M1 muscarinic receptor activation reverses age-related memory updating impairment in mice.

Previously consolidated memories can become temporarily labile upon reactivation. Reactivation-based memory updating is chiefly studied in young subjects, so we aimed to assess this process across the lifespan. To do this, we developed a behavioural paradigm wherein a reactivated object memory is updated with contextual information; 3-month-old and 6-month-old male C57BL/6 mice displayed object memory updating, but 12-month-old mice did not. We found that M1 muscarinic acetylcholine receptor signaling during reactivation was necessary for object memory updating in the young mice. Next, we targeted this mechanism in an attempt to facilitate object memory updating in aging mice. Remarkably, systemic pharmacological M1 receptor activation reversed the age-related deficit. Quantification of cholinergic system markers within perirhinal cortex revealed subtle cellular changes that may contribute to differential performance across age groups. These findings suggest that natural cholinergic change across the lifespan contributes to inflexible memory in the aging brain.

Domain-specific updating of metacognitive self-beliefs.

Metacognitive self-monitoring is thought to be largely domain-general, with numerous prior studies providing evidence of a metacognitive g-factor. The observation of shared inter-individual variance across different measures of metacognition does not however preclude the possibility that some aspects may nevertheless be domain-specific. In particular, it is unknown the degree to which explicit metacognitive beliefs regarding one's own abilities may exhibit domain generality. Similarly, little is known about how such prior self-beliefs are maintained and updated in the face of new metacognitive experiences. In this study of 330 healthy individuals, we explored metacognitive belief updating across memory, visual, and general knowledge domains spanning nutritional and socioeconomic facts. We find that across all domains, participants strongly reduced their self-belief (i.e., expressed less confidence in their abilities) after completing a multi-domain metacognition test battery. Using psychological network and cross-correlation analyses, we further found that while metacognitive confidence exhibited strong domain generality, metacognitive belief updating was highly domain-specific, such that participants shifted their confidence specifically according to their performance on each domain. Overall, our findings suggest that metacognitive experiences prompt a shift in self-priors from a more general to a more specific focus.

Modal test and finite element updating of sprayer boom truss.

In addressing the finite element model and actual structural error of the sprayer boom truss, this study aims to achieve high-precision dynamic characteristics, enhance simulation credibility, make informed optimization decisions, and reduce testing costs. The research investigates the dynamic behavior of the sprayer boom truss through modal experiments and finite element simulations. Initially, modal parameters of the sprayer boom are obtained through experimental testing, validating their reasonableness and reliability. Subsequently, Ansys Workbench18.0 simulation software was employed to analyze the finite element model of the sprayer boom, revealing a maximum relative error of 11.93% compared to experimental results. To improve accuracy, a kriging-based response surface model was constructed, and multi-objective parameter adjustments using the MOGA algorithm reduce the maximum relative error to 4.6%. Sensitivity analysis further refines the model by optimizing target parameters, resulting in a maximum relative error of 4.96%. These findings demonstrate the effective enhancement of the corrected finite element model's precision, with the response surface method outperforming sensitivity analysis the maximum relative error between the updated finite element model and experimental results was within the engineering allowable range, confirming the effectiveness of the updated model.

Enhancing real-time urban drainage network modeling through Crossformer algorithm and online continual learning.

This study presents an innovative approach to real-time modeling of urban drainage networks, leveraging a highly accurate coupled one- and two-dimensional hydrodynamic model to generate a training dataset for node water levels. By employing global states inferred from monitoring points as model inputs, this study overcomes the limitations imposed by the scarcity of monitoring data and the challenge of capturing all node levels. The Crossformer algorithm, which simultaneously accounts for correlation at both temporal and feature scales, is applied to enhance the precision of simultaneous water level predictions across the network. Comparative analysis of different prediction patterns reveals that extending predictions based on a high-accuracy infrastructure offers more benefits than direct modifications to the algorithm's structure. In addition, this paper pioneered the application of online continuous learning concepts to update the prediction model in real time, achieving a balanced integration of measured and simulated data. Consequently, this paper establishes a complete monitoring-predicting-updating real-time simulation system for urban drainage networks.

