Mnemonic Research Articles

Semantic-to-autobiographical memory priming occurs when stimuli are presented below the threshold of awareness

A number of studies have now shown that general information processing causes the activation of memories in the autobiographical memory system. These studies have shown that general processing of words, sounds, objects, or pictures primes autobiographical memories on voluntary and involuntary autobiographical memory tasks (the Crovitz cue-word task and the vigilance task). Deemed semantic-to-autobiographical memory priming, our goal in the current study was to demonstrate that this form of priming causes the unconscious activation of autobiographical memories (autobiographical automaticity) at the point of priming. Participants named words under subliminal and supraliminal conditions and then received a test of priming (the vigilance task). The results showed that words that were processed below the threshold of awareness were equally likely as words processed above the threshold of awareness to prime the production of involuntary autobiographical memories on the vigilance task. The results support the idea that autobiographical memory activations in semantic-to-autobiographical priming is both unintentional and unconscious.

Peer creation and sharing of mnemonics in collaborative documents for pathology education: a pilot study

BackgroundMnemonic techniques are memory aids that could help improve memory encoding, storage, and retrieval. Using the brain's natural propensity for pattern recognition and association, new information is associated with something familiar, such as an image, a structure, or a pattern. This should be particularly useful for learning complex medical information. Collaborative documents have the potential to revolutionize online learning because they could increase the creativity, productivity, and efficiency of learning. The purpose of this study was to investigate the feasibility of combining peer creation and sharing of mnemonics with collaborative online documents to improve pathology education.MethodsWe carried out a prospective, quasi-experimental, pretest–posttest pilot study. The intervention group was trained to create and share mnemonics in collaborative documents for pathological cases, based on histopathological slides. The control group compared analog and digital microscopy.ResultsBoth groups consisted of 41 students and did not reveal demographic differences. Performance evaluations did not reveal significant differences between the groups' pretest and posttest scores. Our pilot study revealed several pitfalls, especially in instructional design, time on task, and digital literacy, that could have masked possible learning benefits.ConclusionsThere is a gap in evidence-based research, both on mnemonics and on CD in pathology didactics. Even though, the combination of peer creation and sharing of mnemonics is very promising from a cognitive neurobiological standpoint, and collaborative documents have great potential to promote the digital transformation of medical education and increase cooperation, creativity, productivity, and efficiency of learning. However, the incorporation of such innovative techniques requires meticulous instructional design by teachers and additional time for students to become familiar with new learning methods and the application of new digital tools to promote also digital literacy. Future studies should also take into account validated high-stakes testing for more reliable pre-posttest results, a larger cohort of students, and anticipate technical difficulties regarding new digital tools.

Semantic-to-autobiographical memory priming: the role of stimulus processing.

It is now well established that semantic processing can cause the activation of memories in the autobiographical memory system. Studies have shown that semantic processing of words, sounds, objects, or pictures primes autobiographical memories on voluntary and involuntary autobiographical memory tasks (the Crovitz cue-word task and the vigilance task). Known as semantic-to-autobiographical memory priming, our goal in the current study was to demonstrate that this form of priming occurs under different forms of processing (i.e., shallow versus deep), and that some forms of processing (e.g., visual mental imagery) may enhance priming in this domain. In Experiment 1, equivalent semantic-to-autobiographical priming was obtained on the vigilance task following shallow (e-counting) and deep (meaning judgements) word processing. In Experiment 2, word meaning judgements were compared to visual imagery of word meanings, and visual imagery led to more semantic-to-autobiographical priming on the vigilance task than meaning judgements. The results of these experiments support the idea that semantic-to-autobiographical priming occurs under a wide range of processing conditions, supporting a ubiquity claim, with some conditions producing more priming than others, and they further support the idea that this form of may play an important role in the production of involuntary memories in everyday life.

