Electrophysiological evidence for encoding mechanisms underlying the formation of false recognitions with context retrieval.

Reinstating context from encoding to retrievalincreases true recognition (e.g.,testing object memoryonreinstated versus switched background scenes), but context reinstatement also increases false recognition of similar objects (a "context reinstatement illusion"; see Doss et al., Psychological Science, 29, 914-925, 2018). Here we report three experiments extending these context reinstatement effects to semantically associated words (i.e., the Deese/Roediger-McDermott (DRM) false memory task), thereby demonstrating that the context illusion generalizes beyond the object pictures used in prior work. We found robust context reinstatement effects on both true and false recognition, even thoughparticipantswere toldthat the encoding and retrieval context would not always match(Experiments 1 and 3). Moreover, context reinstatement boosted claims of"recollecting"details, thereby boosting the typical DRM illusion.By contrast, when we gave a stronger warning to avoid context asmisleading(Experiment 2), context reinstatement effects on both true and false recognition were smaller and unreliable, suggesting that strategic attempts to minimize this illusion during retrieval came at the cost of context reinstatement's benefit to true recognition. Overall, our demonstration of a context reinstatement illusion using the DRM task provides another example of how context reinstatement can increase both true and false recognition. These results also provide new evidence that item-context conceptual associations can drive these misleading effects of context reinstatement during memory retrieval.

Prediction, that is, the anticipation of linguistic features during sentence reading, affects language processing in the moment-but does it also affect the memory representations that people store? Here, we aimed to adjudicate between conflicting theoretical accounts that offer divergent predictions about how prediction affects the formation of both false and true (i.e., veridical) memory. Whereas false memory for predictable words can result from prediction during sentence encoding or semantic association during memory retrieval (i.e., prediction vs. retrieval-based activation accounts of false memory), true memory is sometimes improved from congruency with schema or novelty (i.e., schema-congruency vs. prediction-error accounts of true memory). Experiment 1 manipulated the plausibility of prediction-disconfirming words during encoding to investigate whether larger prediction errors suppress false and improve true memory. Experiment 2 investigated false memory for unpredictable semantically related or form-related words to substantiate whether false remembering results from prediction. Experiment 3 investigated how false and true memory are affected by encoding conditions that render a word predictable from or merely congruent with the linguistic context. Our results show that false memory is mediated by prediction and retrieval-based activation-even though a much more sizable contribution comes from retrieval-based activation, not prediction. Similarly, for true memory, results show that both congruency with schema and magnitude of prediction error boost memory formation. We conclude that both true and false memory are driven by multiple cognitive processes, and we explore avenues toward a unified framework for understanding memory. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Memory must balance preserving episode-specific details with extracting generalizable structure. Here, we test whether spontaneous hippocampal reactivation during postencoding rest actively transforms memories by strengthening semantic gist at the expense of episodic detail. Twenty-four participants encoded Deese-Roediger-McDermott word lists presented in male or female voices, creating orthogonal semantic (list theme) and episodic (voice source) information. Using high-resolution fMRI, we tracked list-specific reactivation in hippocampal subfields during a 90-sec delay period. Most consistently in CA1, reactivation frequency showed opposing relationships with subsequent memory: Greater reactivation predicted increased false recognition of semantic lures (gist extraction; dz = 0.52) and decreased accuracy for voice discrimination (source memory; dz = -0.65), yielding a large, bidirectional effect within CA1 (gist minus source, paired dz = 1.00). This bidirectional pattern was most prominent in anterior-to-mid hippocampus, consistent with its role in schematic processing. These findings reveal that hippocampal reactivation implements a computational trade-off, actively constructing abstract and generalizable representations while competitively weakening episode-specific details. This mechanism could explain how the brain rapidly extracts meaning from experience, with false memories emerging as a natural consequence of adaptive memory transformation rather than retrieval failure.