Prediction errors lead to updating of memories for conversations.

Previous research has established that the brain uses episodic memories to make continuous predictions about the world and that prediction errors, so the mismatch between generated predictions and reality, can lead to memory updating. However, it remains unclear whether prediction errors can stimulate updating in memories for naturalistic conversations. Participants encoded naturalistic dialogues, which were later presented in a modified form. We found that larger modifications were associated with increased learning of the modified statement. Moreover, memory for the original version of the statement was weakened after medium-strong prediction errors, which resulted from the interplay of modification extent and strength of previous memory. After strong prediction errors, both original and modification were well-remembered. Prediction errors thus play a role in keeping representations of statements and therefore socially relevant knowledge about others up to date.

Memory Reconsolidation Updating in Substance Addiction: Applications, Mechanisms, and Future Prospects for Clinical Therapeutics.

Persistent and maladaptive drug-related memories represent a key component in drug addiction. Converging evidence from both preclinical and clinical studies has demonstrated the potential efficacy of the memory reconsolidation updating procedure (MRUP), a non-pharmacological strategy intertwining two distinct memory processes: reconsolidation and extinction-alternatively termed "the memory retrieval-extinction procedure". This procedure presents a promising approach to attenuate, if not erase, entrenched drug memories and prevent relapse. The present review delineates the applications, molecular underpinnings, and operational boundaries of MRUP in the context of various forms of substance dependence. Furthermore, we critically examine the methodological limitations of MRUP, postulating potential refinement to optimize its therapeutic efficacy. In addition, we also look at the potential integration of MRUP and neurostimulation treatments in the domain of substance addiction. Overall, existing studies underscore the significant potential of MRUP, suggesting that interventions predicated on it could herald a promising avenue to enhance clinical outcomes in substance addiction therapy.

An improved Coati Optimization Algorithm with multiple strategies for engineering design optimization problems.

Aiming at the problems of insufficient ability of artificial COA in the late optimization search period, loss of population diversity, easy to fall into local extreme value, resulting in slow convergence and lack of exploration ability; In this paper, an improved COA algorithm based on chaotic sequence, nonlinear inertia weight, adaptive T-distribution variation strategy and alert updating strategy is proposed to enhance the performance of COA (shorted as TNTWCOA). The algorithm introduces chaotic sequence mechanism to initialize the position. The position distribution of the initial solution is more uniform, the high quality initial solution is generated, the population richness is increased, and the problem of poor quality and uneven initial solution of the Coati Optimization Algorithm is solved. In exploration phase, the nonlinear inertial weight factor is introduced to coordinate the local optimization ability and global search ability of the algorithm. In the exploitation phase, adaptive T-distribution variation is introduced to increase the diversity of individual population under low fitness value and improve the ability of the algorithm to jump out of the local optimal value. At the same time, the alert update mechanism is proposed to improve the alert ability of COA algorithm, so that it can search within the optional range. When Coati is aware of the danger, Coati on the edge of the population will quickly move to the safe area to obtain a better position, while Coati in the middle of the population will randomly move to get closer to other Coatis. IEEE CEC2017 with 29 classic test functions were used to evaluate the convergence speed, convergence accuracy and other indicators of TNTWCOA algorithm. Meanwhile, TNTWCOA was used to verify 4 engineering design optimization problems, such as pressure vessel optimization design and welding beam design. The results of IEEE CEC2017 and engineering design Optimization problems are compared with Improved Coati Optimization Algorithm (ICOA), Coati Optimization Algorithm (COA), Golden Jackal Optimization Algorithm (GJO), Osprey Optimization Algorithm (OOA), Sand Cat Swarm Optimization Algorithm (SCSO), Subtraction-Average-Based Optimizer (SABO). The experimental results show that the improved TNTWCOA algorithm significantly improves the convergence speed and optimization accuracy, and has good robustness. Three­bar truss design problem, The Gear Train Design Problem, Speed reducer design problem shows a strong solution advantage. The superior optimization ability and engineering practicability of TNTWCOA algorithm are verified.

