-orienting task comes from analysis of the lateralised alpha-band activity, which becomes desynchronized over posterior contralateral electrodes in anticipation of the probe scenes (Stokes et al., 2012 see also Summerfield et al., 2011). Notably, we also found two nonlateralized modulations in the ERPs elicited by valid as compared with neutral cues, which may reflect non-spatially specific retrieval of the associations between target and context (see also Summerfield et al., 2011).

Alternatively, the discrepancy between the attenuation of the N2pc found here and the enhancement of the N2pc in previous contextual cueing tasks could be explained by the difference in the types of memory traces involved. In the classical contextual cueing paradigm, it is assumed that the memories guiding attention are implicit in nature (Chun & Jiang, 1998, 2003). In the current experiment, the memories for target–context associations were formed by explicit instruction, and the contexts were rich in visual detail, thus making them more available for explicit recall. When tested explicitly, participants were accurate at retrieving the learned locations of the keys, and reported high confidence levels. This pattern of results also occurred for scenes containing no keys within the orienting task, showing that performance on the memory retrieval task was not dependent on re-exposure to the location of keys during the immediately preceding task). There is no way, of course, to rule out the formation and availability of implicit memory traces in our task, but we can be confident that explicit memory traces were also available, and these may have played a role. This difference in the types of memory sources available may account for the difference in attentional guidance strategies, target selection, and/or distracter suppression processes engaged by the tasks. This interpretation would be in line with proposals by Moscovitch and colleagues, who suggest that explicit, episodic memories may play a unique top–down role in regulating and facilitating a number of cognitive functions, such as priming (Sheldon & Moscovitch, 2010) and problem solving (Sheldon, McAndrews, & Moscovitch, 2011).

Additionally, it has been argued that in arbitrary target–distracter arrays, the local context around the target is sufficient to elicit a contextual cueing effect (Olson & Chun, 2002), whereas in naturalistic scenes, global information is crucial for guiding attention (Brockmole, Castelhano, & Henderson, 2006). It is possible that, in the current experiment, the contextual effect is guided by a more holistic scene representation with a target location associated within it, as opposed to spatial configurations with arbitrary target–distracter relationships. Thus, the mechanisms at play in contextual cueing versus in our experiment may differ for multiple different reasons, and these are not mutually exclusive.

We also identified a later, spatially specific effect characterized by a lateralized posterior positivity contralateral to the target location, labeled here as PCP, which was not found to be modulated by memory cues. In reviewing previous ERP studies on PCP-like components, we found a recent description by Hilimire and colleagues (Hilimire, Mounts, Parks, & Corballis, 2010) of a positive posterior contralateral component, called Ptc (approximately 290–340 msec poststimulus), proposed to index additional processing necessary to individuate the target after it is identified under conditions of high competition between stimuli in an array. This finding was not predicted by our initial hypothesis and needs to be interpreted with caution however, it is plausible to propose that similar later target-related processes are engaged when discrimination of relevant objects within crowded scenes is required.

Earlier visual potentials P1 and/or N1 are also typically modulated by visuospatial attention (Hillyard & Anllo-Vento, 1998). In a previous memory-based orienting task using transiently appearing targets, Summerfield and colleagues recently reported modulation of these early visual potentials. The amplitude of the P1 was enhanced by memory cues, whereas the N1 potential showed a more distinctive pattern of modulation—with contralateral attenuation and ipsilateral enhancement as well as latency reduction (Summerfield et al., 2011). However, the effects on P1 and N1 were not significant in our current experiment. This may simply have reflected the challenging conditions for measuring these potentials under our current task parameters. Typically, visual–spatial tasks use targets that appear transiently onto blank or very simple backgrounds. In our task, however, the target stimuli were intrinsically bound to an associated complex, cluttered scene, which it makes difficult to measure the influence of spatial biases on visual evoked potentials.

The results of the topographical analysis are preliminary but raise the possibility of different neural sources or functional networks when target selection in our environment is facilitated by LTM cues. Further experimentation using alternative methods with higher spatial resolution may help characterize the brain areas involved in guiding target selection during memory-guided attention.

In summary, this study provides evidence about the role of explicit long-term contextual memories in optimizing visual search and in modulating the ongoing processing of incoming information by biasing neural activity related to target selection. Furthermore, the data imply that the spatial or contextual information from LTM facilitates target selection through a different top–down mechanism than that engaged by attention-directing perceptual cues. Whereas perceptual cues do not influence feature-based selection of targets, memory cues may facilitate identification of target features and substantially diminish the neural resources involved in this process. Furthermore, search for objects in cluttered environments based on explicit memories of specific target–context associations results in a different neural modulation of target identification than that observed when unconsciously memorized contextual relations guide visual search. Further experimentation aimed at comparing the neural mechanisms of top–down biases triggered by memory cues versus perceptual cues will be especially informative.


Methods

Participants

Seventy-six young adults (42 male mean age = 19.6) and 78 older adults (25 male mean age = 71.4) participated 1 . Young adults were undergraduates at the Georgia Institute of Technology and participated for course credit. Older adults were from the Atlanta community and were paid $10 per hour. Participants from the two age groups were randomly assigned to the recognition and cued-recall test conditions.

Materials

Stimuli consisted of 120 five-letter nouns and adjectives of moderate frequency (e.g., glass, clown) that were divided into three sets of 40. Each participant studied words from two of the sets with the remaining set acting as unstudied items during test. Word sets were counterbalanced across participants such that each word served equally often as studied and unstudied. The recognition and cued-recall tests used the same words and word sets. For cued recall, word-stem cues were created by replacing the final two letters from each word with underlines. Each stem could be completed by at least two words but only one word from the stimulus set. Testing was done on Windows-based PCs with 11 keys across the top of the keyboard (“

Procedure

At study, participants were told they would see a list of words that they should remember for a later memory test, and that they “may want to associate each word with something that is personally meaningful” or “generate a mental image.” They were also asked to rate how likely they were to remember each word on the later memory test. Words were presented one at a time in the center of the screen with the JOL scale (𠇀 … 100”) appearing at the bottom of the screen 3 seconds after the word was presented. Participants were told a rating of 0 meant they were �solutely certain” they would not remember the word, 100 meant they were �solutely certain” they would remember the word, and 10 to 90 indicated intermediate levels of certainty. Participants were encouraged to use the entire scale. Words remained visible until the JOL rating was entered. One second after the rating, the next word was presented.

Following study, participants completed two distractor tasks, resulting in a study-test delay of 10 to 12 minutes. For recognition, participants saw words one at a time in the center of the screen and were asked to rate the quality of their memory for each using “Recollect”, �miliar”, or “No Memory”. Definitions of these responses were similar to those used in Remember/Know studies (e.g., Gardiner, 1988). Briefly, participants were told to respond Recollect when they could clearly remember specific details associated with studying a word Familiar when they felt the word was from the study list but could not remember specific details and No Memory when the word neither felt familiar, nor could they remember any details about its earlier presentation. The response options were presented at the bottom of the screen [“Recollect (R), Familiar (F), No Memory (N)”] and participants responded using the “R”, 𠇏”, and “N” keys.

For cued recall, participants were presented with word-stems and told to complete each with a word from the study list. If they could not remember a study word, they were to complete the stem with the first word that came to mind. Immediately after a word was entered, the R/F/N response options, defined as in the recognition condition, appeared at the bottom of the screen.


Discussion

Participants made recognition memory judgments in the fMRI scanner for previously studied scenes and novel scenes. We first identified brain areas that distinguished true memory (i.e., hits) and false memory (i.e., false alarms) without taking confidence ratings into account. This analysis identified the left hippocampus, 15 neocortical regions, and the caudate nucleus. Activity in all of these regions was linearly related to confidence levels. Thus, these regions likely distinguished high and low confidence rather than true and false memories per se.

When we equated the confidence ratings associated with true and false memories and repeated the same analysis, only three regions (all in parietal cortex) distinguished true and false memories. Two of these regions minimally overlapped with regions identified when confidence was not equated, and one region was new. In addition, there was one sub-threshold MTL cluster (Table 1 Fig. 2). Thus, brain activity can distinguish true memory and false memory even when the two kinds of memory judgments are made with similar levels of confidence.

Last, we identified regions where activity was similar for true memory and false memory (and also higher than for correct rejections). One cortical region (and no MTL regions) exhibited similar activity for true memory and false memory before confidence was taken into account. When confidence was equated for hits and false alarms (and correct rejections), two regions in posterior parietal cortex exhibited similar activity for true and false memories (Table 2 Fig. 3). These included one new region and one region that partially overlapped with the region that had been identified before equating for confidence. Accordingly, brain activity in these two regions was related to the judgment that the scene had been presented previously, regardless whether the judgment was correct or incorrect.

The hippocampus and true and false memories

Hippocampal activity differentiated true and false memories when confidence ratings were different for the two conditions. This finding is consistent with the results of the only other study that compared hits and false alarms and where targets and foils were unrelated (Kirwan et al. 2009). In that study, hits were associated with higher confidence than false alarms, and the hippocampus exhibited higher activity for hits than for false alarms.

Other findings also indicate that hippocampal activity can differentiate true and false memories when confidence ratings are not controlled, so long as accuracy is relatively high as in our study [79.8% correct accuracy = hit rate/(hit rate + false alarm rate)]. For example, Gutchess and Schacter (2011) and Kirwan et al. (2009) obtained a hippocampal finding when all hits were contrasted with all false alarms, and memory scores were high (accuracy = 70.7% and 75.4% correct, respectively). In contrast, studies that did not obtain hippocampal findings had lower accuracy rates (Schacter et al. 1996: 54.0% correct Schacter et al. 1997: 61.1% correct Cabeza et al. 2001: 52.4% correct Slotnick and Schacter 2004: 53.4% correct Garoff-Eaton et al. 2007: 59.3% correct Iidaka et al. 2012: 54.1% correct).

The relationship between accuracy and the finding that hippocampal activity can differentiate true and false memory is less clear in studies where true or false memory refers to accurate or inaccurate retrieval of the context in which material was studied (hippocampal findings: Cansino et al. 2002: 69.8% correct where chance = 25% Dobbins et al. 2002: hit rate = 80%, but false alarm rate was not available Weis et al. 2004: 52.1% correct where chance = 25% no hippocampal findings: Okado and Stark 2003: 78.8% correct Kahn et al. 2004: 65.3% correct Stark et al. 2010: 63.5% correct).

When confidence ratings were equated (and accuracy was high, 74.9% correct), hippocampal activity marginally distinguished true and false memories (i.e., voxel-wise probability P < 0.05 uncorrected). In contrast, no MTL regions distinguished true and false memories in the case of memory judgments made with low confidence (accuracy = 45.7% correct). These findings for the hippocampus are consistent with results from two other studies. In one study (Kim and Cabeza 2007), the hippocampus distinguished high-confidence true and false memories (Sure-Hits and Sure-False Alarms accuracy = 71.0% correct) but not low-confidence true and false memories (Unsure-Hits and Unsure-False Alarms accuracy = 43.5% correct). In the second study (Dennis et al. 2012), hippocampal activity distinguished true and false recollection (using a Remember, Know, New Paradigm accuracy = 70.6% correct). Recollection is typically associated with high confidence and high accuracy (Wixted 2007).

The neocortex and true and false memories

The neocortex distinguished true and false memories when confidence ratings associated with the two conditions were similar. Specifically, parietal cortex (BA 7/40) exhibited higher activity for hits than for false alarms. The finding is in agreement with earlier work demonstrating a role for this region in the retrieval of contextual information (Curran 2000), a circumstance more likely to occur for true memory than for false memory. Kim and Cabeza (2007) also found regions in the parietal cortex that distinguished true and false memories when confidence was equated, but these regions exhibited higher activity for false memory than for true memory.

Other studies that did not equate confidence found that activity in posterolateral parietal cortex (BA 7/39/40) was higher for true than for false memory (Cabeza et al. 2001 Slotnick and Schacter 2004 Stark et al. 2010). We also detected clusters in the posterolateral parietal cortex for the contrast of hits and false alarms when confidence levels were not equated. Activity in these regions was linearly related to confidence levels. Accordingly, activity in posterolateral parietal cortex is likely related to confidence in the perceived oldness of the stimuli (also see Wheeler and Buckner 2003, 2004) and not to true and false memories per se. Similarly, the activity we observed in prefrontal cortex (when confidence was not equated) is likely related to confidence and not to true and false memories. This activity was higher for false alarms than for hits (also see Schacter and Slotnick 2004) and was negatively related to confidence ratings.

Last, one region in posterolateral parietal cortex responded similarly for true and false memories before confidence was equated. In contrast, earlier studies that tested for regions that responded similarly for true and false memories (Kahn et al. 2004 Slotnick and Schacter 2004 Garoff-Eaton et al. 2006, 2007 Gutchess and Schacter 2011) found a number of brain regions in frontal, temporal, parietal, and occipital cortex. Note that in these earlier studies, the targets and foils were conceptually or perceptually related. This circumstance may increase the number of regions that respond similarly for true and false memories.