The error-speed effect, in which items are responded to more accurately if associated with a slow rather than fast erroneous response in a preceding task, is often interpreted as evidence that recognition errors are sometimes driven by systematically misleading memory evidence. However, recent observations challenge this interpretation demonstrating that the error-speed effect only occurs among previously studied items but not among non-studied items (Akan et al. Memory, 31, 1340-1351, 2023), which is not in line with most models of recognition memory. In the present study, we demonstrate that the error-speed effect can occur for non-studied items. In Experiment1, we replicate the error-speed effect for studied but not non-studied items using picture stimuli. In the second experiment, we systematically manipulate the similarity between studied and non-studied items, thereby increasing misleading memory evidence and the rate of false alarms. As a result, the error-speed effect also emerges among non-studied items, indicating that false recognition decisions for these items are at least partially driven by misleading memory evidence.

Existing intelligent monitoring methods are limited by insufficient training samples and target-feature degradation in complex environments. To address these issues, an industrial visual inspection scheme with dual verification is proposed for material sheds. The scheme integrates sample enhancement preprocessing based on a Dynamic Enhanced Generative Adversarial Network (DEGAN) with an Attention-Enhanced YOLO-SLOWFAST (AE-YOLO-SLOWFAST) model for target and behavior detection, enabling feature enhancement, real-time dust monitoring, and timely dust suppression. A dynamic enhancement module is first introduced into a GAN, creating DEGAN to generate high-quality samples and augment the training dataset. An AE-YOLO model is then developed to improve static feature extraction under low illumination and enhance small-target detection. The objective function is refined to improve recognition of hard-to-distinguish samples during training. AE-YOLO is combined with SLOWFAST to recognize vehicle behaviors. Dual verification is performed using dust and vehicle detection results together with action recognition outputs, enabling precise control of dust suppression equipment for targeted water mist spraying. The improved AE-YOLO model achieves an mAP@50 of 94.4%. The proposed method delivers a vehicle–dust association matching accuracy of up to 97.2%, which enables all-weather, intelligent, traceable dust suppression in material sheds, reduces false recognition interference, and ensures timely suppression in areas where vehicles are operating.

Episodic autobiographical memory (EAM) relies on vivid encoding contexts, with the self as a central reference point. However, EAM models that account for both encoding and consolidation processes in naturalistic settings remain relatively sparse. This study investigated the impact of memory encoding and subsequent reactivations during the retention interval on EAM. Sixty participants incidentally encoded thirty events varying in emotional valence (positive, neutral, negative) and interaction type (action or observation), while navigating through a naturalistic virtual city. At the end of the encoding session, all participants rated each event on several subjective memory-related dimensions, including emotional valence, self-relevance, and anticipated rehearsal. One month later, they completed free recall and recognition tests. Additionally, a subgroup of thirty participants completed intermediate reactivations through free recall tests, administered immediately and one week after encoding. Between-groups analyses showed that one-month free recall performance was enhanced in the group with intermediate reactivations, whereas recognition performance did not differ, except for a significant increase in false recognitions in the reactivation group. Mixed-effects models based on the subjective encoding ratings identified two core predictors of long-lasting EAM, regardless of reactivation: event novelty and the richness of mental imagery. With intermediate reactivations, additional predictors emerged, such as emotional valence and prospective relevance of the event. Overall, this study contributes to predictive models of EAM by demonstrating how reactivations modulate memory consolidation in a naturalistic context, supporting a dynamic view of consolidation aligned with the Multiple-Trace and Trace-Transformation theories. These findings open new directions for ecologically valid memory research, with particular relevance for therapeutic and clinical applications.

False memories are often triggered by visual similarity, but this study shows that meaning encoded during learning can distort memory for visual details, even when retrieval cues are meaningless. Participants learned fonts associated with words or pseudowords and judged whether similar lure fonts, shown on a meaningless letter string, were seen before. Although behavioral performance was similar across conditions, brain imaging revealed a key dissociation: the left fusiform gyrus and anterior hippocampus promote semantic generalization that increases false recognition, whereas the right fusiform gyrus and posterior hippocampus support perceptual specificity that protects against it. These findings reveal how distinct hippocampal-cortical pathways differentially bias memory toward truth or illusion.

Presenting participants with lists of related words (e.g., climb, rock, slope, hike) often elicits false recall and recognition for a related but nonstudied critical lure (CL; mountain). This false memory illusion is often ascribed to the strong associative and/or thematic relations between the related list words and the CL. In the present study, we further evaluated this false memory effect by presenting participants with a list of affordances (e.g., arrange, pick, smell) that were related to a specific object CL (e.g., flower) via a normed database. Affordances refer to specific actions that can be completed with a given object, and the presentation of these actions may result in a false memory for a CL. Each study list was followed by either a free-recall test or a filler arithmetic task and a final recognition test. False memory for the CL was found both in the initial recall test and the final recognition test and was greater following an initial recall test. These data indicate that affordances can produce false memories for related objects which is particularly notable given affordances are verbs and CLs are nouns.