Addressing unreliable local models in federated learning through unlearning.

Federated unlearning (FUL) is a promising solution for removing negative influences from the global model. However, ensuring the reliability of local models in FL systems remains challenging. Existing FUL studies mainly focus on eliminating bad data influences and neglecting scenarios where other factors, such as adversarial attacks and communication constraints, also contribute to negative influences that require mitigation. In this paper, we introduce Local Model Refining (LMR), a FUL method designed to address the negative impacts of bad data as well as other factors on the global model. LMR consists of three components: (i) Identifying and categorizing unreliable local models into two classes based on their influence source: bad data or other factors. (ii) Bad Data Influence Unlearning (BDIU): BDIU is a client-side algorithm that identifies affected layers in unreliable models and employs gradient ascent to mitigate bad data influences. Boosting training is applied when necessary under specific conditions. (iii) Other Influence Unlearning (OIU): OIU is a server-side algorithm that identifies unaffected parameters in the unreliable local model and combines them with corresponding parameters of the previous global model to construct the updated local model. Finally, LMR aggregates updated local models with remaining local models to produce the unlearned global model. Extensive evaluation shows LMR enhances accuracy and accelerates average unlearning speed by 5x compared to comparison methods on MNIST, FMNIST, CIFAR-10, and CelebA datasets.

Implementation of a dynamic model updating pipeline provides a systematic process for maintaining performance of prediction models.

OBJECTIVES: We describe the steps for implementing a dynamic updating pipeline for clinical prediction models and illustrate the proposed methods in an application of 5-year survival prediction in cystic fibrosis. STUDY DESIGN AND SETTING: Dynamic model updating refers to the process of repeated updating of a clinical prediction model with new information to counter performance degradation. We describe 2 types of updating pipeline: "proactive updating" where candidate model updates are tested any time new data are available, and "reactive updating" where updates are only made when performance of the current model declines or the model structure changes. Methods for selecting the best candidate updating model are based on measures of predictive performance under the 2 pipelines. The methods are illustrated in our motivating example of a 5-year survival prediction model in cystic fibrosis. Over a dynamic updating period of 10 years, we report the updating decisions made and the performance of the prediction models selected under each pipeline. RESULTS: Both the proactive and reactive updating pipelines produced survival prediction models that overall had better performance in terms of calibration and discrimination than a model that was not updated. Further, use of the dynamic updating pipelines ensured that the prediction model's performance was consistently and frequently reviewed in new data. CONCLUSION: Implementing a dynamic updating pipeline will help guard against model performance degradation while ensuring that the updating process is principled and data-driven.

Efficient Model Updating of a Prefabricated Tall Building by a DNN Method.

The significance of model updating methods is becoming increasingly evident as the demand for greater precision in numerical models rises. In recent years, with the advancement of deep learning technology, model updating methods based on various deep learning algorithms have begun to emerge. These methods tend to be complicated in terms of methodological architectures and mathematical processes. This paper introduces an innovative model updating approach using a deep learning model: the deep neural network (DNN). This approach diverges from conventional methods by streamlining the process, directly utilizing the results of modal analysis and numerical model simulations as deep learning input, bypassing any additional complex mathematical calculations. Moreover, with a minimalist neural network architecture, a model updating method has been developed that achieves both accuracy and efficiency. This distinctive application of DNN has seldom been applied previously to model updating. Furthermore, this research investigates the impact of prefabricated partition walls on the overall stiffness of buildings, a field that has received limited attention in the previous studies. The main finding was that the deep neural network method achieved a Modal Assurance Criterion (MAC) value exceeding 0.99 for model updating in the minimally disturbed 1st and 2nd order modes when compared to actual measurements. Additionally, it was discovered that prefabricated partitions exhibited a stiffness ratio of about 0.2-0.3 compared to shear walls of the same material and thickness, emphasizing their role in structural behavior.