Anuario de Psicología Jurídica 2019

Annual Review of Legal Psychology 2019

Director/Editor Antonio L. Manzanero Subdirectores/Associate Editors Enrique Calzada Collantes Rocío Gómez Hermoso Miguel Hierro Requena Mónica Pereira Dávila M.ª Paz Ruiz Tejedor Jorge Sobral Fernández María Yela García

Volumen 29, Año 2019 ISSN: 1133-0740

Stobbs & Kebbell, 2003 Tharinger, Horton, & Millea, 1990

Valenti-Hein & Schwartz, 1993). In many cases, the testimonies associated

with people with ID have been considered less credible (Peled et al., 2004). On the other hand, one myth implies that people with ID may be more believable (Bottoms, Nysse-Carris, Harris, & Tyda, 2003).

Some studies (Manzanero, Contreras, Recio, Alemany, & Martorell, 2012) have shown that people with ID may perform approximately the same as others in forensic contexts. Moreover, their autobiographical memories may be quite stable over time, being their ability to describe an event independent of the degree of disability (Morales

et al., 2017). Indeed, Henry et al. (2011) found no correlation between

credibility assessment and either witness mental age or anxiety. For eyewitnesses with ID, the key may be the lack of studies regarding differentiating characteristics of their true/false statements. With other types of population (mainly children), forensic psychology has proposed useful procedures for assessing credibility by analyzing the content of statements. One of these procedures is Statement Validity Assessment (SVA) (Köhnken, Manzanero, & Scott,

2015 Steller & Köhnken, 1989 Volbert & Steller, 2014), a technique

that assesses the credibility of statements given by minors who are alleged victims of sexual abuse. SVA is a comprehensive procedure for generating and testing hypotheses about the source and validity of a given statement. It includes methods of collecting relevant data regarding such hypotheses and techniques for analyzing these data, plus guidelines for drawing conclusions regarding the hypotheses.

Criteria-based content analysis (CBCA) is a method included in SVA for distinguishing truthful from fabricated statements. It is not applicable for distinguishing statements experienced as real memories, which are actually the result of suggestive influences

(Scott & Manzanero, 2015 Scott, Manzanero, Muñoz, & Köhnken,

2014), but may be applied complementarily to other procedures

(Blandón-Gitlin, López, Masip, & Fenn, 2017). The use of the CBCA

content criteria in the absence of a detailed analysis of the moderator variables would produce rather low percentages of discrimination between true and false statements, where around 30% of false alarms have been found (Oberlader et al., 2016). Previous research has shown that the level of accuracy in the classification of true and false statements can sometimes be low even when evaluators are specifically trained in this technique, which could indicate that CBCA has basic problems (Akehurst, Bull, Vrij, & Köhnken, 2004).

Table 1.Content Criteria for Statement Credibility Assessment GENERAL CHARACTERISTICS

1. Logical structure. 2. Unstructured production. 3. Quantity of details. SPECIFIC CONTENTS 4. Contextual embedding. 5. Descriptions of interactions. 6. Reproduction of conversation.

7. Unexpected complication during the incident. PECULIARITIES OF CONTENT

8. Unusual details. 9. Superfluous details.

10. Accurately reported details misunderstood. 11. Related external associations.

12. Accounts of subjective mental state. 13. Attribution of perpetrator’s mental state. MOTIVATION-RELATED CONTENTS 14. Spontaneous corrections. 15. Admitting lack of memory.

16. Raising doubts about one’s own testimony. 17. Self-deprecation.

18. Pardoning the perpetrator. OFFENCE-SPECIFIC ELEMENTS 19. Details characteristic of the offence.

CBCA takes into account 19 content criteria grouped into five categories (see Table 1): general characteristics, specific contents of

the statement, peculiarities of content, motivation-related contents, and offence-specific elements. The basic assumption of the CBCA is that statements based on memories of real events are qualitatively different from statements not based on experience (Undeutsch, 1982). According to his original proposal, each content criterion is an indicator of truth its presence in a given statement is viewed as an indicator of the truth of that statement, but its absence does not necessarily mean the statement is false. This assumption has been shown to be incomplete, because it does not consider false memories as a source of incorrect statements, nor the effects of liars knowing about the criteria (Vrij, Akehurst, Soukara, & Bull, 2004a). However, not all the criteria are always relevant when it comes to discriminating

(Bekerian & Dennett, 1992 Manzanero, 2006, 2009 Manzanero,

López, & Aróztegui, 2016 Porter & Yuille, 1996 Sporer & Sharman,

2006 Vrij, 2005 Vrij, Akehurst, Soukara, & Bull, 2004b) the presence

of these criteria depends on a host of moderator variables (Hauch,

Blandón-Gitlin, Masip, & Sporer, 2015 Oberlader et al., 2016).

Among these variables are preparation (Manzanero & Diges, 1995), time delay (Manzanero, 2006 McDougall & Bull, 2015), the individual’s age (Comblain, D’Argembeau, & Van der Linden, 2005

Roberts & Lamb, 2010), and the asking of questions and multiple

retrieval (Strömwall, Bengtsson, Leander, & Granhag, 2004). Also, fantasies, lies, dreams, and post-event information do not each involve the same differentiating characteristics. Furthermore, changing a small detail, however important it may be, of a real event—such as whether the role played in the event was witness or protagonist

(Manzanero, 2009)—is not the same as fabricating an entire event.

Indeed, false statements rarely are entirely fabricated but originate, in part, from actual experiences that are modified to create something new. In addition, the characteristics of statements vary depending on the person’s ability to generate a plausible statement. This is relevant to people with ID, it having been proposed that lying would usually be cognitively more complex than telling the truth (Vrij, Fisher, Mann,

& Leal, 2006) and, therefore, would involve a greater demand for

cognitive resources (Vrij & Heaven, 1999).

The aims of the present study were (i) to use CBCA in order to analyze the statements given by true and simulating witnesses with intellectual disability, (ii) people’s intuitive ability to discri-minate between the two types of statements, and (iii) the ability to discriminate through big data analysis.

Video recorded accounts provided by 32 people with mild to moderate, non-specific intellectual disability were used as material to be analyzed. Fifteen participants were true witnesses to a real event that took place two years ago when the bus they were travelling during a day trip caught fire. Those participants had an average IQ of 62.00 (SD = 10.07) and were 33.93 years old (SD = 6.49). Seventeen other participants who provided simulated accounts of the same event had an average IQ of 58.41 (SD = 8.42) and were 31.75 years old (SD = 7.07). No significant differences were found in IQ as a function of condition, F(1, 30) = 1.204, p = .281, η2 = .039. The IQ scores were obtained by the

Wechsler Adult Intelligence Scale (WAIS-IV Wechsler, 2008). All of these 32 participants provided informed consent. The statements were obtained with a procedure similar to that used in other studies (Vrij et al., 2004a, 2004b), as follows:

All the participants who did not go on the day trip knew the event beforehand, because they knew the people involved as they belong to the same care centre for people with intellectual disabilities. The event was very commented by everyone when it took place and it was even informed in the media. In any case, a verbal summary of the most important information about the day trip, such as its location, the main complication on the day trip, and the course of the day was given to all participants of either condition. To increase the ecological

validity of the study, all 32 participants were encouraged to give their testimonies as best they could. While they were not put under the stress of trying to make the interviewer believe their testimony (to prevent undue tension in the interview), we told them they would be invited to a soda if they succeeded in convincing the interviewer that they had, in fact, experienced the event (all of them actually received this invitation).

Two forensic psychologists, experts on interviewing and taking testimony, from the Unit for Victims with Intellectual Disability, interviewed each of these 32 participants individually. An audiovisual recording was made of all interviews. The same instructions were followed: “We want you to tell us, with as much detail as you can, from the beginning to the end, what happened when you went on the day trip and the bus caught fire. We want you to tell us even the things you think are not very important.” Once a free-recall statement was obtained, all participants were asked the same questions: Who were you with? Where was it? Where did you go? What did you do? What happened afterwards? The forensic psychologists who conducted the interviews were blind to the groups (true vs. false experience) the participants belonged to.

Once the testimonies were obtained, the videos were evaluated using two different procedures: a) intuitive analysis carried out by people without knowledge of forensic psychology and b) technical analysis performed by forensic psychologists using CBCA criteria.

Of the 32 statements discussed above, two videos of the true condition and one of the false condition were removed from the intuitive judgments. This was due to communication problems that prevented the evaluators from understanding what the participants said in the conditions in which the intuitive evaluation was carried out.

Intuitive Credibility Assessment

There were 33 participants as evaluators (6 men and 27 women age average 23.54, SD = 4.04), recruited among psychology students in Spain, who wanted to voluntarily participate in the study. They did not receive any compensation for participating, and had no specific knowledge of credibility analysis techniques and no specific understanding of intellectual disability.

The video recordings of sixteen true and thirteen false statements were shown on a large-format screen at the university. All evaluators attended the showing at the same time, but they were prevented from interacting so that they did not bias each other while making their individual assessments. The instructions were as follows: “Next, a series of videos will be shown in which people with intellectual disability are talking about an event related to a bus accident. Some of the statements were given by individuals who experienced that event the others were given by individuals who, although they were not there, were told about the event, and they have given their statement with the intention of making us believe they were there. The task is to decide who is telling the truth and who is lying to us. As you are assessing each statement, bear in mind that the interviewees are all people with intellectual disability, so their way of telling things may be special.” The twenty-nine videos were shown in random order to prevent a learning effect from impacting the ability to evaluate true and false statements. After each video was shown, the evaluators were asked to categorize the statement as true or false. In the first evaluations, it was observed that the viewing of 29 videos produced saturation and fatigue in the evaluators. To avoid this circumstance leading to random decisions, it was decided to submit to each evaluator a maximum of 15 videos, taking care that finally all the videos were evaluated. In any case, the evaluators were warned that when they felt very tired, they should warn the experimenters. A total of 197 evaluations of the true condition and 256 evaluations of the false condition were collected.

Analysis of Phenomenological Characteristics of the Statements Using CBCA Criteria

The interview video recordings were transcribed to facilitate analysis of the phenomenological characteristics of the statements. Two trained CBCA evaluators each made their own criteria assessment of each statement and then reached an interjudge agreement. To assess the CBCA criteria codings for inter-coder reliability, an agreement index was computed as follows: AI = agreements / (agreements + disagreements). For all the variables, this was greater than the cut-off of .80 (Tversky, 1977), except for “logical structure” and “unstructured production”, where it was .67.

Each criterion was assessed in terms of its absence or presence in the statement, as was originally defined by Steller and Köhnken

(1989). To measure the degree of presence of each criterion, the

evaluators quantified how many times the criterion was present throughout the report. For the criteria of “quantity of details”, the micropropositions that described, as objectively as possible, what happened in the actual event were used, which is a better measure than counting words because it is not influenced by the descriptive style used by participants.

Criterion 13, “attribution of perpetrator’s mental state”, was modified to be “attribution of other’s mental state”. Criterion 19, “details characteristic of the offence”, was modified to be “details characteristic of the event”. Criteria 17 (self-deprecation) and 18 “Pardoning the perpetrator”, were not taken into consideration, because of the nature of the event.

CBCA Characteristics of the Statements

An ANOVA test was conducted to assess the effects of the type of statement on the number of times each CBCA criterion was present in each report. As multiple comparisons were conducted, the significance level was adjusted with a Bonferroni adjustment to .003. Table 2 shows only “quantity of details” was significant in determining truth. The remaining 16 criteria (some of which rarely occurred) produced no significant differences.

Big Data Analysis of Characteristic Features of Statements

Big data techniques aim towards complex data exploration and analysis. High-Dimensional Visualization (HDV) graphs facilitate the visualization of complex data. This technique displays all the data at once, enabling researchers to graphically explore in search of data distribution patterns (for more information see Manzanero, Alemany,

Recio, Vallet, & Aróztegui, 2015 Manzanero, El-Astal, & Aróztegui,

2009 Vallet, Manzanero, Aróztegui, & García-Zurdo, 2017). The graphs

are similar to scatter plots. The different variables corresponding to a subject’s responses on questionnaire items are represented as a point in a high-dimensional space (17 values or dimensions in this study). When there are more than three variables, as in this study, mathematical dimensionality reduction techniques are used to build a 3D graph (Buja et al., 2008 Cox & Cox, 2001). Each point in the hyperspace has a distance to each of the other points. Multidimensional scaling will search 3D points, preserving the distances between points as much as possible (Barton & Valdés, 2008). Sammon’s error (Barton

& Valdés, 2008) is used to calculate the 3D transformation error.

3D points are represented using Virtual Reality Modelling Language (VRML). VRML files allow graphical rotation and exploration to facilitate graphical data analysis. 3D graphs permit visual exploration of the data in search of its distribution patterns.

