Memory Errors Research Articles

Increased memory confidence and delusions in Alzheimer’s disease: a preliminary study

ABSTRACT There is uncertainty about whether delusion formation in Alzheimer’s disease (AD) can be explained by false memories. “Metamemory,” the ability to self-evaluate memory and identify memory errors, is impaired in people with delusions in schizophrenia. Our objective was to investigate whether false memory and metamemory were associated with delusions in AD. Participants with mild AD, with and without delusions, completed a computerized word recognition task and a metamemory measure. Group differences were compared using independent-samples t-tests or Mann Whitney tests. Significant findings were explored through binary logistic regression modeling. Participants with delusions (n = 10) gave more high confidence responses, significantly so for correct responses; percentage of high confidence correct responses for those with delusions (mean (SD)) was 69.7% (31.0%) and for those without (n = 14) was 43.5% (29.9%); t 22 = -2.09, p = .049. This remained significant when sex was included in regression modeling; for each 1.0% increase in high confidence correct responses, participants were 5.4% more likely to have delusions (Exp(β) 1.054, 95% CI 1.007–1.105, p = .025). Findings provide tentative support for a link between metamemory and delusions in AD. This should be explored in a larger sample as it has potential implications for treatment.

Hyperdimensional computing: a framework for stochastic computation and symbolic AI

Hyperdimensional Computing (HDC), also known as Vector Symbolic Architectures (VSA), is a neuro-inspired computing framework that exploits high-dimensional random vector spaces. HDC uses extremely parallelizable arithmetic to provide computational solutions that balance accuracy, efficiency and robustness. The majority of current HDC research focuses on the learning capabilities of these high-dimensional spaces. However, a tangential research direction investigates the properties of these high-dimensional spaces more generally as a probabilistic model for computation. In this manuscript, we provide an approachable, yet thorough, survey of the components of HDC. To highlight the dual use of HDC, we provide an in-depth analysis of two vastly different applications. The first uses HDC in a learning setting to classify graphs. Graphs are among the most important forms of information representation, and graph learning in IoT and sensor networks introduces challenges because of the limited compute capabilities. Compared to the state-of-the-art Graph Neural Networks, our proposed method achieves comparable accuracy, while training and inference times are on average 14.6× and 2.0× faster, respectively. Secondly, we analyse a dynamic hash table that uses a novel hypervector type called circular-hypervectors to map requests to a dynamic set of resources. The proposed hyperdimensional hashing method has the efficiency to be deployed in large systems. Moreover, our approach remains unaffected by a realistic level of memory errors which causes significant mismatches for existing methods.

Sensory-memory interactions via modular structure explain errors in visual working memory

Sensory-memory interactions via modular structure explain errors in visual working memory

Sensory-memory interactions via modular structure explain errors in visual working memory.

Errors in stimulus estimation reveal how stimulus representation changes during cognitive processes. Repulsive bias and minimum variance observed near cardinal axes are well-known error patterns typically associated with visual orientation perception. Recent experiments suggest that these errors continuously evolve during working memory, posing a challenge that neither static sensory models nor traditional memory models can address. Here, we demonstrate that these evolving errors, maintaining characteristic shapes, require network interaction between two distinct modules. Each module fulfills efficient sensory encoding and memory maintenance, which cannot be achieved simultaneously in a single-module network. The sensory module exhibits heterogeneous tuning with strong inhibitory modulation reflecting natural orientation statistics. While the memory module, operating alone, supports homogeneous representation via continuous attractor dynamics, the fully connected network forms discrete attractors with moderate drift speed and nonuniform diffusion processes. Together, our work underscores the significance of sensory-memory interaction in continuously shaping stimulus representation during working memory.