Multimodal Cues to Change Your Mind: The Intertwining of Faces, Voices, and Behaviors in Impression Updating.

Impressions of others are formed from multiple cues, including facial features, vocal tone, and behavioral descriptions, and may be subject to multimodal updating. Four experiments (N = 803) examined the influence of a target's face or voice on impression updating. Experiments 1a-1b examined whether behavior-based impressions are susceptible to updating by incongruent information conveyed by the target's face, voice, or behavior (within-participant manipulation). Both faces and voices updated impressions with comparable strength, but less than behaviors. Experiment 2, contrasting faces and voices only (between-participants manipulation), showed that voices outperformed faces regardless of how impressions were formed (i.e., via behavioral vs. nonbehavioral information). Experiment 3 found no difference when comparing faces and voices in a within-participant design and controlling for stimulus attractiveness. Our work highlights the importance of multimodal cues for impression updating and shows that the relative power of faces and voices depends on contextual factors.

The Mixed Role of Sleep and Time of Day in Working Memory Performance of Older Adults with Mild Cognitive Impairment.

The importance of night sleep for maintaining good physical and cognitive health is well documented as well as its negative changes during aging. Since Mild Cognitive Impairment (MCI) patients bear additional disturbances in their sleep, this study aimed at examining whether there are potential mixed effects of sleep and afternoon time of day (ToD) on the storage, processing, and updating components of working memory (WM) capacity in older adults with MCI. In particular, the study compared patients' performance in the three working memory components, in two-time conditions: "early in the morning and after night sleep", and "in the afternoon and after many hours since night sleep". The Working Memory Capacity & Updating Task from the R4Alz battery was administered twice to 50 older adults diagnosed with MCI. The repeated measures analysis showed statistically significant higher performance in the morning condition for the working memory updating component (p < 0.001). Based on the findings, it seems that the afternoon ToD condition negatively affects tasks with high cognitive demands such as the WM updating task in MCI patients. These findings could determine the optimal timing for cognitive rehabilitation programs for MCI patients and the necessary sleep duration when they are engaged in cognitively demanding daily activities.

Tracking politically motivated reasoning in the brain: the role of mentalizing, value-encoding, and error detection networks.

Susceptibility to misinformation and belief polarization often reflects people's tendency to incorporate information in a biased way. Despite the presence of competing theoretical models, the underlying neurocognitive mechanisms of motivated reasoning remain elusive as previous empirical work did not properly track the belief formation process. To address this problem, we employed a design that identifies motivated reasoning as directional deviations from a Bayesian benchmark of unbiased belief updating. We asked the members of a proimmigration or an anti-immigration group regarding the extent to which they endorse factual messages on foreign criminality, a polarizing political topic. Both groups exhibited a desirability bias by overendorsing attitude-consistent messages and underendorsing attitude-discrepant messages and an identity bias by overendorsing messages from in-group members and underendorsing messages from out-group members. In both groups, neural responses to the messages predicted subsequent expression of desirability and identity biases, suggesting a common neural basis of motivated reasoning across ideologically opposing groups. Specifically, brain regions implicated in encoding value, error detection, and mentalizing tracked the degree of desirability bias. Less extensive activation in the mentalizing network tracked the degree of identity bias. These findings illustrate the distinct neurocognitive architecture of desirability and identity biases and inform existing cognitive models of politically motivated reasoning.

Plinko: Eliciting beliefs to build better models of statistical learning and mental model updating.