Remembering is constructive. Thus, a complete account of memory must predict patterns in both veridical and false recognition. Ideally, that account will predict the content of what people falsely remember, not just that they falsely remember. To move towards that goal, we need theories of both representation and retrieval as well as an account that joins those two accounts into a cohesive whole. To work towards that goal, Reid and Jamieson Journal of Memory and Language, 129, 104397, (2023) previously modelled false recognition in the Deese-Roediger-McDermott paradigm by importing vectors that represent word meaning, derived from distributional semantic models, into the MINERVA 2 framework. However, semantic meaning is only one aspect of a word's representation in human memory as words are also represented in terms of their perceptual features (i.e., their phonology and orthography). In this paper, we extended Reid and Jamieson's account by developing a common method to derive orthographic and phonological representations for words to investigate veridical and false recognition of words in DRM-like tasks. To test the model, we conducted three false recognition experiments for study lists composed of (a) semantic word associates, (b) phonological neighbors, and (c) perceptually similar nonwords. Following this, we conducted two more experiments where phonological and orthographic similarity were more delineated, with critical lures that were either orthographically, but not phonologically related to studied items (Exp. 2a) or vice versa (Exp. 2b). We then extend the analysis to three more experiments with the directed forgetting procedure. The model tracks people's recognition and false recognition of lures that are semantically, phonologically, and orthographically related to studied items. These findings connect current efforts back to Herbert Simon's arguments about the importance of modelling both representation and process for understanding memory. It also joins a growing body of work on the development of structured language representations and the incorporation of those representations into existing models of memory. At broader view, we conclude that the field's models of memory are not only positioned to explain people's memory performance in principle, but also positioned to investigate and predict people's memory performance conditional on the specific memoranda presented in experimental tasks.

Determining the optical potential is one of the crucial aspects of the analysis and control of optical tweezers. It requires finding the scale factor of displacements in an optical tweezer recording, which typically is hard to obtain directly through a photodetector, and instead is usually done by fitting the statistical features of the data to the Langevin dynamics predictions. We argue that this procedure is harder for measurements made using a Segmented Quadrant Detector, for which these scale factors can vary along different axes due to image distortions. Subsequently, it can lead to a false recognition of the potential's anisotropy. We quantify this phenomenon by comparing simultaneous measurements from a Segmented Quadrant Detector and a camera, as well as from the 2D Lateral Effect Detector and a camera. We then discuss which experimental and data analysis techniques should be used to properly determine the geometry of the optical potential.

Individuals often encounter experiences similar to those they have experienced before. These similarities between experiences can elicit spontaneous remembering, such that the current experience serves as a reminder of previous, similar experiences. Remindings have been deemed beneficial for later recall of related items; however, the consequences of reminding on recognition memory performance in terms of novelty detection remain unclear. By developing a novel variant of the Deese-Roediger-McDermott (DRM) paradigm, we investigated the dynamics of novelty detection as a function of explicit similarity-driven reminding and recollection of those remindings. In this paradigm, self-reported remindings were indicated on a trial-by-trial basis at both study and test. We found a pattern of false recognition in line with the DRM literature; however, the ability to accurately detect novelty was moderated by instances of reminding. Indeed, we found that during test, remindings without recollection of the reminded items produced greater false memory for related lures compared to unrelated lures. Interestingly, when related lures induced reminding accompanied by exhaustive recollection of related study items, false alarms were no higher than those for unrelated lures. We interpret this dual consequence of reminding as a function of the interplay between familiarity and recollection, as famously championed by Larry Jacoby (Journal of Memory and Language, 30(5), 513-541, 1991). Moreover, our findings imply that exhaustive recollection of remindings are critical to reducing instances of false memory.