Approaching scalable quantum memory with integrated atomic devices

Quantum memory, which maps photonic quantum information into a stationary medium and retrieves it at a chosen time, plays a vital role in the advancement of quantum information science. In particular, the scalability of a quantum memory is a central challenge for quantum network that can be overcome by using integrated devices. Quantum memory with an integrated device is highly appealing since it not only expands the number of memories to increase data rates, but also offers seamless compatibility with other on-chip devices and existing fiber network, enabling scalable and convenient applications. Over the past few decades, substantial efforts have been dedicated to achieving integrated quantum memory using rare earth ions doped solid-state materials, color centers, and atomic gases. These physical platforms are the primary candidates for such devices, where remarkable advantages have been demonstrated in achieving high-performance integrated quantum memory, paving the way for efficiently establishing robust and scalable quantum network with integrated quantum devices. In this paper, we aim to provide a comprehensive review of integrated quantum memory, encompassing its background and significance, advancement with bulky memory system, fabrication of integrated device, and its memory function considering various performance metrics. Additionally, we will address the challenges associated with integrated quantum memory and explore its potential applications. By analyzing the current state of the field, this review will make a valuable contribution by offering illustrative examples and providing helpful guidance for future achievements in practical integrated quantum memory.

Engineering of TiN/ZnO/SnO2/ZnO/Pt multilayer memristor with advanced electronic synapses and analog switching for neuromorphic computing

The two-terminal memristor is a promising neuromorphic artificial electronic device, mirroring biological synapses in structure and replicating various synaptic functions. Despite its advantages, challenges in achieving high reliability, gradual switching, and low energy consumption hinder progress in neuromorphic devices. This study explores electronic synapses and simulates analog switching in a Pt/TiN/ZnO/SnO2/ZnO/Pt multilayer (ML) configuration, featuring a ∼3 nm SnO2 layer between ZnO layers. Results show enhanced cycling endurance (more than 250 cycles), resistance window (102), tunable synaptic plasticity, and multilevel switching. ML memristors exhibit low coefficient of variation (4.5 %) in set voltage, low energy consumption (set = ∼0.12 nj, reset = ∼0.1 nj), and fast switching speeds (set = 300 ns, reset = 200 ns), suitable for high-density memory and neuromorphic systems. They successfully emulate synaptic functions, including paired-pulse facilitation (PPF), spike voltage-dependent plasticity (SVDP), spike width-dependent plasticity (SWDP), spike frequency-dependent plasticity (SFDP), and post-tetanic potentiation (PTP). Modulating pulse amplitude and width achieves multilevel conductance in long-term potentiation (LTP) and long-term depression (LTD). Using nonlinear conductance data, a 96.5 % image pattern recognition accuracy is achieved in a deconvolution neural network (DNN) simulation. These results highlight the ML memristor's potential in efficient neuromorphic computing systems.

Multilevel Conductance States of Vapor-Transport-Deposited Sb2S3 Memristors Achieved via Electrical and Optical Modulation.

The pursuit of advanced brain-inspired electronic devices and memory technologies has led to explore novel materials by processing multimodal and multilevel tailored conductive properties as the next generation of semiconductor platforms, due to von Neumann architecture limits. Among such materials, antimony sulfide (Sb2S3) thin films exhibit outstanding optical and electronic properties, and therefore, they are ideal for applications such as thin-film solar cells and nonvolatile memory systems. This study investigates the conduction modulation and memory functionalities of Sb2S3 thin films deposited via the vapor transport deposition technique. Experimental results indicate that the Ag/Sb2S3/Pt device possesses properties suitable for memory applications, including low operational voltages, robust endurance, and reliable switching behavior. Further, the reproducibility and stability of these properties across different device batches validate the reliability of these devices for practical implementation. Moreover, Sb2S3-based memristors exhibit artificial neuroplasticity with prolonged stability, promising considerable advancements in neuromorphic computing. Leveraging the photosensitivity of Sb2S3 enables the Ag/Sb2S3/Pt device to exhibit significant low operating potential and conductivity modulation under optical stimulation for memory applications. This research highlights the potential applications of Sb2S3 in future memory devices and optoelectronics and in shaping electronics with versatility.