Prior beliefs are central to Bayesian accounts of cognition, but many of these accounts do not directly measure priors. More specifically, initial states of belief heavily influence how new information is assumed to be utilized when updating a particular model. Despite this, prior and posterior beliefs are either inferred from sequential participant actions or elicited through impoverished means. We had participants to play a version of the game 'Plinko', to first elicit individual participant priors in a theoretically agnostic manner. Subsequent learning and updating of participant beliefs was then directly measured. We show that participants hold various priors that cluster around prototypical probability distributions that in turn influence learning. In follow-up studies, we show that participant priors are stable over time and that the ability to update beliefs is influenced by a simple environmental manipulation (i.e., a short break). These data reveal the importance of directly measuring participant beliefs rather than assuming or inferring them as has been widely done in the literature to date. The Plinko game provides a flexible and fecund means for examining statistical learning and mental model updating.

The association between maternal and child posttraumatic stress symptoms among families living in southern Israel: The buffering role of maternal executive functions.

Posttraumatic stress disorder is a prolonged stress and anxiety response that occurs after exposure to a traumatic event. Research shows that both parental and child posttraumatic stress symptoms (PTSS) are correlated but parental executive functions (EFs) could buffer this link. EFs refers to a group of high-level cognitive processes that enable self-regulation of thoughts and actions to achieve goal-directed behaviours and can be of importance for both positive parenting interactions and effective coping skills for PTSS. Our study aimed to (1) examine the link between maternal and child PTSS and the moderating role of varying degrees of exposure to severe security threats context, and (2) to identify the moderating role of maternal EFs in this interaction, among families living in southern Israel. Our sample included 131 mothers in their second pregnancy and their firstborn children. Mothers performed computerised tasks to assess their EFs and they reported on their own and their child's PTSS. Results revealed a positive correlation between maternal PTSS and child PTSS. However, the link between maternal and child PTSS was moderated by maternal working memory updating abilities and threat context severity. Among mothers with lower updating capacities, the association between maternal and child symptoms was stronger under higher threat contexts; conversely, among mothers with higher maternal updating abilities, threat context did not modulate the link between maternal and child PTSS, suggesting a stress-buffering effect. Our study contributes to the growing literature on the significant role of parental EFs in the context of parent-child interactions.

Exploring the relationship between frequent cannabis use, belief updating under uncertainty and psychotic-like symptoms.

Background: Cannabis users present an important group for investigating putative mechanisms underlying psychosis, as cannabis-use is associated with an increased risk of psychosis. Recent work suggests that alterations in belief-updating under uncertainty underlie psychosis. We therefore compared belief updating under uncertainty between cannabis and non-cannabis users. Methods: 49 regular cannabis users and 52 controls completed the Space Game, via an online platform used for behavioral testing. In the task, participants were asked to predict the location of the stimulus based on previous information, under different uncertainty conditions. Mixed effects models were used to identify significant predictors of mean score, confidence, performance error and learning rate. Results: Both groups showed decreased confidence in high noise conditions, and increased belief updating in more volatile conditions, suggesting that they could infer the degree and sources of uncertainty. There were no significant effects of group on any of the performance indices. However, within the cannabis group, frequent users showed worse performance than less frequent users. Conclusion: Belief updating under uncertainty is not affected by cannabis use status but could be impaired in those who use cannabis more frequently. This finding could show a similarity between frequent cannabis use and psychosis risk, as predictors for abnormal belief-updating.

Correcting fake news headlines after repeated exposure: memory and belief accuracy in younger and older adults.

The efficacy of fake news corrections in improving memory and belief accuracy may depend on how often adults see false information before it is corrected. Two experiments tested the competing predictions that repeating fake news before corrections will either impair or improve memory and belief accuracy. These experiments also examined whether fake news exposure effects would differ for younger and older adults due to age-related differences in the recollection of contextual details. Younger and older adults read real and fake news headlines that appeared once or thrice. Next, they identified fake news corrections among real news headlines. Later, recognition and cued recall tests assessed memory for real news, fake news, if corrections occurred, and beliefs in retrieved details. Repeating fake news increased detection and remembering of corrections, correct real news retrieval, and erroneous fake news retrieval. No age differences emerged for detection of corrections, but younger adults remembered corrections better than older adults. At test, correct fake news retrieval for earlier-detected corrections was associated with better real news retrieval. This benefit did not differ between age groups in recognition but was greater for younger than older adults in cued recall. When detected corrections were not remembered at test, repeated fake news increased memory errors. Overall, both age groups believed correctly retrieved real news more than erroneously retrieved fake news to a similar degree. These findings suggest that fake news repetition effects on subsequent memory accuracy depended on age differences in recollection-based retrieval of fake news and that it was corrected.