SBAC-SDN: A Scalable Blockchain-based Access Control in Northbound Interface for Multi-Controller SDN with Load Balancing Mechanism

Abstract The rapid adoption of Software-Defined Networking (SDN) has not only revolutionized network management but also presented challenges in scalability and security, particularly in multi-controller environments. This paper introduces a load balancing technique aimed at enhancing the scalability of the Northbound interface, a critical component often susceptible to performance bottlenecks. The experimental setup utilized Mininet and Ryu to create a simulated multi-controller SDN environment, where SBAC-SDN was thoroughly evaluated based on CPU and memory usage, response times, and error rates. The results showed substantial improvements in critical performance indicators, confirming the effectiveness of the load balancing technique in addressing scalability challenges. The findings indicate that the system can be scaled without excessive resource consumption, as CPU usage peaked at 60.3% and memory usage remained below 8 GB even with eight controllers. The stable average response times were consistently below 1 s, and the high throughput, ranging from 4.34 to 4.67 requests per second, further demonstrates the efficiency of the system. The error rate remained 0.0% across all configurations, highlighting the robustness of the implemented security measures. This study contributes to ongoing efforts to improve the scalability and overall robustness of SDNs, aligning with the broader goals of reliability and efficiency in network management.

Intrinsic functional networks for distinct sources of error in visual working memory.

Visual working memory (VWM) is a core cognitive function wherein visual information is stored and manipulated over short periods. Response errors in VWM tasks arise from the imprecise memory of target items, swaps between targets and nontargets, and random guesses. However, it remains unclear whether these types of errors are underpinned by distinct neural networks. To answer this question, we recruited 80 healthy adults to perform delayed estimation tasks and acquired their resting-state functional magnetic resonance imaging scans. The tasks required participants to reproduce the memorized visual feature along continuous scales, which, combined with mixture distribution modeling, allowed us to estimate the measures of memory precision, swap errors, and random guesses. Intrinsic functional connectivity within and between different networks, identified using a hierarchical clustering approach, was estimated for each participant. Our analyses revealed that higher memory precision was associated with increased connectivity within a frontal-opercular network, as well as between the dorsal attention network and an angular-gyrus-cerebellar network. We also found that coupling between the frontoparietal control network and the cingulo-opercular network contributes to both memory precision and random guesses. Our findings demonstrate that distinct sources of variability in VWM performance are underpinned by different yet partially overlapping intrinsic functional networks.

Symbolic testing of floating-point bugs and exceptions

Numerical software are susceptible to floating-point bugs and exceptions, which may lead to severe threats like denial of service attacks. Static analysis techniques such as symbolic execution are effective in detecting general bugs which often cause memory error or program crash. Unfortunately, these methods do not deal well with numerical code as they do not support floating-point constraints and math functions symbolically. In this paper, we propose a new analysis framework YUSE, which can detect floating-point bugs by constructing constraints and exploring paths which contain floating-point expressions. Specifically, we introduce interval computation and interval constraint propagation in non-relational numerical abstract domains, and symbolically model math functions, to accurately detect floating-point bugs and exceptions. Moreover, we leverage two-phase constraint solving to enhance YUSE’s performance. Experimental results show that YUSE outperforms two state-of-the-art tools, Frama-c and Fpse-study, in terms of effectiveness and efficiency, with 1.4× and 7.1× faster than Frama-c and Fpse-study, respectively. Moreover, YUSE found 20 new bugs in real-world software, 12 of which were assigned CVE IDs and 8 of which were confirmed by developers.

An extension to Kendall’s Tau metric to evaluate dissimilarities between data series

Data analysis is performed to examine, interpret, and extract information from data series, and it includes applying various methods and techniques to understand patterns and compare data. An approach to compare data is to use rank metrics that help identify how distinct two data series are when compared to each other according to patterns, formats, criteria, and dimensions in both data series. Among these metrics, Kendall’s Tau metric stands out, as it is robust and inexpensive, widely used in analyzing sequences and genomes, to detect errors in flash memories, and to compare distributions and top-k ranked values. However, a challenge arises when comparing lists with different lengths or when lists do not share the same elements. This happens, for example, when lists are defined by top-k elements, commonly called k-list. In this case, there is no guarantee that two k-lists share the same set of elements. Traditional metrics like Kendall’s Tau are designed to quantify differences only between shared elements in lists. Recognizing this limitation, a possible solution is to apply the metric to the shared elements of the lists. Another solution, named the generalization of Kendall’s Tau, proposed by Fagin et al., considers all elements in two lists. However, this generalization of Kendall Tau is a semi-metric, as it does not satisfy the triangular inequality. To solve this problem, we propose the Extended Kendall Tau (EKT) metric that meets all the conditions of a metric and simultaneously considers the distinct elements of the compared lists. The proposed metric was evaluated by applying conventional Kendall’s Tau and the extended Kendall’s Tau over 40 text files divided into five different languages (eight files per language). We compared KT and EKT measures within the ”same language” and across ”other language” files for the two scenarios. The results revealed that both methods could accurately identify the differences between the groups of texts of the ”same language” and ”other language”. However, the numerical results show that EKT is able to more significantly highlight the difference between groups of texts of different languages.