Figure 1 represents all criteria, regardless of whether their

the dimensionality reduction through multidimensional scaling

(Buja et al., 2008) was very good, with a small Sammon’s error of

.03. The dotted line graphically dividing true statements from false statements shows correct classification of 81.25 percent (simulated statements were classified as being true 29.42%).

A possible explanation for several of the CBCA criteria not discriminating could stem from the variability among participants. As can be seen in Figure 1, the cloud of dots that graphically represents each type of statement is very dispersed and overlapped.

Figure 1. HDV Graph of Content Criteria in True (Light) and False (Dark) Statements, Including all CBCA Criteria.

Note. Sammon´s error = .030 correct classification = 81.25%.

Intuitive Credibility Assessment

Considering the 197 evaluations of the true- and the 256 evaluations of the false videos, discriminability accuracy (hits, false alarms, omissions, and correct rejections), discriminability index (d’), and response criterion (c) as specified by Signal Detection Theory

(MacMillan & Kaplan, 1985 Tanner & Swets, 1954) were measured.

Analysis of the credibility assessments based on lay participants’ natural ability found above chance accuracy for the discriminability index (d’) was .626 (SD = .121), Zd = 5.159, p < .05.

The response criterion (c) reached a score of .086 (SD = .061), Zc = 1.412, p = ns The subjects had a neutral response criterion (scores equal to 0 indicate a neutral criterion, greater than 0 a conservative criterion, and less than 0 a liberal criterion). The proportion of statements correctly classified was 61.81 percent (see Table 3), with 65.48% of false statements being correctly assessed and 58.98% of the true ones.

Table 3. Intuitive Responses for Each Type of Statement

Assessment False CR: 129 (65.48%) O: 105 (41.02%) True FA: 68 (34.52%) H: 151 (58.98%)

Note. CR = correct rejection O = omission FA = false alarm H = hit.

Depending on the number of times a story was considered true or false by the intuitive judges, the probability of “truthfulness” was established (number of times considered true / number of evaluations made for that testimony). The average probability of truthfulness assigned to the false testimonies was 36.37 (SD = 31.64), while that assigned to the true ones was 64.00 (SD = 23.93), F(1, 28) = 6.750, p < .05, η2 = .200.

The levels of disabilities of the persons with ID could be one of the indicators on which the evaluators based their intuitive assessments. However, no significant effects were found when participants’ IQ was analysed based on how their statements had been classified, considering the four possible types of response (H, FA, O, and CR), F(3, 26) = 0.498, p = ns, η2 = .056. As can be seen in Table 4, IQ means were

Table 4. IQ Means and Standard Deviations of the Subjects according to the Type of Response Issued by the Evaluators

Correct rejections 58.09 9.74

Relationship between CBCA Criteria and Intuitive Credibility Assessment

The Pearson correlation (bilateral) between the degree of presence of each CBCA criteria in the testimonies and the probability of truthfulness indicates that the evaluators’ natural ability may have been mediated by the following criteria: “structured production”, Table 2. Means, Standard Deviations, and ANOVA Values for Each Dependent Variable

N = 17 True StatementN = 15

Mean SD Mean SD F(1, 30) p η2

Logical structure 5.67 2.74 6.86 2.32 1.726 .199 .054

Unstructured production 6.11 2.54 5.46 2.82 0.470 .498 .015

Quantity of details * 7.35 3.60 13.93 4.74 19.800 .000 .398

Contextual embedding 2.52 1.06 3.93 1.90 6.812 .014 .185

Interactions 1.23 1.98 1.53 2.26 0.158 .694 .005

Conversations 0.41 0.50 1.40 1.59 5.878 .022 .164

Unexpected complications 0.47 0.79 0.40 0.50 0.086 .771 .003

Unusual details 0.58 0.79 0.80 0.86 0.523 .475 .017

Superfluous details 0.00 0.00 0.40 0.82 3.984 .055 .117

Details misunderstood 0.23 0.56 0.00 0.00 2.616 .116 .080

External associations 0.05 0.24 0.13 0.35 0.496 .487 .016

Subjective mental state 0.88 0.99 1.00 1.25 0.088 .769 .003

Other’s mental state 1.47 1.28 1.13 1.50 0.469 .499 .015

Corrections 0.17 0.39 0.13 0.35 0.106 .747 .004

Lack of memory 2.38 3.47 2.16 3.08 0.034 .855 .001

Doubts 0.29 0.58 0.50 0.62 0.919 .345 .030

Characteristic details 1.88 1.45 2.13 1.18 0.281 .600 .009

r(29) = .546, p < .01 “quantity of details”, r(29) = .618, p < .01 “unexpected complications”, r(29) = .526, p < .01 and “characteristic details”, r(29) = .437, p < .05. No significant correlations were found for the remaining 13 criteria (see Table 5). The greater presence of these criteria would imply greater intuitive truthfulness.

Table 5. Pearson Correlations between Content Criteria and Intuitive Assessments of “True”

Logical structure** .546 .002

Unstructured production -.346 .066

Quantity of details** .618 .000

Contextual embedding .238 .214

Unexpected complications** .526 .003

Superfluous details .150 .436

Details misunderstood -.149 .441

External associations .166 .389

Subjective mental state .014 .944

Other’s mental state .098 .614

Characteristic details* .437 .018

*p < .05 (bilateral) **p < .01 (bilateral).

In line with many other studies (not involving truth tellers/liars with ID), the lay participants could not discriminate between false and true stories at a level to be considered useful in a forensic context

(Rassin, 1999), this being one of the reasons why CBCA was developed.

The CBCA technique did indeed discriminate at a better level. However, of the 19 criteria, only one (“quantity of details”) was found significant. This criterion, which is present in some lies, also deemed “richness in detail”, has also been identified as potential biases which may lead to incorrect veracity judgements (Nahari & Vrij, 2015). “Quantity of details” was found in the present study to be significant for people who have ID, even though when truly narrating an event, they tend to give fewer details than the general population (Dent, 1986 Kebbell &

Wagstaff, 1997 Perlman, Ericson, Esses, & Isaacs, 1994).

ID is a component of certain syndromes that have associated deficits in language development and articulation. This might explain why several of the CBCA criteria were rarely present in the current study. In Down’s Syndrome, for example—the most common genetic syndrome with an ID component—language disorders are one of the effects. In spontaneous conversation, the speech of people with ID is less intelligible, and they have more difficulty with grammatical structuring (Rice, Warren, & Betz, 2005)—in fact, their problems with sentence structuring are similar to those of individuals diagnosed with language development disorder (Laws & Bishop, 2003).

Thus, if the criteria that help us to determine the true statements of people with ID indeed is “quantity of details”, what could happen if their true accounts are compared with true accounts from the general population? For those with ID who have reduced vocabulary, semantic, and autobiographical memory deficits (rendering them unable to detail the event), we could run the risk that such people will suffer an erroneous judgment of their credibility, and thus, revictimisation could result.

However, since the natural ability evaluators were capable of discriminating between true and false statements at only 12% above-chance accuracy, a procedure that achieves better accuracy is needed.

If we were to extrapolate such natural ability data to a law enforcement setting, for example, we could predict that the testimonies of people with ID would be correctly assessed in only 60 percent of cases, resulting in many true accounts not being believed. This percentage is not far from what the police (and others) usually reach when judging the statements of people with standard development (Manzanero,

To analyse what the possible basis is for intuitively assessing the testimonies of people with ID—which, in turn, is going to determine the credibility assessments granted in forensic and legal settings—we correlated the probability of a “true” assessment with the IQ and the CBCA content criteria. As in the study by Henry et al. (2011), the results showed that IQ did not account for the lay evaluators’ decisions. In relation to the different CBCA criteria, only four criteria appear to mediate intuitive truthfulness (structured production, quantity of details, unexpected complications, and characteristic details).

On the other hand, big data analysis reached a better classification score. It must be taken into account that, surprisingly, these results were obtained after considering all CBCA variables, not only the ones yielding significant differences, although, initially, it was expected that the variable showing significant differences should lead to a better classification in comparison with the rest. Because that was not the case, it seems that useful information is held by those other variables not showing significant differences and the big data technique is able to profit from it, providing better classification quality. This approach could maybe allow to find, in a near future, an improved way of distinguishing true and false statements.

The authors of this article declare no conflict of interest.

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Introduction

A hallmark of human cognition is its flexibility, i.e., the ability to redirect goal behavior to meet changing demands [1]. Traditionally, cognitive flexibility is measured using task-switching, set-shifting, and reversal learning tasks. In all of these tasks, participants learn an initial response pattern, rule, or strategy that must then be adapted when the contingencies or task requirements are abruptly changed. Typically, contingency/requirement changes are not cued, so participants must learn that a change has occurred through feedback on obtained outcomes. High anxiety individuals display less flexible performance on these tasks by continuing to rely on the acquired response even after learning it is no longer relevant [2–6]. Poor performance in high anxiety individuals is frequently attributed to differences in attentional control, particularly greater interference from the irrelevant response [7].

The relative cognitive inflexibility demonstrated by people with high anxiety can have important consequences for behavior when faced with rapidly changing environments. There are many situations in everyday life where learned information must be inhibited because it is no longer relevant in the decision environment: e.g., preferred commuting routes can be blocked by new construction, and food items in the grocery store can be relocated to different shelves/aisles. There are also situations that require overcoming a preexisting rule-of-thumb or preference that biases decision making. For example, people tend to be risk averse even in situations when taking a risk could result in a better outcome: paying a costly insurance premium for a low probability event or opting out of an experimental health procedure with a strong success rate. An important limitation of existing cognitive flexibility research is that it has almost entirely focused on flexibility when overcoming recently acquired/learned information. However, the ability to flexibly overcome a preexisting bias (like risk-aversion) may be particularly important for high anxiety individuals because research suggests they are more vulnerable to biases than low anxiety individuals [8–9].

It is important to note that people are not risk avoidant in all contexts. Rather there is a strong tendency to be risk averse when choices are framed in terms of gains and to be risk seeking when choices are framed in terms of losses. This framing bias was documented in the classic Asian Disease Problem [10]: when disease outbreak intervention programs were framed in terms of lives saved (i.e., 200 people will be saved OR 1/3 probability 600 people will be saved, 2/3 probability 0 people will be saved) participants preferred the sure option over the gamble option, but when the same programs were framed in terms of lives lost participants preferred the gamble option over the sure option. In business, health, and social domains it is well documented that choice framing can produce risk preference reversals that detrimentally influence decisions [11], and high trait anxiety individuals have been shown to be more vulnerable to framing bias than low anxiety individuals [12–13]. While there is substantial research on the impact of such preexisting biases on decision making, only one study has examined whether framing bias can be flexibly overcome using feedback on obtained outcomes [14], and no research has assessed whether trait anxiety can influence the reduction of bias.

Previous research [14] used a task that combined a framing manipulation with a gambling task to evaluate whether bias could be overcome by learning through outcome feedback. In this task a sure option of either $50 or -$50 was presented along with an ambiguous gamble option. One gamble option had average outcomes greater than the sure gain of $50, and the other gamble option had average outcomes worse than the sure loss of $50. Thus, unlike typical risky choice framing tasks that produce shifts in gamble preference, each choice trial had a normatively correct choice that was sometimes inconsistent with framing bias. For example, when given a choice between the sure gain and the “good” gamble option, framing bias drives selection of the sure gain, but it is more advantageous on average to select the gamble option. It was found that participants developed accurate knowledge of the gains and losses provided by the gamble options but continued to make frame-biased choices, even when the bias led to a normatively incorrect choice [14]. It is reasonable to expect that high trait anxiety will exacerbate the problems with overcoming bias experienced by participants in [14]. High anxiety could increase initial vulnerability to framing bias (as in past research, [12–13]), and anxiety-related changes in attentional control could make it more difficult for high anxiety individuals to adapt to change and overcome their preexisting bias.


Introduction

With the Internet’s rapid advance and social penetration, its negative effects have emerged prominently. Many research reports have indicated that some on-line users are becoming addicted to the Internet, in much the same way that other individuals become addicted to drugs, alcohol, or gambling, which results in academic failure, reduced work performance and even marital discord and separation [1]–[3]. Currently, Internet addiction disorder (IAD), also described as pathological Internet use or problematic Internet use, is defined as an individual’s inability to control his or her use of the Internet, which eventually causes psychological, social, school, and work difficulties or dysfunction in a person’s life [4]–[6]. The description of IAD is based on the definition for substance dependence or pathological gambling, which makes it a compulsive-impulsive spectrum disorder. Studies have reported that IAD consists of at least three subtypes: excessive gaming, sexual preoccupations, and e-mail/text messaging. All of the subtypes share the common components, i.e., preoccupation, mood modification, excessive use, withdrawal, tolerance and functional impairment [7]. By using the Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition, DSM-IV) criteria, some authors suggest that IAD is an impulse disorder or that it is at least related to impulse control disorders [8]–[9]. One study investigated deficient inhibitory control in individuals with IAD using a visual go/no-go task by event-related potentials (ERPs). This study indicated that individuals with IAD were more impulsive than the controls and shared neuropsychological and ERPs characteristics of compulsive-impulsive spectrum disorder, which supports the conclusion that IAD is an impulse disorder or is at least related to impulse control disorders [10].

