False Recognition Research Articles

Emotional false memories: the impact of response bias under speeded retrieval conditions

ABSTRACT Emotional false memory findings using the DRM paradigm have been marked by higher false alarms to negatively arousing compared to neutral critical lure items. Explanations for these findings have mainly focused on false memory-based accounts. However, here we address the question of whether a response bias for emotional stimuli can, at least in part, explain this phenomenon. Participants viewed both neutral and negative arousing DRM lists and completed a recognition test in speeded or self-paced conditions. Speeded test reduces the opportunity to adjust response bias. Analysis showed no significant difference in false recognition across critical lure types for the speeded condition, but false recognition was higher for negative compared to neutral critical lures in the self-paced condition. We argue that when retrieval does not allow for shifts in response criteria, false alarms to negative emotional critical lures appear more similar to neutral equivalents. The discussion explores memory-based and criterion-shift explanations for the enhanced emotional false memory finding.

Neural basis of false recognition in Alzheimer's disease and dementia with lewy bodies.

In Alzheimer's disease (AD), reports on the association between false recognition and brain structure have been inconsistent. In dementia with Lewy bodies (DLB), no such association has been reported. This study aimed to identify brain regions associated with false recognition in AD and DLB by analyzing regional gray matter volume (rGMV). We included 184 patients with AD and 60 patients with DLB. The number of false recognitions was assessed using the Alzheimer's Disease Assessment Scale' word recognition task. Brain regions associated with the number of false recognitions were examined by voxel-based morphometry analysis. The number of false recognitions significantly negatively correlated with rGMV in the bilateral hippocampus, left parahippocampal gyrus, bilateral amygdala, and bilateral entorhinal cortex in patients with AD (p < 0.05, family-wise error [FEW] corrected) and in the bilateral hippocampus, left parahippocampal gyrus, right inferior frontal gyrus, right middle frontal gyrus, right basal forebrain, right insula, left medial and lateral orbital gyri, and left fusiform in those with DLB (p < 0.05, FWE corrected). Bilateral hippocampus and left parahippocampal gyrus were associated with false recognition in both diseases. However, we found there were regions where the association between false recognition and rGMV differed from disease to disease.

Self and mother referential processing in phonological false memory

ABSTRACT The current study used phonological DRM and self-reference paradigms to examine whether phonological false memory was affected by self and mother referential processing. In Experiment 1 and 2, words in the Deese–Roediger–McDermott lists were presented to participants with their own name, their mother’s name or “Lu Xun”. Words were presented with their own name and “Lu Xun” in Experiment 3 to compare the effect of self and other referential processing specifically. The results revealed that true recognition rates were not different between self-reference and mother-reference conditions and both were higher than the other-reference condition; there were no significant differences in false recognition rates among the self-reference, mother-reference and other-reference conditions. The findings are discussed in terms of theories of the self and mother reference effects and phonological false memory.

The differential illusion memory for high-associated abstract concepts (DIM-HA) effect.

A vast body of evidence has shown that concrete concepts are processed faster and more accurately than abstract concepts in a variety of cognitive tasks. This phenomenon is widely known as the concreteness effect, and explanations for its occurrence seem to reflect differences in processing and organization for both types of representations. While there is considerable evidence to support this concreteness effect, the nature of these differences is still controversial. In developing an explanation, we have proposed a relatively different approach from a false memory perspective using the Deese-Roediger-McDermott paradigm. To explore the implications of the association in creating false memories, we explore behavioral and electrophysiologically the false memory effect, where targets were manipulated according to their association strength and their concreteness. Results showed that false recognition rates differed significantly between concrete and abstract critical words when they were associated strongly with their respective lists, which led to a higher proportion of abstract false alarms both in behavioral and electrophysiological experiments. The principal outcome, which was called the DIM-HA effect, was discussed in terms of theories of associative activation and qualitatively different representation.