Mathematical model enhancing flash memory reliability through DFT-driven error correction coding

Flash memory, ubiquitous in diverse electronic devices, confronts persistent challenges stemming from inherent errors that jeopardize data integrity. This research situates itself at the intersection of these challenges and advancements, proposing an inventive error correction coding framework that harnesses the unique capabilities of analysis with a hybrid error control coding (HECC) approach. In the proposed work, a mathematical model aimed at enhancing the flash memory by identifying the error pattern within the pages using the discrete fourier transform (DFT). By incorporating distinctive DFT mathematical properties, the proposed technique intends to improve flash memory error correction beyond traditional methods. The flash storage defect detection and rectification results with hybrid error correction coding achieved bit error rate (BER) of 4.3e-6, latency 14.1, mean 15.1 and standard deviation 1.0. Error correction efficiency 98% and storage overhead 10%. With this approach results are significantly improving the error correction efficiency, reduce storage overhead and enhanced adaptability to diverse error patterns.

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.

(Invited) Analog Computation-in-Memory (CiM) for AI and Neuromorphic Computing

[Invited talk]This paper overviews analog Computation-in-Memory (CiM) with non-volatile memories for AI applications and neuromorphic computing. In the edge IoT/mobile devices, CiM will be heterogeneously integrated with digital processors such as CPUs to realize the inference of AI with high energy efficiency.This talk also introduces the approximate CiM. Inspired by the approximate computing, by tolerating some degree of memory errors, the trade-off of performance, energy and cost is resolved.Finally, this paper covers various AI algorithms and neuromorphic computing such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), event driven Spiking Neural Network (SNN) and Reservoir Computing.

The Use of Pictorial Recall Aids Modifies Dietary Assessment Results: Experiences from Quantitative 24-hour Dietary Recalls of Young Children in Nepal and Senegal

BackgroundRecall bias or memory error is a known limitation of recall-based dietary assessment methods and can lead to substantial misrepresentation of diets. Pictorial recall aids have been used by various studies to mitigate recall bias, but their effect on quantitative, 4-pass 24-h dietary recall (24HR) results has not yet been measured. ObjectivesThis article examines uptake of pictorial recall aids among caregivers of young children in Kathmandu Valley, Nepal, and Guédiawaye Department, Senegal; describes what foods/beverages are subject to recall bias in these contexts; and assesses the effect of these recall aids on estimated dietary outcomes. MethodsWe used cross-sectional data from 24HR surveys that provided a recall aid to reduce caregiver recall bias. Young children’s dietary outcomes were calculated from the 24HR data. Logistic regressions were used to compare characteristics and estimated dietary outcomes of children by recall aid uptake. McNemar’s test and paired t-tests were used to compare estimated dietary outcomes in initial 24HR data with revised 24HR data that incorporated omitted items identified by the recall aid. ResultsPictorial recall aids had relatively high uptake by caregivers in both contexts. Beverages, unhealthy snacks, and fruit were most subject to recall bias. Adding omitted items identified by recall aids resulted in statistically significant changes in most dietary outcomes assessed. Children of caregivers who chose to use the recall aid had significantly different estimated dietary outcomes than children of caregivers who did not use the recall aid. ConclusionsThe use of pictorial recall aids modifies 24HR results in these 2 contexts. Rigorous research is needed to fully understand the impact of recall aids on 24HR methodology and results, with an aim to improve the accuracy of dietary assessment and ultimately inform evidence-based nutrition programming, policy, and recommendations.