In many situations of everyday life, the environment changes quite rapidly, requiring flexible behavioural adaptations. Many previous studies indicated that the task-switching paradigm has been a powerful method to study flexible behavioural adaptations to changing contexts [11]. The field of cognitive psychology has been interested in the use of this methodology, mainly because of factors that can potentially affect adaptive behaviour. These factors include the time participants have to prepare for a new task as well as response-related processes, such as response selection and execution. Many compulsive-impulsive spectrum disorders present cognitive bias and executive functional deficit characteristics. For example, using the “Alcohol Shifting Task”, a variant of the go/no-go paradigm, Xavier Noël and colleagues measured the response times and the accuracy of responses to targets and distracters [12]. Sometimes the alcohol-related words were the targets for the “go” response, with neutral words used as distracters, and sometimes the reverse scenario was presented. Several shifts in the type of the target occurred during the task. Relative to controls, the detoxified polysubstance abusers with alcoholism were generally slower to respond to targets. A signal detection analysis also indicated that, relative to controls, the detoxified polysubstance abusers with alcoholism had increased difficulty discriminating between targets and distracters, and they showed more signs of decision bias, reflecting an increased readiness to respond to both targets and distracters. However, these discrimination and inhibition deficits were more pronounced when alcohol-related words were the targets. These results suggest that detoxified polysubstance abusers with alcoholism have cognitive biases towards information related to alcohol and that these biases, as well as poor executive functions (lower mental flexibility and response inhibition), might be responsible for the failure of these individuals to maintain abstinence.

Another study examined the relationship between attention and gambling behaviour by measuring the level of Stroop interference towards gambling-related words in a group of regular poker machine players [13]. A computerised gambling-specific modified version of the Stroop task was used to assess response latencies. The test included three word categories: gambling, drug and neutral. The study found that the participants who had difficulty in controlling their gambling behaviour (the low control group) took significantly longer to name the colour of the words related to gambling, whereas those who had good control over their gambling behaviour (the high control group) did not show any significant differences among the three word categories. These results showed the role of cognitive distortions and biases in addictive gambling behaviour.

Recently, a research study investigated cognitive biases among pathological gambling poker players, experienced poker players and inexperienced poker players in a computerised two-player poker task that used a fictive opponent [14]. The results showed that experienced poker players had a significantly lower average margin of error in estimation of probability than pathological gambling poker players and inexperienced poker players and that pathological gambling poker players played hands with lower winning probabilities than inexperienced poker players. The study concluded that pathological gambling players presented with impaired cognitive bias styles related to probability estimation and decision making. These impairments may have implications for the assessment and treatment of cognitive biases in pathological gambling poker players.

The task-switching paradigm, such as the cue-related go/no-go switching task, provides an experimental approach to study this flexibility in changing situations [12]. Because IAD belongs to the compulsive-impulsive spectrum disorder, theoretically, it should present with the cognitive bias and executive functioning deficit characteristics of some disorders, such as pathological gambling, drug addiction or alcohol abuse, in testing with the cue-related go/no-go switching task. Until now, no studies on cognitive bias and executive function involving mental flexibility and response inhibition in IAD have been reported. Because Internet game addiction is a type of IAD, we selected individuals with Internet game addiction (IGA) as research subjects in this study. Participants’ behavioural responses were recorded while they performed an Internet game-shifting task. The purpose of the present study was to examine whether IGA displays cognitive bias and executive functioning deficit characteristics in an Internet game-shifting task.


Results

Cued Recollection: Placebo vs Encoding

Raw cued recollection performance was directly related to valence as indicated by main effects on hits (F(2,76)=14.109, p<0.001, =0.271) and accuracy (F(2,76)=14.259, p<0.001, =0.273). These main effects were due to the typical advantage for negative stimuli (Figure 1a and c). There was a trending main effect of valence on false alarms (F(2,76)=2.488, p=0.090, =0.061) due to more false alarms for positive stimuli (Figure 1b). No other main effects or interactions were found (all F’s<2.000, all p’s>0.200).

Raw performance on the cued recollection task. (a) Mean hit rates, (b) false alarm rates, and (c) accuracy (hit rates—false alarm rates). Error bars are SEM.

The distributions of DPSD-based recollection and familiarity estimates from the bootstrapping procedure were all normal (Figure 2). Negative (95% CI: (0.004, 0.297), p=0.022) and positive (95% CI: (−0.008, 0.207), p=0.034) recollection estimates in the Encoding group were reduced compared with the Placebo group, though the confidence interval of the difference distribution for positive recollection estimates implied a less reliable effect. The Encoding and Placebo groups did not differ on neutral recollection estimates or familiarity estimates (all p’s>0.100), though there was trend for greater positive familiarity estimates in the Encoding group (95% CI: (−0.085, 0.465), p=0.078).

Distributions of (a) negative, (b) neutral, and (c) positive dual process signal detection-based recollection estimates generated from the bootstrapping procedure on the cued recollection confidence data.

Recognition: Placebo vs Encoding

The ANOVAs on hits, false alarms, and accuracy from the picture recognition test were not significant (all F’s<1 and p’s>0.250), except for a trending effect of valence on false alarms (F(2,76)=2.286, p=0.109, =0.057) due to greater false alarms to positive pictures.

The ANOVA on recollection estimates from the IRK procedure revealed a main effect of valence (F(2,76)=3.962, p=0.007, =0.122) and a marginal effect of group (F(1,38)=5.289, p=0.054, =0.094) with no interaction (F(2,76)=1.559, p=0.217). The main effect of valence was due to smaller recollection estimates for positive pictures, and the main effect of group was due to attenuated recollection in the Encoding group (Figure 3). Although the interaction was not significant, exploratory contrasts found both negative (t(38)=2.154, p=0.038, d=0.681) and positive (t(38)=2.164, p=0.037, d=0.684) recollection estimates to be lower in the Encoding group compared with the Placebo group with no reliable difference between neutral estimates (t(38)=1.079, p=0.287, d=0.341), consistent with the DPSD-based recollection estimates. There were no main effects or interactions on familiarity estimates (all F’s<1 and all p’s>0.250).

Mean independence remember/know estimates of (a) recollection and (b) familiarity from the recognition task. Error bars are SEM.

Cued Recollection: Placebo vs Retrieval

Emotional valence strongly modulated hits (F(2,76)=18.143, p<0.001, =0.323) and accuracy (F(2,76)=16.417, p<0.001, =0.302) such that negative pictures showed a memory advantage. Valence modulated false alarms (F(2,76)=4.121, p=0.020, =0.098) due to more false alarms for positive stimuli. All other main effects and interactions were not statistically significant (all F’s<2.000, p>0.150).

Although the difference between negative recollection estimates in the Placebo and Retrieval groups was not significant (95% CI: (−0.075, 0.198), p=0.193), by comparison, the difference between the Encoding and Retrieval groups was also not significant (95% CI: (−0.052, 0.224), p=0.101). This can be seen in Figure 2a, which shows the negative recollection distribution of the Retrieval group lying in between those of the Encoding and Placebo groups. The distribution for positive recollection estimates of the Retrieval group was also in between the Placebo and Encoding groups with no difference between either of them (Figure 2c Retrieval vs Placebo: 95% CI: (−0.077, 0.157), p=0.235 Retrieval vs Encoding: 95% CI: (−0.124, 0.147), p=0.168). There were no differences between the Retrieval and Placebo groups for neutral recollection estimates and familiarity estimates (all p’s>0.200), though there was a trend for reduced neutral familiarity estimates in the Retrieval group (95% CI: (−0.057, 0.421), p=0.066).

Recognition: Placebo vs Retrieval

The ANOVA on hits comparing Placebo and Retrieval groups did not reveal any main effects or interactions (all F’s<1, all p’s>0.250). However, the ANOVA on false alarms revealed a main effect of valence (F(2,76)=4.247, p=0.018 =0.101), again explained by greater false alarms for positive pictures. Although a main effect of group did not reach significance (F(1,38)=2.389, p=0.130, =0.059), exploratory contrasts found that there was a trend for the Retrieval group to false alarm more to positive stimuli than the Placebo group (t(38)=1.816, p=0.077, d=0.075), consistent with the high confidence false alarms on the cued recollection test (SOM), and this was not found for other valences (all t’s<1.500, all p’s>0.200). Finally, there was a trending main effect of valence on accuracy (F(2,76)=2.695, p=0.074, =0.066) explained by decreased accuracy for positive pictures, owing to the increased false alarms. No other main effects and interactions were significant (all F’s<1.500, all p’s>0.200).

There was a trending main effect of valence on recollection estimates (F(2,76)=2.651, p=0.077, =0.065) with positive recollection estimates being the smallest but no effect of group or interaction (all F’s<2.000, all p’s>0.150). Although there were no between-group differences, exploratory contrasts were conducted to determine consistent trends between the cued recollection and picture recognition tests, as were found among the Encoding group’s analyses. These analyses found that both negative (t(19)=2.146, p=0.045, d=0.480) and positive (t(19)=2.393, p=0.027, d=0.535) recollection estimates were reduced compared to neutral estimates in the Retrieval group, but neither of these effects was found in the Placebo group (Figure 3a neutral vs negative: t(38)=0.566, p=0.578 neutral vs positive: t(19)=1.163, p=0.259). There were no main effects or interactions on the familiarity estimates (all F’s<1 and all p’s>0.250).


Neuronal correlates of perception in early visual cortex

We used functional magnetic resonance imaging (fMRI) to measure activity in human early visual cortex (areas V1, V2 and V3) during a challenging contrast-detection task. Subjects attempted to detect the presence of slight contrast increments added to two kinds of background patterns. Behavioral responses were recorded so that the corresponding cortical activity could be grouped into the usual signal detection categories: hits, false alarms, misses and correct rejects. For both kinds of background patterns, the measured cortical activity was retinotopically specific. Hits and false alarms were associated with significantly more cortical activity than were correct rejects and misses. That false alarms evoked more activity than misses indicates that activity in early visual cortex corresponded to the subjects' percepts, rather than to the physically presented stimulus.


Methods

Participants

Seventy-six young adults (42 male mean age = 19.6) and 78 older adults (25 male mean age = 71.4) participated 1 . Young adults were undergraduates at the Georgia Institute of Technology and participated for course credit. Older adults were from the Atlanta community and were paid $10 per hour. Participants from the two age groups were randomly assigned to the recognition and cued-recall test conditions.

Materials

Stimuli consisted of 120 five-letter nouns and adjectives of moderate frequency (e.g., glass, clown) that were divided into three sets of 40. Each participant studied words from two of the sets with the remaining set acting as unstudied items during test. Word sets were counterbalanced across participants such that each word served equally often as studied and unstudied. The recognition and cued-recall tests used the same words and word sets. For cued recall, word-stem cues were created by replacing the final two letters from each word with underlines. Each stem could be completed by at least two words but only one word from the stimulus set. Testing was done on Windows-based PCs with 11 keys across the top of the keyboard (“

Procedure

At study, participants were told they would see a list of words that they should remember for a later memory test, and that they “may want to associate each word with something that is personally meaningful” or “generate a mental image.” They were also asked to rate how likely they were to remember each word on the later memory test. Words were presented one at a time in the center of the screen with the JOL scale (𠇀 … 100”) appearing at the bottom of the screen 3 seconds after the word was presented. Participants were told a rating of 0 meant they were �solutely certain” they would not remember the word, 100 meant they were �solutely certain” they would remember the word, and 10 to 90 indicated intermediate levels of certainty. Participants were encouraged to use the entire scale. Words remained visible until the JOL rating was entered. One second after the rating, the next word was presented.

Following study, participants completed two distractor tasks, resulting in a study-test delay of 10 to 12 minutes. For recognition, participants saw words one at a time in the center of the screen and were asked to rate the quality of their memory for each using “Recollect”, �miliar”, or “No Memory”. Definitions of these responses were similar to those used in Remember/Know studies (e.g., Gardiner, 1988). Briefly, participants were told to respond Recollect when they could clearly remember specific details associated with studying a word Familiar when they felt the word was from the study list but could not remember specific details and No Memory when the word neither felt familiar, nor could they remember any details about its earlier presentation. The response options were presented at the bottom of the screen [“Recollect (R), Familiar (F), No Memory (N)”] and participants responded using the “R”, 𠇏”, and “N” keys.