Deduplication-Aware Healthcare Data Distribution in IoMT

As medical sensors undergo expeditious advancements, there is rising interest in the realm of healthcare applications within the Internet of Medical Things (IoMT) because of its broad applicability in monitoring the health of patients. IoMT proves beneficial in monitoring, disease diagnosis, and better treatment recommendations. This emerging technology aggregates real-time patient health data from sensors deployed on their bodies. This data collection mechanism consumes excessive power due to the transmission of data of similar types. It necessitates a deduplication mechanism, but this is complicated by the variable sizes of the data chunks, which may be either very small or larger in size. This reduces the likelihood of efficient chunking and, hence, deduplication. In this study, a deduplication-based data aggregation scheme was presented. It includes a Delimiter-Based Incremental Chunking Algorithm (DICA), which recognizes the breakpoint among two frames. The scheme includes static as well as variable-length windows. The proposed algorithm identifies a variable-length chunk using a terminator that optimizes the windows that are variable in size, with a threshold limit for the window size. To validate the scheme, a simulation was performed by utilizing NS-2.35 with the C language in the Ubuntu operating system. The TCL language was employed to set up networks, as well as for messaging purposes. The results demonstrate that the rise in the number of windows of variable size amounts to 62%, 66.7%, 68%, and 72.1% for DSW, RAM, CWCA, and DICA, respectively. The proposed scheme exhibits superior performance in terms of the probability of the false recognition of breakpoints, the static and dynamic sizes of chunks, the average sizes of chunks, the total attained chunks, and energy utilization.

Non-relevant segment recognition via hard example mining under sparsely distributed events

We propose on/offline hard example mining (HEM) techniques to alleviate the degradation of the generalization performance in the sparse distribution of events in non-relevant segment (NRS) recognition and to examine their utility for long-duration surgery. Through on/offline HEM, higher recognition performance can be achieved by extracting hard examples that help train NRS events, for a given training dataset. Furthermore, we provide two performance measurement metrics to quantitatively evaluate NRS recognition in the clinical field. The existing precision and recall-based performance measurement method provides accurate quantitative statistics. However, it is not an efficient evaluation metric in tasks where false positive recognition errors are fatal, such as NRS recognition. We measured the false discovery rate (FDR) and threat score (TS) to provide quantitative values that meet the needs of the clinical setting. Finally, unlike previous studies, the utility of NRS recognition was improved by applying our model to long-duration surgeries, instead of short-length surgical operations such as cholecystectomy. In addition, the proposed training methodology was applied to robotic and laparoscopic surgery datasets to verify that it can be robustly applied to various clinical environments.

Investigating a bias account of emotional false memories using a criterion warning and force choice restrictions at retrieval

ABSTRACT Here, we add to the debate as to whether false recognition of emotional stimuli is more memory-based or more bias-based. Emotional false memory findings using the DRM paradigm have been marked by higher false alarms to negatively arousing compared to neutral critical lure items. Explanation for these findings has mainly focused on false memory-based accounts. However, here we address the question of whether a response bias for emotional stimuli can, at least in part, explain this phenomenon. In Experiment 1, we used a criterion warning, previously shown to increase more conservative responding and reduce false recognition. Experiment 2, we employed a two-alternative-forced choice test, which minimises the role of criterion setting. In both experiments, we compared false alarms to negative and neutral critical lures. We observed a significant decrease in false recognition rates for both negative and neutral critical lures under the conditions of forced choice restriction and criterion warning. However, despite these conditions, negative items, compared to their neutral counterparts, still consistently provoked a higher degree of false recognition. The discussion that follows presents an exploration of both memory-based accounts and criterion-setting explanations for the enhanced emotional false memory finding.

Effect of Particle Swarm Optimization Convolutional Neural Network in An Iris Recognition System

An iris recognition system based on Convolutional Neural Network with Particle Swarm Optimization (CNN-PSO) was developed to improve the identified hitches in the existing systems. Iris images of 150 and 108 persons were acquired from LAUIRIS (Nigeria) and CASIA (China) respectively. The images were resized and cropped after which Hough transform was used for effective localization of the iris region and normalised using Daugman’s rubber sheet model, while an efficient Cumulative Sum-based analysis method was used to extract discriminative features from the normalised iris images after which the iris code was generated. The iris code generated in a vector form was optimised with PSO after which they are fed into Convolutional neural network; the same procedure was engaged during enrolment and authentication to generate the iris template. Euclidean distance was used for decision making on test sample template and stored template. The system was implemented with MATLAB R2013a. The performance of the developed system was evaluated on LAURIS and CASIA, and compared with the existing systems (CNN, BPNN-PSO and BPNN) using False Acceptance Rate (FAR), False Rejection Rate (FRR) and Recognition Rate (RR). CNN-PSO has the highest recognition rate of 98.67% and 97.22% for LAUIRIS and CASIA respectively among the systems which showed an improvement over other three recognition technique. The developed CNN-PSO has not only produced an improved Iris recognition system over the others, with the highest recognition rate for both datasets but it also provides a significant recognition rate of black Iris images despite the limitations identified with black Iris images in separating Iris image from other part of the eyes. The developed