The Combined Effect of Tribulus terrestris Hydroalcoholic Extract and Swimming Exercise on Memory and Oxidative Stress in Old Male Rats

ABSTRACT Background and objective This study aims to assess the effect of swim exercise along with consumption of bindii hydroalcoholic extract on memory and the oxidative stress markers in old male rats. Materials and methods This study was conducted on 32 old (400–500 g) and eight young male Wistar rats. The groups included young, old, old bindii (200 mg/kg), old exercise, and old bindii exercise (concurrent swimming training). All interventions were performed within 14 days. The animals’ spatial memory was evaluated by the Y maze, radial maze, and shuttle box, Oxidative stress factors were also measured. Results Compared to the old control group, the bindii extract along with swimming exercise significantly increased the periodic behavior percentage in the Y maze and the delay time in entry into the dark chamber in the shuttle box but no significant difference was seen in the reference memory error in the radial maze. Also, a significant increase in the amount of catalase (CAT) and antioxidant capacity (TAC) and a significant decrease in the amount of malondialdehyde (MDA) were observed in all treatment groups. Conclusion These results show that exercise, along with the bindii extract consumption, can improve spatial and avoidance memory in old rats probably through the reduction of oxidative stress effects.

DRM LISTS CREATED WITH CHATGPT: ANALYSING RECOGNITION MEMORY WITH CLIMATE-CHANGE THEMED LISTS

Machine learning and natural language processing have led to the development of powerful language models such as ChatGPT, which can generate consistent and human-like responses to a wide range of queries. In many domains, ChatGPT provides appropriate responses to given commands. One of the aims of this study is to investigate the use of these association lists, such as the Deese-Roediger-McDermott (DRM) lists popular in cognitive psychology studies, by ChatGPT by giving the necessary instructions. The same method was then used to create association lists around a specific topic (climate change). The results of the first study showed that participants gave more false answers when discriminating whether critical words were presented during the test phase than when related and unrelated words were presented. This finding shows that DRM lists generated by ChatGPT can be used to search for memory errors. In line with the literature, false answers for critical words were predominantly rated as ‘remember’. The results of the second study, which was applied to the lists created on the topic of climate change and compared the responses of the groups with the climate denial scores, show that there is no significant difference in the emergence of false memories between the two groups. The level of climate change denial did not significantly affect the participants’ responses to the critical words in the climate- related lists. The low level of climate denial in the sample is a limitation of this study. It is recommended that future studies compare memory performance across an appropriate sample.

Distortions to the passage of time for annual events: Exploring why Christmas and Ramadan feel like they come around more quickly each year.

Commonly heard statements such as "Christmas comes around more quickly each year" suggest that the passage of time between annual events can become distorted, leading to the sensation of time passing more quickly than normal. At present however, it is unclear how prevalent such beliefs are and, what factors are predictive of it. To explore the prevalence of beliefs that annual events such as Christmas (Study 1 UK sample) and Ramadan (Study 2 Iraqi sample) feel like they come around more quickly each year. To establish the association between distortions to the passage of time between annual events and emotional wellbeing, event specific enjoyment, memory function and self-reported attention to time. Participants completed an online questionnaire exploring their subjective experience of time in relation to Christmas and Ramadan. In addition, measures of attention to time, memory function, quality of life and event specific emotion were also taken. There was widespread agreement that Christmas and Ramadan appeared to come around more quickly each year. In both countries, this belief was associated with greater prospective memory errors, greater attention to time and greater enjoyment of the event. Furthermore, in the UK greater belief that Christmas comes around more quickly was associated with lower social quality of life and in Iraq, greater belief that Ramadan comes around more quickly each year was associated with lower age and female gender. Distortions to the passage of time for annual events are widespread, occur across multiple cultures and are consistently predicted by prospective function, event enjoyment and attention to time. The absence of an association between older age (above 55 years) and a faster passage of time suggests that caution should be taken when concluding that time passes more quickly with increasing age.

Constructive Memory and Conscious Experience.

Episodic memory relies on constructive processes that support simulating novel future events by flexibly recombining elements of past experiences, and that can also give rise to memory errors. In recent studies, we have developed methods to characterize the cognitive and neural processes that support conscious experiences linked to this process of episodic recombination, both when people simulate novel future events and commit recombination-related memory errors. In this Perspective, we summarize recent studies that illustrate these phenomena, and discuss broader implications for characterizing the basis of conscious experiences associated with constructive memory from a cognitive neuroscience perspective.