For cued recall, participants were presented with word-stems and told to complete each with a word from the study list. If they could not remember a study word, they were to complete the stem with the first word that came to mind. Immediately after a word was entered, the R/F/N response options, defined as in the recognition condition, appeared at the bottom of the screen.


Using the yes/no recognition response pattern to detect memory malingering

Detection of feigned neurocognitive deficits is a challenge for neuropsychological assessment. We conducted two studies to examine whether memory malingering is characterized by an elevated proportion of false negatives during yes/no recognition testing and whether this could be a useful measure for assessment.

Methods

Study 1 examined 51 participants claiming compensation due to mental disorders, 51 patients with affective disorders not claiming compensation and 13 patients with established dementia. Claimants were sub-divided into suspected malingerers (n = 11) and non-malingerers (n = 40) according to the Test of Memory Malingering (TOMM). In study 2, non-clinical participants were instructed to either malinger memory deficits due to depression (n = 20), or to perform normally (n = 20).

Results

In study 1, suspected malingerers had more false negative responses on the recognition test than all other groups and false negative responding was correlated with Minnesota-Multiphasic Personality Inventory (MMPI) measures of deception.

In study 2, using a cut-off score derived from the clinical study, the number of false negative responses on the yes/no recognition test predicted group membership with comparable accuracy as the TOMM, combining both measures yielded the best classification. Upon interview, participants suspected the TOMM more often as a malingering test than the yes/no recognition test.

Conclusion

Results indicate that many malingers adopt a strategy of exaggerated false negative responding on a yes/no recognition memory test. This differentiates them from both dementia and affective disorder, recommending false negative responses as an efficient and inconspicuous screening measure of memory malingering.


Introduction

With the Internet’s rapid advance and social penetration, its negative effects have emerged prominently. Many research reports have indicated that some on-line users are becoming addicted to the Internet, in much the same way that other individuals become addicted to drugs, alcohol, or gambling, which results in academic failure, reduced work performance and even marital discord and separation [1]–[3]. Currently, Internet addiction disorder (IAD), also described as pathological Internet use or problematic Internet use, is defined as an individual’s inability to control his or her use of the Internet, which eventually causes psychological, social, school, and work difficulties or dysfunction in a person’s life [4]–[6]. The description of IAD is based on the definition for substance dependence or pathological gambling, which makes it a compulsive-impulsive spectrum disorder. Studies have reported that IAD consists of at least three subtypes: excessive gaming, sexual preoccupations, and e-mail/text messaging. All of the subtypes share the common components, i.e., preoccupation, mood modification, excessive use, withdrawal, tolerance and functional impairment [7]. By using the Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition, DSM-IV) criteria, some authors suggest that IAD is an impulse disorder or that it is at least related to impulse control disorders [8]–[9]. One study investigated deficient inhibitory control in individuals with IAD using a visual go/no-go task by event-related potentials (ERPs). This study indicated that individuals with IAD were more impulsive than the controls and shared neuropsychological and ERPs characteristics of compulsive-impulsive spectrum disorder, which supports the conclusion that IAD is an impulse disorder or is at least related to impulse control disorders [10].

In many situations of everyday life, the environment changes quite rapidly, requiring flexible behavioural adaptations. Many previous studies indicated that the task-switching paradigm has been a powerful method to study flexible behavioural adaptations to changing contexts [11]. The field of cognitive psychology has been interested in the use of this methodology, mainly because of factors that can potentially affect adaptive behaviour. These factors include the time participants have to prepare for a new task as well as response-related processes, such as response selection and execution. Many compulsive-impulsive spectrum disorders present cognitive bias and executive functional deficit characteristics. For example, using the “Alcohol Shifting Task”, a variant of the go/no-go paradigm, Xavier Noël and colleagues measured the response times and the accuracy of responses to targets and distracters [12]. Sometimes the alcohol-related words were the targets for the “go” response, with neutral words used as distracters, and sometimes the reverse scenario was presented. Several shifts in the type of the target occurred during the task. Relative to controls, the detoxified polysubstance abusers with alcoholism were generally slower to respond to targets. A signal detection analysis also indicated that, relative to controls, the detoxified polysubstance abusers with alcoholism had increased difficulty discriminating between targets and distracters, and they showed more signs of decision bias, reflecting an increased readiness to respond to both targets and distracters. However, these discrimination and inhibition deficits were more pronounced when alcohol-related words were the targets. These results suggest that detoxified polysubstance abusers with alcoholism have cognitive biases towards information related to alcohol and that these biases, as well as poor executive functions (lower mental flexibility and response inhibition), might be responsible for the failure of these individuals to maintain abstinence.

Another study examined the relationship between attention and gambling behaviour by measuring the level of Stroop interference towards gambling-related words in a group of regular poker machine players [13]. A computerised gambling-specific modified version of the Stroop task was used to assess response latencies. The test included three word categories: gambling, drug and neutral. The study found that the participants who had difficulty in controlling their gambling behaviour (the low control group) took significantly longer to name the colour of the words related to gambling, whereas those who had good control over their gambling behaviour (the high control group) did not show any significant differences among the three word categories. These results showed the role of cognitive distortions and biases in addictive gambling behaviour.

Recently, a research study investigated cognitive biases among pathological gambling poker players, experienced poker players and inexperienced poker players in a computerised two-player poker task that used a fictive opponent [14]. The results showed that experienced poker players had a significantly lower average margin of error in estimation of probability than pathological gambling poker players and inexperienced poker players and that pathological gambling poker players played hands with lower winning probabilities than inexperienced poker players. The study concluded that pathological gambling players presented with impaired cognitive bias styles related to probability estimation and decision making. These impairments may have implications for the assessment and treatment of cognitive biases in pathological gambling poker players.

The task-switching paradigm, such as the cue-related go/no-go switching task, provides an experimental approach to study this flexibility in changing situations [12]. Because IAD belongs to the compulsive-impulsive spectrum disorder, theoretically, it should present with the cognitive bias and executive functioning deficit characteristics of some disorders, such as pathological gambling, drug addiction or alcohol abuse, in testing with the cue-related go/no-go switching task. Until now, no studies on cognitive bias and executive function involving mental flexibility and response inhibition in IAD have been reported. Because Internet game addiction is a type of IAD, we selected individuals with Internet game addiction (IGA) as research subjects in this study. Participants’ behavioural responses were recorded while they performed an Internet game-shifting task. The purpose of the present study was to examine whether IGA displays cognitive bias and executive functioning deficit characteristics in an Internet game-shifting task.


Anuario de Psicología Jurídica 2019

Annual Review of Legal Psychology 2019

Director/Editor Antonio L. Manzanero Subdirectores/Associate Editors Enrique Calzada Collantes Rocío Gómez Hermoso Miguel Hierro Requena Mónica Pereira Dávila M.ª Paz Ruiz Tejedor Jorge Sobral Fernández María Yela García

Volumen 29, Año 2019 ISSN: 1133-0740

Stobbs & Kebbell, 2003 Tharinger, Horton, & Millea, 1990

Valenti-Hein & Schwartz, 1993). In many cases, the testimonies associated

with people with ID have been considered less credible (Peled et al., 2004). On the other hand, one myth implies that people with ID may be more believable (Bottoms, Nysse-Carris, Harris, & Tyda, 2003).

Some studies (Manzanero, Contreras, Recio, Alemany, & Martorell, 2012) have shown that people with ID may perform approximately the same as others in forensic contexts. Moreover, their autobiographical memories may be quite stable over time, being their ability to describe an event independent of the degree of disability (Morales

et al., 2017). Indeed, Henry et al. (2011) found no correlation between

credibility assessment and either witness mental age or anxiety. For eyewitnesses with ID, the key may be the lack of studies regarding differentiating characteristics of their true/false statements. With other types of population (mainly children), forensic psychology has proposed useful procedures for assessing credibility by analyzing the content of statements. One of these procedures is Statement Validity Assessment (SVA) (Köhnken, Manzanero, & Scott,

2015 Steller & Köhnken, 1989 Volbert & Steller, 2014), a technique

that assesses the credibility of statements given by minors who are alleged victims of sexual abuse. SVA is a comprehensive procedure for generating and testing hypotheses about the source and validity of a given statement. It includes methods of collecting relevant data regarding such hypotheses and techniques for analyzing these data, plus guidelines for drawing conclusions regarding the hypotheses.

Criteria-based content analysis (CBCA) is a method included in SVA for distinguishing truthful from fabricated statements. It is not applicable for distinguishing statements experienced as real memories, which are actually the result of suggestive influences

(Scott & Manzanero, 2015 Scott, Manzanero, Muñoz, & Köhnken,

2014), but may be applied complementarily to other procedures

(Blandón-Gitlin, López, Masip, & Fenn, 2017). The use of the CBCA

content criteria in the absence of a detailed analysis of the moderator variables would produce rather low percentages of discrimination between true and false statements, where around 30% of false alarms have been found (Oberlader et al., 2016). Previous research has shown that the level of accuracy in the classification of true and false statements can sometimes be low even when evaluators are specifically trained in this technique, which could indicate that CBCA has basic problems (Akehurst, Bull, Vrij, & Köhnken, 2004).

Table 1.Content Criteria for Statement Credibility Assessment GENERAL CHARACTERISTICS

1. Logical structure. 2. Unstructured production. 3. Quantity of details. SPECIFIC CONTENTS 4. Contextual embedding. 5. Descriptions of interactions. 6. Reproduction of conversation.

7. Unexpected complication during the incident. PECULIARITIES OF CONTENT

8. Unusual details. 9. Superfluous details.

10. Accurately reported details misunderstood. 11. Related external associations.

12. Accounts of subjective mental state. 13. Attribution of perpetrator’s mental state. MOTIVATION-RELATED CONTENTS 14. Spontaneous corrections. 15. Admitting lack of memory.

16. Raising doubts about one’s own testimony. 17. Self-deprecation.

18. Pardoning the perpetrator. OFFENCE-SPECIFIC ELEMENTS 19. Details characteristic of the offence.

CBCA takes into account 19 content criteria grouped into five categories (see Table 1): general characteristics, specific contents of

the statement, peculiarities of content, motivation-related contents, and offence-specific elements. The basic assumption of the CBCA is that statements based on memories of real events are qualitatively different from statements not based on experience (Undeutsch, 1982). According to his original proposal, each content criterion is an indicator of truth its presence in a given statement is viewed as an indicator of the truth of that statement, but its absence does not necessarily mean the statement is false. This assumption has been shown to be incomplete, because it does not consider false memories as a source of incorrect statements, nor the effects of liars knowing about the criteria (Vrij, Akehurst, Soukara, & Bull, 2004a). However, not all the criteria are always relevant when it comes to discriminating

(Bekerian & Dennett, 1992 Manzanero, 2006, 2009 Manzanero,

López, & Aróztegui, 2016 Porter & Yuille, 1996 Sporer & Sharman,

2006 Vrij, 2005 Vrij, Akehurst, Soukara, & Bull, 2004b) the presence

of these criteria depends on a host of moderator variables (Hauch,

Blandón-Gitlin, Masip, & Sporer, 2015 Oberlader et al., 2016).

Among these variables are preparation (Manzanero & Diges, 1995), time delay (Manzanero, 2006 McDougall & Bull, 2015), the individual’s age (Comblain, D’Argembeau, & Van der Linden, 2005

Roberts & Lamb, 2010), and the asking of questions and multiple

retrieval (Strömwall, Bengtsson, Leander, & Granhag, 2004). Also, fantasies, lies, dreams, and post-event information do not each involve the same differentiating characteristics. Furthermore, changing a small detail, however important it may be, of a real event—such as whether the role played in the event was witness or protagonist

(Manzanero, 2009)—is not the same as fabricating an entire event.

Indeed, false statements rarely are entirely fabricated but originate, in part, from actual experiences that are modified to create something new. In addition, the characteristics of statements vary depending on the person’s ability to generate a plausible statement. This is relevant to people with ID, it having been proposed that lying would usually be cognitively more complex than telling the truth (Vrij, Fisher, Mann,

& Leal, 2006) and, therefore, would involve a greater demand for

cognitive resources (Vrij & Heaven, 1999).

The aims of the present study were (i) to use CBCA in order to analyze the statements given by true and simulating witnesses with intellectual disability, (ii) people’s intuitive ability to discri-minate between the two types of statements, and (iii) the ability to discriminate through big data analysis.