Effect of Particle Swarm Optimization Convolutional Neural Network in An Iris Recognition System

An iris recognition system based on Convolutional Neural Network with Particle Swarm Optimization (CNN-PSO) was developed to improve the identified hitches in the existing systems. Iris images of 150 and 108 persons were acquired from LAUIRIS (Nigeria) and CASIA (China) respectively. The images were resized and cropped after which Hough transform was used for effective localization of the iris region and normalised using Daugman’s rubber sheet model, while an efficient Cumulative Sum-based analysis method was used to extract discriminative features from the normalised iris images after which the iris code was generated. The iris code generated in a vector form was optimised with PSO after which they are fed into Convolutional neural network; the same procedure was engaged during enrolment and authentication to generate the iris template. Euclidean distance was used for decision making on test sample template and stored template. The system was implemented with MATLAB R2013a. The performance of the developed system was evaluated on LAURIS and CASIA, and compared with the existing systems (CNN, BPNN-PSO and BPNN) using False Acceptance Rate (FAR), False Rejection Rate (FRR) and Recognition Rate (RR). CNN-PSO has the highest recognition rate of 98.67% and 97.22% for LAUIRIS and CASIA respectively among the systems which showed an improvement over other three recognition technique. The developed CNN-PSO has not only produced an improved Iris recognition system over the others, with the highest recognition rate for both datasets but it also provides a significant recognition rate of black Iris images despite the limitations identified with black Iris images in separating Iris image from other part of the eyes. The developed technique can be applied to various field of life like security, surveillance systems.

Vulnerability to semantic and phonological interference in normal aging and amnestic mild cognitive impairment (aMCI).

To determine whether the increased vulnerability to semantic interference previously observed in amnestic mild cognitive impairment (aMCI) is specifically associated with semantic material or if it also affects other types of material, suggesting generalized executive and inhibitory impairment. Seventy-two participants divided into two groups (33 aMCI, 39 normal control [NC]) matched for age and education were included. They completed a comprehensive neuropsychological examination, the French version of the Loewenstein Acevedo Scale for Semantic Interference and Learning (LASSI-L; semantic interference test), and a homologous experimental phonological test, the phonological interference and learning test. Independent sample t tests, mixed analysis of variance (ANOVA), and analysis of covariance (ANCOVA) on memory and interference scores were conducted to compare memory and interference in both conditions for both groups. For memory scores, results revealed significant main effects of group (NC > aMCI) and condition (semantic > phonological) and significant interactions (poorer performance in the semantic condition for aMCI). aMCI committed more phonological false recognition errors, were disproportionately more vulnerable to retroactive semantic interference, and showed a higher percentage of intrusion errors associated with proactive semantic interference than NC. To our knowledge, this is the first study to compare vulnerability to interference in aMCI and normal aging with two similarly designed semantic and phonological word list learning tasks. Taken together, our results suggest that aMCI present with broad difficulties in source memory and inhibition, but that impaired deep semantic processing results in additional semantic intrusion errors during proactive interference and impacts their ability to show good recall after an interference list (greater semantic retroactive interference). Results are discussed according to the level-of-processing and activation/monitoring theories. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Auditory illusions and Post-traumatic stress disorder: Sound test in a case-control study

Post-Traumatic Stress Disorder (PTSD) is a prevalent and disabling disorder with a high degree of comorbidity. Clinical studies have focused on hallucinations, which could be associated with the severity of the disorder and treatment resistance. Auditory illusions have received little attention so far, possibly because they are particularly difficult to assess. However, they may impact functioning, and underdiagnosis could impair prognosis. To provide a clearer understanding of PTSD psychopathology, this paper proposes to focus on these auditory illusions.A monocentric case-control study was conducted on 30 subjects with PTSD and 30 controls, based on an original design. False recognitions were estimated during a sound test created with a vocoder. Additionally, differences in emotional valence, dissociation, hyperarousal, and reliving were assessed.The study found that individuals with PTSD experience a higher frequency of auditory illusions compared to healthy controls (65% versus 20%, p < 0.001). Additionally, the emotional valence of these illusions was more negative in individuals with PTSD than in controls. The study also identified a correlation between dissociation symptoms, hyperarousal and reliving with auditory illusions.These findings are in line with neurobiological studies of PTSD, as well as cognitive predictive models, and support the hypothesis of a significant prevalence of auditory illusions in PTSD. Like hallucinations, auditory illusions may be influenced by dissociation. Although these results are preliminary, they suggest a need for further investigation into auditory illusions in PTSD and their effect on prognosis.