Hippocampus and striatum show distinct contributions to longitudinal changes in value-based learning in middle childhood

The hippocampal-dependent memory system and striatal-dependent memory system modulate reinforcement learning depending on feedback timing in adults, but their contributions during development remain unclear. In a 2-year longitudinal study, 6-to-7-year-old children performed a reinforcement learning task in which they received feedback immediately or with a short delay following their response. Children’s learning was found to be sensitive to feedback timing modulations in their reaction time and inverse temperature parameter, which quantifies value-guided decision-making. They showed longitudinal improvements towards more optimal value-based learning, and their hippocampal volume showed protracted maturation. Better delayed model-derived learning covaried with larger hippocampal volume longitudinally, in line with the adult literature. In contrast, a larger striatal volume in children was associated with both better immediate and delayed model-derived learning longitudinally. These findings show, for the first time, an early hippocampal contribution to the dynamic development of reinforcement learning in middle childhood, with neurally less differentiated and more cooperative memory systems than in adults.

Hippocampus and striatum show distinct contributions to longitudinal changes in value-based learning in middle childhood.

The hippocampal-dependent memory system and striatal-dependent memory system modulate reinforcement learning depending on feedback timing in adults, but their contributions during development remain unclear. In a 2-year longitudinal study, 6-to-7-year-old children performed a reinforcement learning task in which they received feedback immediately or with a short delay following their response. Children's learning was found to be sensitive to feedback timing modulations in their reaction time and inverse temperature parameter, which quantifies value-guided decision-making. They showed longitudinal improvements towards more optimal value-based learning, and their hippocampal volume showed protracted maturation. Better delayed model-derived learning covaried with larger hippocampal volume longitudinally, in line with the adult literature. In contrast, a larger striatal volume in children was associated with both better immediate and delayed model-derived learning longitudinally. These findings show, for the first time, an early hippocampal contribution to the dynamic development of reinforcement learning in middle childhood, with neurally less differentiated and more cooperative memory systems than in adults.

Designing and Validating a Comprehensive Patient-Reported Outcomes Measure for Ambulatory Cancer Settings: The Revised Edmonton Symptom Assessment System for Cancer.

Patient-reported outcomes (PROs) information has been routinely collected in Cancer Care Alberta (CCA) for years using the revised Edmonton Symptom Assessment System (ESAS-r) and Canadian Problem Checklist (CPC). There was interest in combining these into a more comprehensive single measure tailored to ambulatory cancer settings. The purpose of this study was to validate an expanded and redesigned ESAS-r called the ESAS-r Cancer. Stakeholder engagement, a review of the literature, and 2 years of CPC data collected in the cancer program informed the addition of six symptoms to the ESAS-r. To assess and validate the measure, 1,600 randomly sampled patients were mailed paper copies of the ESAS-r Cancer, ESAS-r, and a validated, comprehensive PRO measure called the Memorial System Assessment Scale-Short Form (MSAS-SF), which is often used with patients with cancer. Canonical Correlation Analysis and exploratory factor analyses were performed to assess concurrent and construct validity of the ESAS-r Cancer against ESAS-r, using MSAS-SF as the reference measure for comparison. Cronbach α was calculated to assess reliability. Four hundred and sixty-one patients (29% response rate) completed all three questionnaires. ESAS-r Cancer showed higher numerical correlation than ESAS-r and accounted for more information included on MSAS-SF, explaining slightly more variance than ESAS-r (75.2% v 73.5%). The three-dimensional factor structure of ESAS-r Cancer outperformed the two-dimensional factor structure of ESAS-r. The reliability of ESAS-r Cancer was verified and found to be slightly higher than ESAS-r (Cronbach α = .903 v .884). ESAS-r Cancer is now in use with patients throughout CCA. This valid and reliable PRO measure can be used by other cancer or specialized health care programs who wish to routinely assess common symptoms.