The Association among the Sleep Spindle and Category-Induced False Recognition

ABSTRACT This research describes a multimodal biometric identification system that uses machine learning and feature-level fusion. The importance of this study has to do with the use of a person’s face and palm print to identify themselves. The performance of a multimodal biometric identification system is enhanced by machine learning. Precision, recognition rate, equal error rate, and many more assessment criteria are used to assess performance. The accuracy of the proposed multimodal system is 90.18 % , the false acceptance rate (FAR) is 3.44 % , and the false recognition rate (FRR) is 2.96 % . The multimodal system uses score-level fusion to obtain this outcome. It is shown that a multimodal system can achieve good accuracy with low FAR and FRR.

The increasing incidence of oral cancer presents a significant public health challenge, with many cases diagnosed at advanced stages, leading to poor prognoses and treatment outcomes. Traditional diagnostic methods, including imaging techniques like PET and CT scans, while effective, often suffer from limitations such as invasiveness, time consumption, and the potential for human error in interpretation. The need for more efficient and accurate diagnostic tools is critical, especially as the mortality rate associated with late-stage oral cancer remains alarmingly high. This research aims to address these challenges by developing an optimized recurrent neural network (RNN) specifically designed for oral cancer detection, leveraging the capabilities of machine learning to enhance diagnostic accuracy. In this study, an oral cancer dataset of 2,264 images consisting of Cancer, Malignant and Normal cases was sourced from Mendeley Data, accessible via a publicly available dataset. The acquired images were divided into training and testing sets using the Random Sub-Sampling Cross Validation method, with 70% allocated for training and 30% for testing. The Images were preprocessed using Contrast Limited Adaptive Histogram Equalization (CLAHE) for enhancement. Pied Kingfisher Optimizer (PKO) was employed to optimize RNN parameters: training weights and sigmoid values, to address challenges like over fitting and the difficulty of training on long sequences. The resulting PKO-RNN was used for oral cancer detection of cancer, malignant and normal cases and their classification, with its implementation in MATLAB R(2023a). The system’s performance was evaluated using accuracy, sensitivity, specificity, false positive rate and recognition time.

The déjà vu phenomenon, despite its high prevalence in the general population, is a key symptom in patients with mesial temporal lobe epilepsy (MTLE), where it occurs in 50–80 % of cases. This lecture summarizes current concepts of the neurobiological and cognitive mechanisms of déjà vu, examining it through both the model of pathology (MTLE) and its occurrence in healthy individuals. Based on a review of the literature, the article demonstrates that the hippocampal system, particularly the parahippocampal gyrus, is the central neuroanatomical substrate of déjà vu. Direct evidence comes from studies of MTLE patients, where intracranial electroencephalography and electrical stimulation unequivocally link the onset of déjà vu aura to pathological activity in the mesial temporal lobe. Cognitive models (e.g., Dual Processing, Neural Delay/Mismatch, Global Matching hypotheses) interpret déjà vu as a temporary “glitch” in memory systems, involving a dissociation between the feeling of familiarity (mediated by the perirhinal cortex) and the mechanism of detailed contextual recollection (hippocampus). The contribution of specific subregions of the hippocampal formation (dentate gyrus, СА1–СА3 fields, entorhinal cortex) to pattern separation and completion processes, whose impairment underlies false recognition, is examined. Thus, the déjà vu phenomenon serves as a unique “window” into the mechanisms of memory function. The clinical model of MTLE demonstrates its direct link to pathological activity in the mesial temporal lobe, while cognitive models explain its occurrence in healthy people because of transient dysfunction of the same hippocampal mechanisms. Future research should focus on identifying specific electrophysiological patterns and clarifying the role of impaired inhibitory control and pattern separation mechanisms in the genesis of this phenomenon.

ABSTRACT False memories are often studied using the Deese–Roediger–McDermott (DRM) paradigm. This paradigm demonstrates how semantically related word lists can induce erroneous recall or recognition of non-presented critical lures. Emotional DRM lists typically elicit higher false recognition than neutral lists but paradoxically reduce false recall. To examine why, this study examined the retrieval dynamics of false recall for emotional (negative) versus neutral lists using an externalised free recall task. Here, participants list all words that come to mind (inclusion output) before indicating which they believe were studied (recall output). Emotional lists produced fewer critical lures during inclusion, but no difference in the proportion of those lures later labelled as recalled. These results help us to more fully understand the role of retrieval in emotional false memory development and the importance of lure accessibility and error correction as a crucial feature in theoretical explanations of false memories. Implications for theoretical models and differences in recall and recognition dynamics are discussed.