The False Recognition Test, a new tool for the assessment of false memories, with normative data from an Italian sample

IntroductionFalse memory can be defined as remembering something that did not happen. To a certain extent it is a normal phenomenon, but its occurrence seems to increase in healthy and pathological aging, possibly providing relevant clues on some clinical conditions in the spectrum of dementia. We adapted a well-established Deed-Roediger-McDermott paradigm, frequently used in experimental contexts, to devise a new neuropsychological assessment tool, the False Recognition Test (FRT), that can investigate classical facets of episodic memory performance (i.e. free recall and recognition), and assess proneness to produce semantically related and non-semantic false memories. Here we describe the FRT and provide normative data and correction grids to consider the possible effects of age, gender, and education on the FRT scores.MethodTwo-hundred and thirty-two Italian healthy individuals (99 male) aged 18–91 years, with different educational levels (from primary to university) underwent the FRT, together with validated tests for cognitive screening and episodic memory assessment and one scale for depression.ResultsMultiple linear regression analysis revealed that age and education significantly influenced performance on FRT. From the derived linear equations, we provide correction grids for the raw scores of the FRT, and equivalent scores estimated using a nonparametric method. Correlational analysis showed significant associations between FRT subscores and cognitive, executive and memory functions, and depression.ConclusionThe FRT may constitute a useful instrument for both clinical and research purposes.

YOLO-MPAM: Efficient real-time neural networks based on multi-channel feature fusion

Object detection is a crucial foundation in the field of autonomous driving. Therefore, accurate and real-time detection of road objects, including vehicles and pedestrians, is essential for the success of autonomous vehicles. Convolutional neural networks represented by YOLOv8 have made significant progress in autonomous driving. However, there are also various problems, such as false recognition, missed detection, and susceptibility to complex weather. Aiming at the problem of low traffic sign detection effect in automatic driving scenes, an improved YOLOv8 automatic driving target detection algorithm is proposed. Firstly, Mosaic data augmentation and Multi-Path Attention Mechanism (MPAM) are introduced based on the YOLOv8 object detection model. The improved model can obtain location information from the feature map in the early stages of recognition, enabling the model to focus more on the regions of interest. Secondly, because the model pays different attention to global and local features, to make the model pay more attention to fine-grained small targets, so a complex bidirectional fusion structure is proposed in the neural network. Finally, we comprehensively consider the ratio of predicted boxes to ground-truth. The CIoU-tune loss function replaces the original IoU loss function, minimizing the height and width discrepancies between predicted and actual bounding boxes. This improvement accelerates the convergence speed of the model, achieving better localization results. After undergoing ablation analysis, the YOLO-MPAM method is compared with other methods on dataset TT100K. The mAP50 value of the improved algorithm is 86.1%, showing a 9.6% improvement compared to YOLOv8n. Additionally, the model achieves higher accuracy in detecting small objects, effectively enhancing the detection performance of the model at the same inference speed.

Effects of emotional ambiguity and emotional intensity on true and false memory.

Whereas the effects of emotional intensity (the perceived strength of an item's valence or arousal) have long been studied in true- and false-memory research, emotional ambiguity (the uncertainty that attaches to perceived emotional intensity) has only been studied recently. Available evidence suggests that emotional ambiguity has reliable effects on true memory that are distinct from those of emotional intensity. However, those findings are mostly restricted to recall, and the effects of emotional ambiguity on false memory remain unexplored. The current study addressed both limitations by measuring the effects of emotional ambiguity and emotional intensity on true and false recognition. In two experiments, we manipulated valence ambiguity and valence intensity (Experiment 1) and arousal ambiguity and arousal intensity (Experiment 2) of Deese/Roediger/McDermott lists. Multiple linear regression analyses were conducted for Experiment 1, Experiment 2, and the combined data of the experiments to separate the effects of emotional ambiguity and emotional intensity. Our results showed that both valence ambiguity and arousal ambiguity improved true recognition, and the effects of valence ambiguity remained robust even when controlling for valence intensity, arousal intensity, and arousal ambiguity. More importantly, for both valence and arousal, there was an interaction between ambiguity and intensity in false memory. Specifically, we found that valence ambiguity increased false recognition with positive valence, while arousal ambiguity amplified the effect of arousal intensity on false recognition. Our results are discussed in the context of the emotional ambiguity hypothesis and fuzzy-trace theory.

Probing the content of affective semantic memory following caregiving-related early adversity.