Removing information from working memory with a delay: Effective but not beneficial.

Ideally, removing outdated information from working memory (WM) should have two consequences: The removed content should be less accessible (removal costs), and other WM content should benefit from the freeing up of WM capacity (removal benefits). Robust removal benefits and removal costs have been demonstrated when people are told to forget items shortly after they were encoded (immediate removal). However, other studies suggest that people might be unable to selectively remove items from an already encoded set of items (delayed removal). In two experiments (n = 219; n = 241), we investigated the effectiveness and consequences of delayed removal by combining a modified version of Ecker's et al. (Journal of Memory and Language, 74, 77-90, 2014) letter updating task with a directed-forgetting in WM paradigm. We found that while delayed removal resulted in reduced memory for the to-be-forgotten item-location relations (removal costs), it failed to enhance performance for existing WM content. This contrasts sharply with immediate removal, where removal benefits can be observed. A fine-grained analysis of removal benefits shows that removal from WM proactively facilitates the subsequent encoding of new information but does not retroactively aid stored WM content.

Probabilistic inference and Bayesian-like estimation in animals: Empirical evidence.

Animals often make decisions without perfect knowledge of environmental parameters like the quality of an encountered food patch or a potential mate. Theoreticians often assume animals make such decisions using a Bayesian updating process that combines prior information about the frequency distribution of resources in the environment with sample information from an encountered resource; such a process leads to decisions that maximize fitness, given the available information. I examine three aspects of empirical work that shed light on the idea that animals can make such decisions in a Bayesian-like manner. First, many animals are sensitive to variance differences in behavioral options, one metric used to characterize frequency distributions. Second, several species use information about the relative frequency of preferred versus nonpreferred items in different populations to make probabilistic inferences about samples taken from populations in a manner that results in maximizing the likelihood of obtaining a preferred reward. Third, the predictions of Bayesian models often match the behavior of individuals in two main approaches. One approach compares behavior to models that make different assumptions about how individuals estimate the quality of an environmental parameter. The patch exploitation behavior of nine species of birds and mammals has matched the predictions of Bayesian models. The other approach compares the behavior of individuals who learn, through experience, different frequency distributions of resources in their environment. The behavior of three bird species and bumblebees exploiting food patches and fruit flies selecting mates is influenced by their experience learning different frequency distributions of food and mates, respectively, in ways consistent with Bayesian models. These studies lend support to the idea that animals may combine prior and sample information in a Bayesian-like manner to make decisions under uncertainty, but additional work on a greater diversity of species is required to better understand the generality of this ability.

Engrams of Fear Memory Attenuation.

Fear attenuation is an etiologically relevant process for animal survival, since once acquired information needs to be continuously updated in the face of changing environmental contingencies. Thus, when situations are encountered that were originally perceived as fearful but are no longer so, fear must be attenuated, otherwise, it risks becoming maladaptive. But what happens to the original memory trace of fear during fear attenuation? In this chapter, we review the studies that have started to approach this question from an engram perspective. We find evidence pointing to both the original memory trace of fear being suppressed, as well as it being updated towards safety. These seemingly conflicting results reflect a well-established dichotomy in the field of fear memory attenuation, namely whether fear attenuation is mediated by an inhibitory mechanism that suppresses fear expression, called extinction, or by an updating mechanism that allows the fear memory to reconsolidate in a different form, called reconsolidation-updating. Which of these scenarios takes the upper hand is ultimately influenced by the behavioral paradigms used to induce fear attenuation, but is an important area for further study as the precise cell populations underlying fear attenuation and the molecular mechanisms therein can now be understood at unprecedented resolution.