Active compound jamming, utilizing complex multi-modulation strategies, generates clusters of deceptive or suppressive false targets (FTs) on radar range profiles, severely compromising detection and tracking performance. To address the threat of compound interrupted sampling repeater jamming (ISRJ), this paper proposes a FT recognition framework based on non-uniform multi-subpulse waveforms. By analyzing the temporal characteristics of ISRJ within the non-uniform subpulse architecture, we first establish a quantitative correlation between FT peaks and subpulse width-amplitude parameters. A segmented multi-subpulse spectral blocking scheme with peak compensation is introduced to enhance discriminative features across detection domains, thereby developing a two-stage recognition method integrating range-velocity matching (RVM) and peak variance (PV) analysis. Specifically, RVM exploits the distinct range-velocity distributions of ISRJ residual sideband artifacts and subpulse-combination sidelobes to achieve preliminary suppression, while PV analysis identifies real targets among the remaining candidates. Furthermore, to guarantee recognition robustness against varying jamming parameters, we formulate a non-convex mixed- integer nonlinear programming (MINLP) model for the joint optimization of subpulse widths and amplitudes. Simulation results demonstrate that the proposed method effectively identifies and eliminates three overlapping ISRJs without prior knowledge, with energy loss limited to a single subpulse, significantly outperforming existing approaches. Meanwhile, semi-physical experiments validate the practical feasibility of the proposed scheme.

First wall protection is a critical challenge in fusion machine operation, requiring rapid and precise diagnostic capabilities. The infrared diagnostics of long-pulse fusion machines, generate a large amount of video data. At WEST, the analysis of infrared thermal data is currently performed by Plasma-facing component Protection Officers (PPOs). This work presents a novel multimodal Large Language Model (LLM) approach to support PPOs in their diagnostic tasks. The framework addresses multiple challenges in fusion machine first wall monitoring by integrating infrared data (images and temperature curves) with domain-specific expertise. Key features include the preservation of data privacy, capturing a priori expert knowledge, quick post-pulse analysis capabilities, and the incorporation of expert feedback loops. The system employs prompt engineering and Low-Rank fine-tuning to embed domain knowledge directly into the diagnostic process. An ablation study using synthetic data demonstrated that a false recognition rate below 0.01 for hot spot characteristics could be achieved with 100 training samples. Based on these findings, the model was fine-tuned using 150 pairs of real-world images and corresponding prompts. This tool, once appropriately fine-tuned, was successfully implemented in the WEST control room during the C10 (10/2024 - 12/2024). Performance evaluation showed that of all the feedback collected, 32% were rated as ”OK” and 41% as ”positive response”. This development marks a significant advancement in automated diagnostic capabilities for fusion operations.

Previous research shows that being directed to forget (or remember) trait-implying behaviors immediately after encoding impairs memory for behaviors but not inferred character traits, as measured by the false recognition paradigm. We reassessed this finding using a more diverse set of faces, newly piloted behaviors and traits, and a different trait-inference measure – the savings in relearning paradigm (Experiment 1). After encoding faces with trait-implying behaviors, each followed by remember or forget instructions, participants learned face-trait word pairs in which traits were either consistent or inconsistent with the encoded behavior. Participants recalled more consistent than inconsistent trait words, confirming spontaneous trait inferences during behavior encoding. This effect was resistant to forget instructions, replicating previous findings while addressing limitations of the false recognition paradigm. Experiment 2 replicated impaired recall for forget-cued behaviors using our new materials. Experiment 3 further examined the impact of forget instructions on impression formation and use, specifically whether they influence future behavior predictions. Results showed that directing participants to forget (or remember) trait-implying behaviors reduced expectations of future trait-consistent behaviors and increased openness to trait-inconsistent behaviors. This is the first study to demonstrate that directed forgetting can alter expectations about others, indicating that reduced memory accessibility, whether of impressions or original behaviors, can promote greater flexibility in social judgments. These findings inform theories of directed forgetting and impression formation and have practical implications for contexts where forgetting is both warranted and beneficial.