Concretely Mapped Symbolic Memory Locations for Memory Error Detection

Concretely Mapped Symbolic Memory Locations for Memory Error Detection

Effects of agmatine on radial-arm maze memory performance and autistic-like behaviors in a male rat model of autism.

Autism spectrum disorder (ASD) is the fastest-growing child neuropsychiatric condition. Cognitive dysfunctions such as memory impairments are experienced by patients along with social disturbances and repetitive/stereotypic movements. We have used the radial arm maze (RAM), for measurement of working and reference memory errors in an animal model of autism. In addition, the potential effects of agmatine, an endogenous NMDA antagonist, on RAM performance and autistic-like behaviors were assessed. Autism was modeled by valproic acid (VPA) administration at gestational Day 12.5. Autism-associated behaviors in male offspring were examined in an open field test (OFT) and three-chambered test (TCT) on postnatal days 50-51. Thereafter, the animals were trained in the RAM (PND 55) until they attained the criteria of 80% correct choices during five consecutive trials. Forty-eight hours after the acquisition of criteria, agmatine was injected 30 min before subsequent behavioral testing, which included the retention phase of the RAM, OFT, and TCT. VPA-treated and intact rats showed the same performance in RAM, and acute injection of agmatine rescued social and anxiety-like behavior induced by VPA without the effect on RAM. In a rat model of autism, spatial learning, and memory did not change. Agmatine rescued social and anxiety-like behavior in autistic animals.

Female mice lacking membrane estrogen receptor alpha display impairments in spatial memory

Estrogens exert effects on cognition by acting on estrogen receptors (ER) including ERα. Activation of nuclear ERα results in classical genomic signaling leading to transcriptional changes that occur over hours to days. In contrast, activation of ERα localized to the membrane results in rapid signaling with effects occurring in seconds to minutes. The goal of the current study was to determine the role of membrane ERα in spatial memory. Female wildtype (WT) and transgenic mice that lack membrane ERα and express nuclear only ERα (NOER) were trained on an eight-arm radial-maze task. Following training, mice were tested on delay trials, in which delays ranging from 30 min to 5 h were inserted between the 4th and 5th arm choices. Performance was measured by number of proactive and retroactive errors. Proactive errors are short-term working memory errors defined by reentries into arms previously visited during the post-delay period or errors made during the pre-delay period. Retroactive errors are delay-dependent memory errors, defined as reentries into arms during the post-delay that were previously visited during the pre-delay. Consistent with a role for membrane ERα in rapid signaling, NOER mice made more proactive errors than WTs across all delays. NOER mice made more retroactive errors than WTs only after the 5-h delay. WT and NOER mice performed similarly on elevated plus maze and open field tests indicating no effects of membrane ERα on anxiety-related behavior or locomotor activity. Results reveal that membrane ERα plays important roles in both short-term and longer-term delay-dependent memory either directly or potentially indirectly through a role in the regulation of estradiol levels via the hypothalamic-pituitary-gonadal axis.

Toward effective secure code reviews: an empirical study of security-related coding weaknesses

Identifying security issues early is encouraged to reduce the latent negative impacts on the software systems. Code review is a widely-used method that allows developers to manually inspect modified code, catching security issues during a software development cycle. However, existing code review studies often focus on known vulnerabilities, neglecting coding weaknesses, which can introduce real-world security issues that are more visible through code review. The practices of code reviews in identifying such coding weaknesses are not yet fully investigated. To better understand this, we conducted an empirical case study in two large open-source projects, OpenSSL and PHP. Based on 135,560 code review comments, we found that reviewers raised security concerns in 35 out of 40 coding weakness categories. Surprisingly, some coding weaknesses related to past vulnerabilities, such as memory errors and resource management, were discussed less often than the vulnerabilities. Developers attempted to address raised security concerns in many cases (39%-41%), but a substantial portion was merely acknowledged (30%-36%), and some went unfixed due to disagreements about solutions (18%-20%). This highlights that coding weaknesses can slip through code review even when identified. Our findings suggest that reviewers can identify various coding weaknesses leading to security issues during code reviews. However, these results also reveal shortcomings in current code review practices, indicating the need for more effective mechanisms or support for increasing awareness of security issue management in code reviews.