Endel Tulving (1927-2023).

This article presents an obituary for Endel Tulving. Tulving's educational and professional careers are summarized. His work in the field of human memory is detailed. It is noted that Tulving's look at the field of verbal learning in the late 1950s persuaded him that the dominant associative tradition missed many important aspects of human memory. His research found that at the time of retrieval, memory for the original event may be successfully reinstated only by contextual cues that interact in a complementary fashion with the specifically encoded memory trace, a process that Tulving referred to as "synergistic ecphory". He is also known for his work on memory systems. In his book, Elements of Episodic Memory published in 1983, Tulving proposed that memory for experienced events, episodic memory, should be distinguished from general knowledge of the world, semantic memory, and from procedural memory, the learned ability to perform such skilled procedures as riding a bicycle or playing a musical instrument. He also proposed an evolutionary framework for these different but related systems, suggesting that simple animals show only procedural memory, more complex animals are consciously aware of their knowledge of the world, but only humans possess episodic memory-the ability to use "mental time travel" to consciously recreate past experiences and to imagine possible future events. Although known initially for his purely cognitive behavioral research, during the 1980s and 1990s, Tulving increasingly incorporated neuropsychological and neuroimaging approaches into his work. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Two Byzantine Medical Texts in Verse Translated into English: Diagnosis and Treatment of Disease

This article aims to provide an English translation of two particularly popular Byzantine medical texts in verse, focusing on diagnosis by the examination of venesected blood and urine. Furthermore, these texts provide valuable therapeutic advice, especially for the use of drugs. They are composed in the form of liturgical hymns, combining mnemonic techniques. These hymns survive in various recensions and are often ascribed either to Nikephoros Blemmydes or Maximos Planoudes, both late Byzantine intellectuals and renowned teachers of advanced educational programs in Nicaea and Constantinople, respectively.

Mathematical problem solving in emergent bilingual children: Is growth related to the navigation between two working memory systems?

Mathematical problem solving in emergent bilingual children: Is growth related to the navigation between two working memory systems?

Portable Medication Reminder System for Elderly and Alzheimer's Patients

As healthcare demands rise and the senior population grows, innovative results are essential for cases with habitual ails and memory-related conditions like Alzheimer's. Introduces a movable drug memorial System designed for hospitals, old age homes, and independent elderly citizens. The system, erected around an Arduino UNO microcontroller, integrates an IR detector, buzzer, TV display, RTC module, keypad, LED, and GSM module, working together to give timely drug monuments. In hospitals, it integrates into patient care routines, driving visual and audile monuments for nursing staff to insure timely drug delivery. This integration helps drug crimes and ensures that cases are cleaved to their prescribed treatment plans. Old age homes profit from the stonerfriendly device, allowing residents to manage their drug schedules via the TV display and keypad. The system's simplicity and trust ability can significantly ameliorate the quality of life for senior residents. For independent seniors, the movable system provides monuments through the TV display and buzzer, with the GSM module transferring SMS cautions to family members or caregivers if boluses are missed. This point is particularly precious for those who live alone, offering peace of mind to both the seniors and their families. Alzheimer's cases, floundering with memory loss and drug adherence, gain significantly from this system. The combination of visual and audile monuments, along with the straightforward keypad and TV display, offers an effective tool for managing their drug needs. By icing timely drug input, alleviates the progression of symptoms and enhances the overall well-being of Alzheimer's cases.

Inkjet‐Printed Phase Change Memory Devices

AbstractPhase change memory (PCM) is among the most promising candidates for the next generation of storage‐class and main memory systems in the computing era beyond Moore's law. However, the widespread installment of PCM devices is limited by the high price‐per‐bit and complex fabrication process. In this paper, it is shown that functional PCM memory devices can be printed, proving low‐cost avenues for non‐silicon memory technologies. Taking Ge‐Sb‐Te (GST) as a case study, PCM inks are prepared and optimize their structural, rheological, and printing parameters. GST layers are then printed onto PCM devices in the planar configuration, showing excellent performance, such as non‐volatility, resistivity contrast, low cycle‐to‐cycle variability, and endurance of at least 100 cycles. This paper provides a novel approach to liquid‐based engineered PCM devices through inkjet printing, enabling patterned memory layers, lower price‐per‐bit, and customizable multi‐material PCM arrays.