Video recorded accounts provided by 32 people with mild to moderate, non-specific intellectual disability were used as material to be analyzed. Fifteen participants were true witnesses to a real event that took place two years ago when the bus they were travelling during a day trip caught fire. Those participants had an average IQ of 62.00 (SD = 10.07) and were 33.93 years old (SD = 6.49). Seventeen other participants who provided simulated accounts of the same event had an average IQ of 58.41 (SD = 8.42) and were 31.75 years old (SD = 7.07). No significant differences were found in IQ as a function of condition, F(1, 30) = 1.204, p = .281, η2 = .039. The IQ scores were obtained by the

Wechsler Adult Intelligence Scale (WAIS-IV Wechsler, 2008). All of these 32 participants provided informed consent. The statements were obtained with a procedure similar to that used in other studies (Vrij et al., 2004a, 2004b), as follows:

All the participants who did not go on the day trip knew the event beforehand, because they knew the people involved as they belong to the same care centre for people with intellectual disabilities. The event was very commented by everyone when it took place and it was even informed in the media. In any case, a verbal summary of the most important information about the day trip, such as its location, the main complication on the day trip, and the course of the day was given to all participants of either condition. To increase the ecological

validity of the study, all 32 participants were encouraged to give their testimonies as best they could. While they were not put under the stress of trying to make the interviewer believe their testimony (to prevent undue tension in the interview), we told them they would be invited to a soda if they succeeded in convincing the interviewer that they had, in fact, experienced the event (all of them actually received this invitation).

Two forensic psychologists, experts on interviewing and taking testimony, from the Unit for Victims with Intellectual Disability, interviewed each of these 32 participants individually. An audiovisual recording was made of all interviews. The same instructions were followed: “We want you to tell us, with as much detail as you can, from the beginning to the end, what happened when you went on the day trip and the bus caught fire. We want you to tell us even the things you think are not very important.” Once a free-recall statement was obtained, all participants were asked the same questions: Who were you with? Where was it? Where did you go? What did you do? What happened afterwards? The forensic psychologists who conducted the interviews were blind to the groups (true vs. false experience) the participants belonged to.

Once the testimonies were obtained, the videos were evaluated using two different procedures: a) intuitive analysis carried out by people without knowledge of forensic psychology and b) technical analysis performed by forensic psychologists using CBCA criteria.

Of the 32 statements discussed above, two videos of the true condition and one of the false condition were removed from the intuitive judgments. This was due to communication problems that prevented the evaluators from understanding what the participants said in the conditions in which the intuitive evaluation was carried out.

Intuitive Credibility Assessment

There were 33 participants as evaluators (6 men and 27 women age average 23.54, SD = 4.04), recruited among psychology students in Spain, who wanted to voluntarily participate in the study. They did not receive any compensation for participating, and had no specific knowledge of credibility analysis techniques and no specific understanding of intellectual disability.

The video recordings of sixteen true and thirteen false statements were shown on a large-format screen at the university. All evaluators attended the showing at the same time, but they were prevented from interacting so that they did not bias each other while making their individual assessments. The instructions were as follows: “Next, a series of videos will be shown in which people with intellectual disability are talking about an event related to a bus accident. Some of the statements were given by individuals who experienced that event the others were given by individuals who, although they were not there, were told about the event, and they have given their statement with the intention of making us believe they were there. The task is to decide who is telling the truth and who is lying to us. As you are assessing each statement, bear in mind that the interviewees are all people with intellectual disability, so their way of telling things may be special.” The twenty-nine videos were shown in random order to prevent a learning effect from impacting the ability to evaluate true and false statements. After each video was shown, the evaluators were asked to categorize the statement as true or false. In the first evaluations, it was observed that the viewing of 29 videos produced saturation and fatigue in the evaluators. To avoid this circumstance leading to random decisions, it was decided to submit to each evaluator a maximum of 15 videos, taking care that finally all the videos were evaluated. In any case, the evaluators were warned that when they felt very tired, they should warn the experimenters. A total of 197 evaluations of the true condition and 256 evaluations of the false condition were collected.

Analysis of Phenomenological Characteristics of the Statements Using CBCA Criteria

The interview video recordings were transcribed to facilitate analysis of the phenomenological characteristics of the statements. Two trained CBCA evaluators each made their own criteria assessment of each statement and then reached an interjudge agreement. To assess the CBCA criteria codings for inter-coder reliability, an agreement index was computed as follows: AI = agreements / (agreements + disagreements). For all the variables, this was greater than the cut-off of .80 (Tversky, 1977), except for “logical structure” and “unstructured production”, where it was .67.

Each criterion was assessed in terms of its absence or presence in the statement, as was originally defined by Steller and Köhnken

(1989). To measure the degree of presence of each criterion, the

evaluators quantified how many times the criterion was present throughout the report. For the criteria of “quantity of details”, the micropropositions that described, as objectively as possible, what happened in the actual event were used, which is a better measure than counting words because it is not influenced by the descriptive style used by participants.

Criterion 13, “attribution of perpetrator’s mental state”, was modified to be “attribution of other’s mental state”. Criterion 19, “details characteristic of the offence”, was modified to be “details characteristic of the event”. Criteria 17 (self-deprecation) and 18 “Pardoning the perpetrator”, were not taken into consideration, because of the nature of the event.

CBCA Characteristics of the Statements

An ANOVA test was conducted to assess the effects of the type of statement on the number of times each CBCA criterion was present in each report. As multiple comparisons were conducted, the significance level was adjusted with a Bonferroni adjustment to .003. Table 2 shows only “quantity of details” was significant in determining truth. The remaining 16 criteria (some of which rarely occurred) produced no significant differences.

Big Data Analysis of Characteristic Features of Statements

Big data techniques aim towards complex data exploration and analysis. High-Dimensional Visualization (HDV) graphs facilitate the visualization of complex data. This technique displays all the data at once, enabling researchers to graphically explore in search of data distribution patterns (for more information see Manzanero, Alemany,

Recio, Vallet, & Aróztegui, 2015 Manzanero, El-Astal, & Aróztegui,

2009 Vallet, Manzanero, Aróztegui, & García-Zurdo, 2017). The graphs

are similar to scatter plots. The different variables corresponding to a subject’s responses on questionnaire items are represented as a point in a high-dimensional space (17 values or dimensions in this study). When there are more than three variables, as in this study, mathematical dimensionality reduction techniques are used to build a 3D graph (Buja et al., 2008 Cox & Cox, 2001). Each point in the hyperspace has a distance to each of the other points. Multidimensional scaling will search 3D points, preserving the distances between points as much as possible (Barton & Valdés, 2008). Sammon’s error (Barton

& Valdés, 2008) is used to calculate the 3D transformation error.

3D points are represented using Virtual Reality Modelling Language (VRML). VRML files allow graphical rotation and exploration to facilitate graphical data analysis. 3D graphs permit visual exploration of the data in search of its distribution patterns.

Figure 1 represents all criteria, regardless of whether their

the dimensionality reduction through multidimensional scaling

(Buja et al., 2008) was very good, with a small Sammon’s error of

.03. The dotted line graphically dividing true statements from false statements shows correct classification of 81.25 percent (simulated statements were classified as being true 29.42%).

A possible explanation for several of the CBCA criteria not discriminating could stem from the variability among participants. As can be seen in Figure 1, the cloud of dots that graphically represents each type of statement is very dispersed and overlapped.

Figure 1. HDV Graph of Content Criteria in True (Light) and False (Dark) Statements, Including all CBCA Criteria.

Note. Sammon´s error = .030 correct classification = 81.25%.

Intuitive Credibility Assessment

Considering the 197 evaluations of the true- and the 256 evaluations of the false videos, discriminability accuracy (hits, false alarms, omissions, and correct rejections), discriminability index (d’), and response criterion (c) as specified by Signal Detection Theory

(MacMillan & Kaplan, 1985 Tanner & Swets, 1954) were measured.

Analysis of the credibility assessments based on lay participants’ natural ability found above chance accuracy for the discriminability index (d’) was .626 (SD = .121), Zd = 5.159, p < .05.

The response criterion (c) reached a score of .086 (SD = .061), Zc = 1.412, p = ns The subjects had a neutral response criterion (scores equal to 0 indicate a neutral criterion, greater than 0 a conservative criterion, and less than 0 a liberal criterion). The proportion of statements correctly classified was 61.81 percent (see Table 3), with 65.48% of false statements being correctly assessed and 58.98% of the true ones.

Table 3. Intuitive Responses for Each Type of Statement

Assessment False CR: 129 (65.48%) O: 105 (41.02%) True FA: 68 (34.52%) H: 151 (58.98%)

Note. CR = correct rejection O = omission FA = false alarm H = hit.

Depending on the number of times a story was considered true or false by the intuitive judges, the probability of “truthfulness” was established (number of times considered true / number of evaluations made for that testimony). The average probability of truthfulness assigned to the false testimonies was 36.37 (SD = 31.64), while that assigned to the true ones was 64.00 (SD = 23.93), F(1, 28) = 6.750, p < .05, η2 = .200.

The levels of disabilities of the persons with ID could be one of the indicators on which the evaluators based their intuitive assessments. However, no significant effects were found when participants’ IQ was analysed based on how their statements had been classified, considering the four possible types of response (H, FA, O, and CR), F(3, 26) = 0.498, p = ns, η2 = .056. As can be seen in Table 4, IQ means were

Table 4. IQ Means and Standard Deviations of the Subjects according to the Type of Response Issued by the Evaluators

Correct rejections 58.09 9.74

Relationship between CBCA Criteria and Intuitive Credibility Assessment

The Pearson correlation (bilateral) between the degree of presence of each CBCA criteria in the testimonies and the probability of truthfulness indicates that the evaluators’ natural ability may have been mediated by the following criteria: “structured production”, Table 2. Means, Standard Deviations, and ANOVA Values for Each Dependent Variable

N = 17 True StatementN = 15

Mean SD Mean SD F(1, 30) p η2

Logical structure 5.67 2.74 6.86 2.32 1.726 .199 .054

Unstructured production 6.11 2.54 5.46 2.82 0.470 .498 .015

Quantity of details * 7.35 3.60 13.93 4.74 19.800 .000 .398

Contextual embedding 2.52 1.06 3.93 1.90 6.812 .014 .185

Interactions 1.23 1.98 1.53 2.26 0.158 .694 .005

Conversations 0.41 0.50 1.40 1.59 5.878 .022 .164

Unexpected complications 0.47 0.79 0.40 0.50 0.086 .771 .003

Unusual details 0.58 0.79 0.80 0.86 0.523 .475 .017

Superfluous details 0.00 0.00 0.40 0.82 3.984 .055 .117

Details misunderstood 0.23 0.56 0.00 0.00 2.616 .116 .080

External associations 0.05 0.24 0.13 0.35 0.496 .487 .016

Subjective mental state 0.88 0.99 1.00 1.25 0.088 .769 .003

Other’s mental state 1.47 1.28 1.13 1.50 0.469 .499 .015

Corrections 0.17 0.39 0.13 0.35 0.106 .747 .004

Lack of memory 2.38 3.47 2.16 3.08 0.034 .855 .001

Doubts 0.29 0.58 0.50 0.62 0.919 .345 .030

Characteristic details 1.88 1.45 2.13 1.18 0.281 .600 .009

r(29) = .546, p < .01 “quantity of details”, r(29) = .618, p < .01 “unexpected complications”, r(29) = .526, p < .01 and “characteristic details”, r(29) = .437, p < .05. No significant correlations were found for the remaining 13 criteria (see Table 5). The greater presence of these criteria would imply greater intuitive truthfulness.

Table 5. Pearson Correlations between Content Criteria and Intuitive Assessments of “True”

Logical structure** .546 .002

Unstructured production -.346 .066

Quantity of details** .618 .000

Contextual embedding .238 .214

Unexpected complications** .526 .003

Superfluous details .150 .436

Details misunderstood -.149 .441

External associations .166 .389

Subjective mental state .014 .944

Other’s mental state .098 .614

Characteristic details* .437 .018

*p < .05 (bilateral) **p < .01 (bilateral).

In line with many other studies (not involving truth tellers/liars with ID), the lay participants could not discriminate between false and true stories at a level to be considered useful in a forensic context

(Rassin, 1999), this being one of the reasons why CBCA was developed.

The CBCA technique did indeed discriminate at a better level. However, of the 19 criteria, only one (“quantity of details”) was found significant. This criterion, which is present in some lies, also deemed “richness in detail”, has also been identified as potential biases which may lead to incorrect veracity judgements (Nahari & Vrij, 2015). “Quantity of details” was found in the present study to be significant for people who have ID, even though when truly narrating an event, they tend to give fewer details than the general population (Dent, 1986 Kebbell &

Wagstaff, 1997 Perlman, Ericson, Esses, & Isaacs, 1994).

ID is a component of certain syndromes that have associated deficits in language development and articulation. This might explain why several of the CBCA criteria were rarely present in the current study. In Down’s Syndrome, for example—the most common genetic syndrome with an ID component—language disorders are one of the effects. In spontaneous conversation, the speech of people with ID is less intelligible, and they have more difficulty with grammatical structuring (Rice, Warren, & Betz, 2005)—in fact, their problems with sentence structuring are similar to those of individuals diagnosed with language development disorder (Laws & Bishop, 2003).