Cognitive science has demonstrated that we construct knowledge about the world by abstracting patterns from routinely encountered experiences and storing them as semantic memories. This preregistered study tested the hypothesis that caregiving-related early adversities (crEAs) shape affective semantic memories to reflect the content of those adverse interpersonal-affective experiences. We also tested the hypothesis that because affective semantic memories may continue to evolve in response to later-occurring positive experiences, child-perceived attachment security will inform their content. The sample comprised 160 children (ages 6-12 at Visit 1; 87F/73M), 66% of whom experienced crEAs (n=105). At Visit 1, crEA exposure prior to study enrollment was operationalized as parental-reports endorsing a history of crEAs (abuse/neglect, permanent/significant parent-child separation); while child-reports assessed concurrent attachment security. A false memory task was administered online ∼2.5 years later (Visit 2) to probe the content of affective semantic memories-specifically attachment schemas. Results showed that crEA exposure (vs. no exposure) was associated with a higher likelihood of falsely endorsing insecure (vs. secure) schema scenes. Attachment security moderated the association between crEA exposure and insecure schema-based false recognition. Findings suggest that interpersonal-affective semantic schemas include representations of parent-child interactions that may capture the quality of one's own attachment experiences and that these representations shape how children remember attachment-relevant narrative events. Findings are also consistent with the hypothesis that these affective semantic memories can be modified by later experiences. Moving forward, the approach taken in this study provides a means of operationalizing Bowlby's notion of internal working models within a cognitive neuroscience framework. RESEARCH HIGHLIGHTS: Affective semantic memories representing insecure schema knowledge (child needs + needs-not-met) may be more salient, elaborated, and persistent among youths exposed to early caregiving adversity. All youths, irrespective of early caregiving adversity exposure, may possess affective semantic memories that represent knowledge of secure schemas (child needs + needs-met). Establishing secure relationships with parents following early-occurring caregiving adversity may attenuate the expression of insecure semantic memories, suggesting potential malleability. Affective semantic memories include schema representations of parent-child interactions that may capture the quality of one's own attachment experiences and shape how youths remember attachment-relevant events.

GWO-Based Joint Optimization of Millimeter-Wave System and Multilayer Perceptron for Archaeological Application.

Recently, low THz radar-based measurement and classification for archaeology emerged as a new imaging modality. In this paper, we investigate the classification of pottery shards, a key enabler to understand how the agriculture was introduced from the Fertile Crescent to Europe. Our purpose is to jointly design the measuring radar system and the classification neural network, seeking the maximal compactness and the minimal cost, both directly related to the number of sensors. We aim to select the least possible number of sensors and place them adequately, while minimizing the false recognition rate. For this, we propose a novel version of the Binary Grey Wolf Optimizer, designed to reduce the number of sensors, and a Ternary Grey Wolf Optimizer. Together with the Continuous Grey Wolf Optimizer, they yield the CBTGWO (Continuous Binary Ternary Grey Wolf Optimizer). Working with 7 frequencies and starting with 37 sensors, the CBTGWO selects a single sensor and yields a 0-valued false recognition rate. In a single-frequency scenario, starting with 217 sensors, the CBTGWO selects 2 sensors. The false recognition rate is 2%. The acquisition time is 3.2 s, outperforming the GWO and adaptive mixed GWO, which yield 86.4 and 396.6 s.

Improved deep learning for automatic localisation and segmentation of rectal cancer on T2-weighted MRI.

The automatic segmentation approaches of rectal cancer from magnetic resonance imaging (MRI) are very valuable to relieve physicians from heavy workloads and enhance working efficiency. This study aimed to compare the segmentation accuracy of a proposed model with the other three models and the inter-observer consistency. A total of 65 patients with rectal cancer who underwent MRI examination were enrolled in our cohort and were randomly divided into a training cohort (n = 45) and a validation cohort (n = 20). Two experienced radiologists independently segmented rectal cancer lesions. A novel segmentation model (AttSEResUNet) was trained on T2WI based on ResUNet and attention mechanisms. The segmentation performance of the AttSEResUNet, U-Net, ResUNet and U-Net with Attention Gate (AttUNet) was compared, using Dice similarity coefficient (DSC), Hausdorff distance (HD), mean distance to agreement (MDA) and Jaccard index. The segmentation variability of automatic segmentation models and inter-observer was also evaluated. The AttSEResUNet with post-processing showedperfect lesion recognition rate (100%) and false recognition rate (0), and its evaluation metrics outperformed other three models for two independent readers (observer 1: DSC = 0.839 ± 0.112, HD = 9.55 ± 6.68, MDA = 0.556 ± 0.722, Jaccard index = 0.736 ± 0.150; observer 2: DSC = 0.856 ± 0.099, HD = 11.0 ± 10.1, MDA = 0.789 ± 1.07, Jaccard index = 0.673 ± 0.130). The segmentation performance of AttSEResUNet was comparable and similar to manual variability (DSC = 0.857 ± 0.115, HD = 10.0 ± 10.0, MDA = 0.704 ± 1.17, Jaccard index = 0.666 ± 0.139). Comparing with other three models, the proposed AttSEResUNet model was demonstrated as a more accurate model for contouring the rectal tumours in axial T2WI images, whose variability was similar to that of inter-observer.