Exploring Memory Systems After Pediatric Posterior Fossa Tumor: From Memory Profile Comparisons in Nonirradiated Versus Irradiated Patients to Episodic Memory Tests Capable of Detecting Radiation-induced Hippocampal Damage

AimsPediatric posterior fossa tumor (PFT) survivors experience long-term cognitive sequelae, including memory disorders, for which irradiation is one of the main risk factors. The aims of the present study were to (1) explore the profile of impairment in episodic, semantic, working and procedural memory systems in irradiated versus nonirradiated PFT survivors, and (2) test whether an autobiographical questionnaire and a two-phase ecological test (Epireal) assessing episodic memory are more sensitive to radiation-induced hippocampal damage than commonly used tests. Materials and methodsA total of 60 participants (22 irradiated PFT survivors, 17 nonirradiated PFT survivors, and 21 controls) were included in the prospective IMPALA study. They all underwent a broad battery of tests assessing the different memory systems in two 2-day sessions 3 weeks apart. We performed between-groups comparisons and analyzed impairment profiles, using -1.65 SDs as a cut-off. For irradiated patients, correlations were calculated between mean radiation doses to key brain structures involved in memory (hippocampus, cerebellum, and striatum) and corresponding memory scores. ResultsPBT survivors performed significantly more poorly than controls (p < 0.001) on conventional tests of episodic, semantic and working memory: 64% of irradiated patients and 35% of nonirradiated patients had a deficit in at least two memory systems, with episodic memory impairment being more specific to the irradiated group. Epireal had a larger effect size than the other episodic memory tests, allowing us to detect deficits in a further 18% of irradiated patients. These deficits were correlated with the mean radiation dose to the left hippocampus. ConclusionMemory impairment is a frequent long-term cognitive sequela in PFT survivors, especially after radiation therapy. New ecological tests of episodic memory that are more sensitive to radiation-induced deficits than conventional tests could yield specific markers of the toxicity of medial temporal lobe irradiation.

Novel coumarin-based ratiometric bifunctional fluorescent probe mimicking a set-reset memorized device

A novel coumarin-based fluorescent sensor CHE, incorporating 2-hydrazinylbenzothiazole and coumarin aldehyde, has been developed that demonstrated a preferential detection of Hg2+ and Ag+ in presence of interferences. Compared to previously prevalent intensity-based fluorescent probes, CHE exhibited a ratiometric fluorescence response to Hg2+ and Ag+, and further accurately differentiated Hg2+ and Ag + using the differential extractive ability of EDTA when interacting with ion-CHE complexes. Sensing mechanism was investigated and elucidated. The chemosensor CHE was successfully applied to detect Hg2+ and Ag+ in six distinct samples with satisfactory results. Additionally, combinatorial logic circuits were constructed utilizing three distinct logic gates (NOT, OR, and INH) based on the sensor's differential output signals in response to Hg2+/Ag+ and other cations. Interestingly, utilizing the reversible and reproducible switching behavior of the EDTA interaction with Hg2+, a conceptual 'Write–Read–Erase–Read' memory function with multi-write capability was proposed, offering a novel perspective for molecular-based memory systems.

Task Termination Triggers Spontaneous Removal of Information From Visual Working Memory

Working memory (WM) is a goal-directed memory system that actively maintains a limited amount of task-relevant information to serve the current goal. By this definition, WM maintenance should be terminated after the goal is accomplished, spontaneously removing no-longer-relevant information from WM. Past studies have failed to provide direct evidence of spontaneous removal of WM content by allowing participants to engage in a strategic reallocation of WM resources to competing information within WM. By contrast, we provide direct neural and behavioral evidence that visual WM content can be largely removed less than 1 s after it becomes obsolete, in the absence of a strategic allocation of resources (total N = 442 adults). These results demonstrate that visual WM is intrinsically a goal-directed system, and spontaneous removal provides a means for capacity-limited WM to keep up with ever-changing demands in a dynamic environment.