Thus, if the criteria that help us to determine the true statements of people with ID indeed is “quantity of details”, what could happen if their true accounts are compared with true accounts from the general population? For those with ID who have reduced vocabulary, semantic, and autobiographical memory deficits (rendering them unable to detail the event), we could run the risk that such people will suffer an erroneous judgment of their credibility, and thus, revictimisation could result.

However, since the natural ability evaluators were capable of discriminating between true and false statements at only 12% above-chance accuracy, a procedure that achieves better accuracy is needed.

If we were to extrapolate such natural ability data to a law enforcement setting, for example, we could predict that the testimonies of people with ID would be correctly assessed in only 60 percent of cases, resulting in many true accounts not being believed. This percentage is not far from what the police (and others) usually reach when judging the statements of people with standard development (Manzanero,

To analyse what the possible basis is for intuitively assessing the testimonies of people with ID—which, in turn, is going to determine the credibility assessments granted in forensic and legal settings—we correlated the probability of a “true” assessment with the IQ and the CBCA content criteria. As in the study by Henry et al. (2011), the results showed that IQ did not account for the lay evaluators’ decisions. In relation to the different CBCA criteria, only four criteria appear to mediate intuitive truthfulness (structured production, quantity of details, unexpected complications, and characteristic details).

On the other hand, big data analysis reached a better classification score. It must be taken into account that, surprisingly, these results were obtained after considering all CBCA variables, not only the ones yielding significant differences, although, initially, it was expected that the variable showing significant differences should lead to a better classification in comparison with the rest. Because that was not the case, it seems that useful information is held by those other variables not showing significant differences and the big data technique is able to profit from it, providing better classification quality. This approach could maybe allow to find, in a near future, an improved way of distinguishing true and false statements.

The authors of this article declare no conflict of interest.

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Introduction

A hallmark of human cognition is its flexibility, i.e., the ability to redirect goal behavior to meet changing demands [1]. Traditionally, cognitive flexibility is measured using task-switching, set-shifting, and reversal learning tasks. In all of these tasks, participants learn an initial response pattern, rule, or strategy that must then be adapted when the contingencies or task requirements are abruptly changed. Typically, contingency/requirement changes are not cued, so participants must learn that a change has occurred through feedback on obtained outcomes. High anxiety individuals display less flexible performance on these tasks by continuing to rely on the acquired response even after learning it is no longer relevant [2–6]. Poor performance in high anxiety individuals is frequently attributed to differences in attentional control, particularly greater interference from the irrelevant response [7].

The relative cognitive inflexibility demonstrated by people with high anxiety can have important consequences for behavior when faced with rapidly changing environments. There are many situations in everyday life where learned information must be inhibited because it is no longer relevant in the decision environment: e.g., preferred commuting routes can be blocked by new construction, and food items in the grocery store can be relocated to different shelves/aisles. There are also situations that require overcoming a preexisting rule-of-thumb or preference that biases decision making. For example, people tend to be risk averse even in situations when taking a risk could result in a better outcome: paying a costly insurance premium for a low probability event or opting out of an experimental health procedure with a strong success rate. An important limitation of existing cognitive flexibility research is that it has almost entirely focused on flexibility when overcoming recently acquired/learned information. However, the ability to flexibly overcome a preexisting bias (like risk-aversion) may be particularly important for high anxiety individuals because research suggests they are more vulnerable to biases than low anxiety individuals [8–9].

It is important to note that people are not risk avoidant in all contexts. Rather there is a strong tendency to be risk averse when choices are framed in terms of gains and to be risk seeking when choices are framed in terms of losses. This framing bias was documented in the classic Asian Disease Problem [10]: when disease outbreak intervention programs were framed in terms of lives saved (i.e., 200 people will be saved OR 1/3 probability 600 people will be saved, 2/3 probability 0 people will be saved) participants preferred the sure option over the gamble option, but when the same programs were framed in terms of lives lost participants preferred the gamble option over the sure option. In business, health, and social domains it is well documented that choice framing can produce risk preference reversals that detrimentally influence decisions [11], and high trait anxiety individuals have been shown to be more vulnerable to framing bias than low anxiety individuals [12–13]. While there is substantial research on the impact of such preexisting biases on decision making, only one study has examined whether framing bias can be flexibly overcome using feedback on obtained outcomes [14], and no research has assessed whether trait anxiety can influence the reduction of bias.

Previous research [14] used a task that combined a framing manipulation with a gambling task to evaluate whether bias could be overcome by learning through outcome feedback. In this task a sure option of either $50 or -$50 was presented along with an ambiguous gamble option. One gamble option had average outcomes greater than the sure gain of $50, and the other gamble option had average outcomes worse than the sure loss of $50. Thus, unlike typical risky choice framing tasks that produce shifts in gamble preference, each choice trial had a normatively correct choice that was sometimes inconsistent with framing bias. For example, when given a choice between the sure gain and the “good” gamble option, framing bias drives selection of the sure gain, but it is more advantageous on average to select the gamble option. It was found that participants developed accurate knowledge of the gains and losses provided by the gamble options but continued to make frame-biased choices, even when the bias led to a normatively incorrect choice [14]. It is reasonable to expect that high trait anxiety will exacerbate the problems with overcoming bias experienced by participants in [14]. High anxiety could increase initial vulnerability to framing bias (as in past research, [12–13]), and anxiety-related changes in attentional control could make it more difficult for high anxiety individuals to adapt to change and overcome their preexisting bias.


DISCUSSION

The results of this experiment show that LTMs enhance attentional guidance during a perceptual discrimination task and influence neural signatures of target selection. Spatial expectations from LTM conferred behavioral benefits, as revealed by increased perceptual sensitivity and decreased RTs to targets appearing in remembered versus non-remembered locations. The d′ sensitivity index clearly shows that LTMs can influence perceptual analysis of the stimulus, thus confirming that top–down signals from LTM do more than change the response criterion through response biases. These findings replicate and extend previous results by Summerfield et al. (2006, 2011) by showing that predictions based on prior knowledge acquired from experience facilitate perceptual decisions about the presence of relevant objects when they are embedded within their natural scene contexts. The facilitation of RTs suggests that this perceptual benefit does not come at any speed cost. Instead, memory also speeds up responses to identify the target, leading to better perceptual discriminations within shorter latencies.

Target-related ERPs show that LTM can enhance neural processes related to target selection, as reflected by modulations of the N2pc component. This memory-driven modulation of target processing reveals a close and rapid interaction between memory and attention systems in the brain. We were able to identify an enhanced negative voltage over contralateral (versus ipsilateral) posterior electrodes with a similar time course as the N2pc. Importantly, memory cueing within complex scenes resulted in an interesting and unexpected finding: LTM-based spatial contextual memory cues reduced the magnitude of the N2pc in the valid condition.

Interestingly, the modulation of the N2pc by LTM cues differed qualitatively from what has been observed with spatial cueing of attention in typical visual search tasks. LTM for the target location in our task clearly and strongly attenuated the N2pc. In contrast, visual spatial cues in search tasks and in other types of perceptual attention tasks do not influence the N2pc (Brignani et al., 2010 Schankin & Schubö, 2010 Seiss et al., 2009 Kiss et al., 2008 Leblanc et al., 2008). Results from previous visual spatial cueing tasks have been interpreted as suggesting that the N2pc does not reflect the spatial guidance of attention (but see Woodman & Luck, 1999, 2003) or a selection process that is influenced by visual spatial attention. Instead, the N2pc appears to reflect a separate set of mechanisms related to feature-based selection processes guided by the identity of the target (Brignani et al., 2010 Kuo et al., 2009). The fact that we only observed the N2pc when the target stimulus was present in the scene reinforces the notion that the N2pc is linked to target-selection processes.

Furthermore, our results clearly point to possible differences in how memory cues and perceptual cues come to influence target selection processes. One possible explanation is that LTM for a specific target location within a scene primed the identification of the target attributes, diminishing the amount of visual analysis and suppression of distracting information required for effective target selection and identification. The cue in our task could activate specific memory traces for target/context configurations, facilitating the target selection and thus reducing the amount of resources required for the suppression of distracters. This interpretation is in line with the findings of Luck and Hillyard (1994), who reported that the N2pc is diminished when distracters are irrelevant or removed. Alternatively, these differences may stem from differences in modulations of neural signatures of target selection when targets are embedded in natural complex backgrounds versus simple visual backgrounds. Further experiments directly comparing ERPs produced when memory cues and perceptual cues guide attentional orienting within complex scenes are needed to settle this question.

Intriguingly, the attenuation of the N2pc by LTM also differs from what has been observed in previous experiments using ERPs in the contextual cueing paradigm. These have consistently reported a larger N2pc for targets appearing in repeated as opposed to novel displays (Schankin et al., 2011 Schankin & Schubö, 2009, 2010 Johnson et al., 2007). The discrepancy could result simply from the timings during which the selection processes can start to operate. It may be that, in general, appearance of a target within a learned context enhances the selection processes indexed by the N2pc, but when the context is preactivated some of the selection processes can proceed ahead of time, in anticipation of the target appearance. In our experiment, the participant is pre-exposed to the scene triggering the contextual memory for the target location. It is possible therefore to process the context–target association and engage neural processes relating to prioritising the target features and/or inhibiting the irrelevant features in the contextual background. In the contextual cueing paradigm, context and target occur only simultaneously, and all the work for prioritising the target features and suppressing distractor features needs to be carried out on-line. Evidence that selection has a head start in our memory-orienting task comes from analysis of the lateralised alpha-band activity, which becomes desynchronized over posterior contralateral electrodes in anticipation of the probe scenes (Stokes et al., 2012 see also Summerfield et al., 2011). Notably, we also found two nonlateralized modulations in the ERPs elicited by valid as compared with neutral cues, which may reflect non-spatially specific retrieval of the associations between target and context (see also Summerfield et al., 2011).

Alternatively, the discrepancy between the attenuation of the N2pc found here and the enhancement of the N2pc in previous contextual cueing tasks could be explained by the difference in the types of memory traces involved. In the classical contextual cueing paradigm, it is assumed that the memories guiding attention are implicit in nature (Chun & Jiang, 1998, 2003). In the current experiment, the memories for target–context associations were formed by explicit instruction, and the contexts were rich in visual detail, thus making them more available for explicit recall. When tested explicitly, participants were accurate at retrieving the learned locations of the keys, and reported high confidence levels. This pattern of results also occurred for scenes containing no keys within the orienting task, showing that performance on the memory retrieval task was not dependent on re-exposure to the location of keys during the immediately preceding task). There is no way, of course, to rule out the formation and availability of implicit memory traces in our task, but we can be confident that explicit memory traces were also available, and these may have played a role. This difference in the types of memory sources available may account for the difference in attentional guidance strategies, target selection, and/or distracter suppression processes engaged by the tasks. This interpretation would be in line with proposals by Moscovitch and colleagues, who suggest that explicit, episodic memories may play a unique top–down role in regulating and facilitating a number of cognitive functions, such as priming (Sheldon & Moscovitch, 2010) and problem solving (Sheldon, McAndrews, & Moscovitch, 2011).

Additionally, it has been argued that in arbitrary target–distracter arrays, the local context around the target is sufficient to elicit a contextual cueing effect (Olson & Chun, 2002), whereas in naturalistic scenes, global information is crucial for guiding attention (Brockmole, Castelhano, & Henderson, 2006). It is possible that, in the current experiment, the contextual effect is guided by a more holistic scene representation with a target location associated within it, as opposed to spatial configurations with arbitrary target–distracter relationships. Thus, the mechanisms at play in contextual cueing versus in our experiment may differ for multiple different reasons, and these are not mutually exclusive.

We also identified a later, spatially specific effect characterized by a lateralized posterior positivity contralateral to the target location, labeled here as PCP, which was not found to be modulated by memory cues. In reviewing previous ERP studies on PCP-like components, we found a recent description by Hilimire and colleagues (Hilimire, Mounts, Parks, & Corballis, 2010) of a positive posterior contralateral component, called Ptc (approximately 290–340 msec poststimulus), proposed to index additional processing necessary to individuate the target after it is identified under conditions of high competition between stimuli in an array. This finding was not predicted by our initial hypothesis and needs to be interpreted with caution however, it is plausible to propose that similar later target-related processes are engaged when discrimination of relevant objects within crowded scenes is required.

Earlier visual potentials P1 and/or N1 are also typically modulated by visuospatial attention (Hillyard & Anllo-Vento, 1998). In a previous memory-based orienting task using transiently appearing targets, Summerfield and colleagues recently reported modulation of these early visual potentials. The amplitude of the P1 was enhanced by memory cues, whereas the N1 potential showed a more distinctive pattern of modulation—with contralateral attenuation and ipsilateral enhancement as well as latency reduction (Summerfield et al., 2011). However, the effects on P1 and N1 were not significant in our current experiment. This may simply have reflected the challenging conditions for measuring these potentials under our current task parameters. Typically, visual–spatial tasks use targets that appear transiently onto blank or very simple backgrounds. In our task, however, the target stimuli were intrinsically bound to an associated complex, cluttered scene, which it makes difficult to measure the influence of spatial biases on visual evoked potentials.