Effect of emotional valence on true and false recognition controlling arousal

ABSTRACT The aim of our experiment was to analyse the effect of the emotional valence (positive, negative, or neutral) on true and false recognition, matching the arousal, frequency, concreteness, and associative strength of the study and recognition words. Fifty younger adults and 46 healthy older adults performed three study tasks (with words of different valence: positive, negative, neutral) and their corresponding recognition tests. Two weeks later, they performed the three recognition tests again. The results show that words with a negative valence produced less true recognition and less false recognition than words with a positive or neutral valence, in both younger and older adults, on the immediate recognition test. This pattern of results was also found in the younger adults on the delayed recognition test, whereas in the older participants, these differences disappeared. Thus, when arousal is controlled, both younger and older adults tend to recognise negative information worse than positive or neutral information, but they also commit fewer errors. Results would suggest that the greater arousal commonly associated with negative stimuli, rather than their own valence, could explain some of the contradictory results found in the literature.

Emotion recognition in amyotrophic lateral sclerosis in a dynamic environment

ObjectiveThe aim of our study was to measure the ability of ALS patients to process dynamic facial expressions as compared to a control group of healthy subjects and to correlate this ability in ALS patients with neuropsychological, clinical and neurological measures of the disease. MethodsSixty-three ALS patients and 47 healthy controls were recruited. All the ALS patients also underwent i) the Geneva Emotion Recognition Test (GERT) in which ten actors express 14 types of dynamic emotions in brief video clips with audio, ii) the Edimburgh Cognitive and Behavioral ALS Screen (ECAS) test; iii) the ALS Functional Rating Scale Revised (ALSFRS-R) and iv) the Medical Research Council (MRC) for the evaluation of muscle strength. All the healthy subjects enrolled in the study underwent the GERT. ResultsThe recognition of irritation and pleasure was significantly different between ALS patients and the control group. The amusement, despair, irritation, joy, sadness and surprise had been falsely recognized differently between the two groups. Specific ALS cognitive impairment was associated with bulbar-onset phenotype (OR = 14,3889; 95%CI = 3,96-52,16). No association was observed between false emotion recognition and cognitive impairment (F(1,60)=,56,971, p=,45,333). The number of categorical errors was significantly higher in the ALS patients than in the control group (27,66 ± 7,28 vs 17,72 ± 5,29; t = 8723; p = 0.001). ConclusionsALS patients show deficits in the dynamic processing of a wide range of emotions. These deficits are not necessarily associated with a decline in higher cognitive functions: this could therefore lead to an underestimation of the phenomenon.

"I heard it before … or not": time-course of ERP response and behavioural correlates associated with false recognition memory.

Electrophysiological and behavioural correlates of true and false memories were examined in the Deese/Roediger-McDermont (DRM) paradigm. A mass univariate approach for analysing event-related potentials (ERP) in the temporal domain was used to examine the electrophysiological effects associated with this paradigm precisely (point-by-point) and without bias (data-driven). Behaviourally, true and false recognition did not differ, and the predicted DRM effect was observed, as false recognition of critical lures (i.e., new words semantically related to studied words) was higher than false alarms of new (unrelated) words. Neurally, an expected old/new effect was observed during the time-range of the late positive component (LPC) over left centro-parietal scalp electrodes. Furthermore, true recognition also evoked larger LPC amplitudes than false recognition over both left centro-parietal and fronto-central scalp electrodes. However, we did not observe LPC-related differences between critical lures and new words, nor between correct rejections of critical lures and new words. In contrast, correct rejections of critical lures were accompanied by higher activation of a sustained positive slow wave (SPSW) in right fronto-central electrodes beyond 1200 ms. This result reveals a key role of post-retrieval processes in recognition. Results are discussed in light of theoretical approaches to false memory in the DRM paradigm.