Learning in the wild – exploring interactive notifications to foster organic retention of everyday media content

ABSTRACT The amount of information we consume daily through smartphones has prompted the development of Personal Knowledge Management Systems (PKMS). However, these external memory aids can lead to overreliance and do not help to keep information in our organic memory. This paper presents the design and evaluation of MemoryMate, an app that fosters recall of saved content through interactive notifications. The app's design is based on insights from a focus group (N = 7) that inquired about people's expectations and preferences regarding the notification design. The results of a three-week in-the-wild study (N = 23) of MemoryMate suggest that notifications could trigger engagement with saved content, and two-thirds of participants felt notifications improved their memory and retention. Retrieval practice notifications with interactive tasks performed well and received positive feedback. Content priority and time since the last interaction influenced notification engagement, emphasising the need for periodic reminders to maintain user interest in saved content. We discuss potential implications for the design of PKMSs as a tool for lifelong learning.

SpChar: Characterizing the sparse puzzle via decision trees

Sparse matrix computation is crucial in various modern applications, including large-scale graph analytics, deep learning, and recommender systems. The performance of sparse kernels varies greatly depending on the structure of the input matrix, making it difficult to gain a comprehensive understanding of sparse computation and its relationship to inputs, algorithms, and target machine architecture. Despite extensive research on certain sparse kernels, such as Sparse Matrix-Vector Multiplication (SpMV), the overall family of sparse algorithms has yet to be investigated as a whole. This paper introduces SpChar, a workload characterization methodology for general sparse computation. SpChar employs tree-based models to identify the most relevant hardware and input characteristics, starting from hardware and input-related metrics gathered from Performance Monitoring Counters (PMCs) and matrices. Our analysis enables the creation of a characterization loop that facilitates the optimization of sparse computation by mapping the impact of architectural features to inputs and algorithmic choices. We apply SpChar to more than 600 matrices from the SuiteSparse Matrix collection and three state-of-the-art Arm Central Processing Units (CPUs) to determine the critical hardware and software characteristics that affect sparse computation. In our analysis, we determine that the biggest limiting factors for high-performance sparse computation are (1) the latency of the memory system, (2) the pipeline flush overhead resulting from branch misprediction, and (3) the poor reuse of cached elements. Additionally, we propose software and hardware optimizations that designers can implement to create a platform suitable for sparse computation. We then investigate these optimizations using the gem5 simulator to achieve a significant speedup of up to 2.63× compared to a CPU where the optimizations are not applied.

Advanced Prognostic Models for Bearing Health: A Comparative Analysis of BiLSTM and ANFIS

Bearings play a critical role in the operation of rotary machines, serving as essential components. Their failure often leads to unexpected shutdowns, posing a significant risk to the entire system. To mitigate these risks, it is imperative to implement proactive maintenance measures and strategic planning to prevent system breakdowns. This article introduces a comparative analysis between two predictive modelling approaches: Bidirectional Long Short-Term Memory (Bi-LSTM) and Adaptive Neuro Fuzzy Inference System (ANFIS) networks, aiming to enhance bearing prognostics. The proposed methodology involves a two-step process. Firstly, data undergoes pre-processing through wavelet packet decomposition (WPD). Subsequently, a degradation model is employed for predicting the remaining useful life (RUL). To validate the accuracy of the proposed approach, extensive testing is conducted using a bearing's life dataset obtained from a run-to-failure experiment. The results demonstrate that the ANFIS model exhibits remarkable capabilities in learning and accurately estimating the system's RUL, achieving this with minimal computation time compared to alternative methods, thus presenting a more efficient and precise solution.