The results of the topographical analysis are preliminary but raise the possibility of different neural sources or functional networks when target selection in our environment is facilitated by LTM cues. Further experimentation using alternative methods with higher spatial resolution may help characterize the brain areas involved in guiding target selection during memory-guided attention.

In summary, this study provides evidence about the role of explicit long-term contextual memories in optimizing visual search and in modulating the ongoing processing of incoming information by biasing neural activity related to target selection. Furthermore, the data imply that the spatial or contextual information from LTM facilitates target selection through a different top–down mechanism than that engaged by attention-directing perceptual cues. Whereas perceptual cues do not influence feature-based selection of targets, memory cues may facilitate identification of target features and substantially diminish the neural resources involved in this process. Furthermore, search for objects in cluttered environments based on explicit memories of specific target–context associations results in a different neural modulation of target identification than that observed when unconsciously memorized contextual relations guide visual search. Further experimentation aimed at comparing the neural mechanisms of top–down biases triggered by memory cues versus perceptual cues will be especially informative.


Results

Cued Recollection: Placebo vs Encoding

Raw cued recollection performance was directly related to valence as indicated by main effects on hits (F(2,76)=14.109, p<0.001, =0.271) and accuracy (F(2,76)=14.259, p<0.001, =0.273). These main effects were due to the typical advantage for negative stimuli (Figure 1a and c). There was a trending main effect of valence on false alarms (F(2,76)=2.488, p=0.090, =0.061) due to more false alarms for positive stimuli (Figure 1b). No other main effects or interactions were found (all F’s<2.000, all p’s>0.200).

Raw performance on the cued recollection task. (a) Mean hit rates, (b) false alarm rates, and (c) accuracy (hit rates—false alarm rates). Error bars are SEM.

The distributions of DPSD-based recollection and familiarity estimates from the bootstrapping procedure were all normal (Figure 2). Negative (95% CI: (0.004, 0.297), p=0.022) and positive (95% CI: (−0.008, 0.207), p=0.034) recollection estimates in the Encoding group were reduced compared with the Placebo group, though the confidence interval of the difference distribution for positive recollection estimates implied a less reliable effect. The Encoding and Placebo groups did not differ on neutral recollection estimates or familiarity estimates (all p’s>0.100), though there was trend for greater positive familiarity estimates in the Encoding group (95% CI: (−0.085, 0.465), p=0.078).

Distributions of (a) negative, (b) neutral, and (c) positive dual process signal detection-based recollection estimates generated from the bootstrapping procedure on the cued recollection confidence data.

Recognition: Placebo vs Encoding

The ANOVAs on hits, false alarms, and accuracy from the picture recognition test were not significant (all F’s<1 and p’s>0.250), except for a trending effect of valence on false alarms (F(2,76)=2.286, p=0.109, =0.057) due to greater false alarms to positive pictures.

The ANOVA on recollection estimates from the IRK procedure revealed a main effect of valence (F(2,76)=3.962, p=0.007, =0.122) and a marginal effect of group (F(1,38)=5.289, p=0.054, =0.094) with no interaction (F(2,76)=1.559, p=0.217). The main effect of valence was due to smaller recollection estimates for positive pictures, and the main effect of group was due to attenuated recollection in the Encoding group (Figure 3). Although the interaction was not significant, exploratory contrasts found both negative (t(38)=2.154, p=0.038, d=0.681) and positive (t(38)=2.164, p=0.037, d=0.684) recollection estimates to be lower in the Encoding group compared with the Placebo group with no reliable difference between neutral estimates (t(38)=1.079, p=0.287, d=0.341), consistent with the DPSD-based recollection estimates. There were no main effects or interactions on familiarity estimates (all F’s<1 and all p’s>0.250).

Mean independence remember/know estimates of (a) recollection and (b) familiarity from the recognition task. Error bars are SEM.

Cued Recollection: Placebo vs Retrieval

Emotional valence strongly modulated hits (F(2,76)=18.143, p<0.001, =0.323) and accuracy (F(2,76)=16.417, p<0.001, =0.302) such that negative pictures showed a memory advantage. Valence modulated false alarms (F(2,76)=4.121, p=0.020, =0.098) due to more false alarms for positive stimuli. All other main effects and interactions were not statistically significant (all F’s<2.000, p>0.150).

Although the difference between negative recollection estimates in the Placebo and Retrieval groups was not significant (95% CI: (−0.075, 0.198), p=0.193), by comparison, the difference between the Encoding and Retrieval groups was also not significant (95% CI: (−0.052, 0.224), p=0.101). This can be seen in Figure 2a, which shows the negative recollection distribution of the Retrieval group lying in between those of the Encoding and Placebo groups. The distribution for positive recollection estimates of the Retrieval group was also in between the Placebo and Encoding groups with no difference between either of them (Figure 2c Retrieval vs Placebo: 95% CI: (−0.077, 0.157), p=0.235 Retrieval vs Encoding: 95% CI: (−0.124, 0.147), p=0.168). There were no differences between the Retrieval and Placebo groups for neutral recollection estimates and familiarity estimates (all p’s>0.200), though there was a trend for reduced neutral familiarity estimates in the Retrieval group (95% CI: (−0.057, 0.421), p=0.066).

Recognition: Placebo vs Retrieval

The ANOVA on hits comparing Placebo and Retrieval groups did not reveal any main effects or interactions (all F’s<1, all p’s>0.250). However, the ANOVA on false alarms revealed a main effect of valence (F(2,76)=4.247, p=0.018 =0.101), again explained by greater false alarms for positive pictures. Although a main effect of group did not reach significance (F(1,38)=2.389, p=0.130, =0.059), exploratory contrasts found that there was a trend for the Retrieval group to false alarm more to positive stimuli than the Placebo group (t(38)=1.816, p=0.077, d=0.075), consistent with the high confidence false alarms on the cued recollection test (SOM), and this was not found for other valences (all t’s<1.500, all p’s>0.200). Finally, there was a trending main effect of valence on accuracy (F(2,76)=2.695, p=0.074, =0.066) explained by decreased accuracy for positive pictures, owing to the increased false alarms. No other main effects and interactions were significant (all F’s<1.500, all p’s>0.200).

There was a trending main effect of valence on recollection estimates (F(2,76)=2.651, p=0.077, =0.065) with positive recollection estimates being the smallest but no effect of group or interaction (all F’s<2.000, all p’s>0.150). Although there were no between-group differences, exploratory contrasts were conducted to determine consistent trends between the cued recollection and picture recognition tests, as were found among the Encoding group’s analyses. These analyses found that both negative (t(19)=2.146, p=0.045, d=0.480) and positive (t(19)=2.393, p=0.027, d=0.535) recollection estimates were reduced compared to neutral estimates in the Retrieval group, but neither of these effects was found in the Placebo group (Figure 3a neutral vs negative: t(38)=0.566, p=0.578 neutral vs positive: t(19)=1.163, p=0.259). There were no main effects or interactions on the familiarity estimates (all F’s<1 and all p’s>0.250).


Discussion

Participants made recognition memory judgments in the fMRI scanner for previously studied scenes and novel scenes. We first identified brain areas that distinguished true memory (i.e., hits) and false memory (i.e., false alarms) without taking confidence ratings into account. This analysis identified the left hippocampus, 15 neocortical regions, and the caudate nucleus. Activity in all of these regions was linearly related to confidence levels. Thus, these regions likely distinguished high and low confidence rather than true and false memories per se.

When we equated the confidence ratings associated with true and false memories and repeated the same analysis, only three regions (all in parietal cortex) distinguished true and false memories. Two of these regions minimally overlapped with regions identified when confidence was not equated, and one region was new. In addition, there was one sub-threshold MTL cluster (Table 1 Fig. 2). Thus, brain activity can distinguish true memory and false memory even when the two kinds of memory judgments are made with similar levels of confidence.

Last, we identified regions where activity was similar for true memory and false memory (and also higher than for correct rejections). One cortical region (and no MTL regions) exhibited similar activity for true memory and false memory before confidence was taken into account. When confidence was equated for hits and false alarms (and correct rejections), two regions in posterior parietal cortex exhibited similar activity for true and false memories (Table 2 Fig. 3). These included one new region and one region that partially overlapped with the region that had been identified before equating for confidence. Accordingly, brain activity in these two regions was related to the judgment that the scene had been presented previously, regardless whether the judgment was correct or incorrect.

The hippocampus and true and false memories

Hippocampal activity differentiated true and false memories when confidence ratings were different for the two conditions. This finding is consistent with the results of the only other study that compared hits and false alarms and where targets and foils were unrelated (Kirwan et al. 2009). In that study, hits were associated with higher confidence than false alarms, and the hippocampus exhibited higher activity for hits than for false alarms.

Other findings also indicate that hippocampal activity can differentiate true and false memories when confidence ratings are not controlled, so long as accuracy is relatively high as in our study [79.8% correct accuracy = hit rate/(hit rate + false alarm rate)]. For example, Gutchess and Schacter (2011) and Kirwan et al. (2009) obtained a hippocampal finding when all hits were contrasted with all false alarms, and memory scores were high (accuracy = 70.7% and 75.4% correct, respectively). In contrast, studies that did not obtain hippocampal findings had lower accuracy rates (Schacter et al. 1996: 54.0% correct Schacter et al. 1997: 61.1% correct Cabeza et al. 2001: 52.4% correct Slotnick and Schacter 2004: 53.4% correct Garoff-Eaton et al. 2007: 59.3% correct Iidaka et al. 2012: 54.1% correct).

The relationship between accuracy and the finding that hippocampal activity can differentiate true and false memory is less clear in studies where true or false memory refers to accurate or inaccurate retrieval of the context in which material was studied (hippocampal findings: Cansino et al. 2002: 69.8% correct where chance = 25% Dobbins et al. 2002: hit rate = 80%, but false alarm rate was not available Weis et al. 2004: 52.1% correct where chance = 25% no hippocampal findings: Okado and Stark 2003: 78.8% correct Kahn et al. 2004: 65.3% correct Stark et al. 2010: 63.5% correct).

When confidence ratings were equated (and accuracy was high, 74.9% correct), hippocampal activity marginally distinguished true and false memories (i.e., voxel-wise probability P < 0.05 uncorrected). In contrast, no MTL regions distinguished true and false memories in the case of memory judgments made with low confidence (accuracy = 45.7% correct). These findings for the hippocampus are consistent with results from two other studies. In one study (Kim and Cabeza 2007), the hippocampus distinguished high-confidence true and false memories (Sure-Hits and Sure-False Alarms accuracy = 71.0% correct) but not low-confidence true and false memories (Unsure-Hits and Unsure-False Alarms accuracy = 43.5% correct). In the second study (Dennis et al. 2012), hippocampal activity distinguished true and false recollection (using a Remember, Know, New Paradigm accuracy = 70.6% correct). Recollection is typically associated with high confidence and high accuracy (Wixted 2007).

The neocortex and true and false memories

The neocortex distinguished true and false memories when confidence ratings associated with the two conditions were similar. Specifically, parietal cortex (BA 7/40) exhibited higher activity for hits than for false alarms. The finding is in agreement with earlier work demonstrating a role for this region in the retrieval of contextual information (Curran 2000), a circumstance more likely to occur for true memory than for false memory. Kim and Cabeza (2007) also found regions in the parietal cortex that distinguished true and false memories when confidence was equated, but these regions exhibited higher activity for false memory than for true memory.

Other studies that did not equate confidence found that activity in posterolateral parietal cortex (BA 7/39/40) was higher for true than for false memory (Cabeza et al. 2001 Slotnick and Schacter 2004 Stark et al. 2010). We also detected clusters in the posterolateral parietal cortex for the contrast of hits and false alarms when confidence levels were not equated. Activity in these regions was linearly related to confidence levels. Accordingly, activity in posterolateral parietal cortex is likely related to confidence in the perceived oldness of the stimuli (also see Wheeler and Buckner 2003, 2004) and not to true and false memories per se. Similarly, the activity we observed in prefrontal cortex (when confidence was not equated) is likely related to confidence and not to true and false memories. This activity was higher for false alarms than for hits (also see Schacter and Slotnick 2004) and was negatively related to confidence ratings.

Last, one region in posterolateral parietal cortex responded similarly for true and false memories before confidence was equated. In contrast, earlier studies that tested for regions that responded similarly for true and false memories (Kahn et al. 2004 Slotnick and Schacter 2004 Garoff-Eaton et al. 2006, 2007 Gutchess and Schacter 2011) found a number of brain regions in frontal, temporal, parietal, and occipital cortex. Note that in these earlier studies, the targets and foils were conceptually or perceptually related. This circumstance may increase the number of regions that respond similarly for true and false memories.


Watch the video: E-Prime: Sending and Receiving Triggers - Part 2 (August 2022).