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Fulltext Examining the roles of visual imagery and working memory in the retrieval of autobiographical memories using a dual-task paradigm

Anthony K, Wong HK, Lim A, Sow F, Janssen SM

Q J Exp Psychol (Hove), 2024 Mar;77(3):447-460.
PMID: 37649149 DOI: 10.1177/17470218231200724

The retrieval of autobiographical memories involves the construction of mental representations of past personal events. Many researchers examining the processes underlying memory retrieval argue that visual imagery plays a fundamental role. Other researchers, however, have argued that working memory is an integral component involved in memory retrieval. The goal of this study was to resolve these conflicting arguments by comparing the relative contributions of visual imagery and working memory during the retrieval of autobiographical memories in a dual-task paradigm. While following a moving dot, viewing a dynamic visual noise (DVN), or viewing a blank screen, 95 participants recalled their memories and subsequently rated them on different memory characteristics. The results suggest that inhibiting visual imagery by having participants view DVN merely delayed memory retrieval but did not affect the phenomenological quality of the memories retrieved. Taxations to the working memory by having participants follow a moving dot, on the contrary, resulted in only longer retrieval latencies and no reductions in the specificity, vividness, or the emotional intensity of the memories retrieved. Whereas the role of visual imagery during retrieval is clear, future studies could further examine the role of working memory during retrieval by administering a task that is less difficult or by recruiting a larger sample than this study. The results of this study seem to suggest that both visual imagery and working memory play a role during the retrieval of autobiographical memory, but more research needs to be conducted to determine their exact roles.

Matched MeSH terms: Memory, Short-Term; Memory, Episodic*
Fulltext Tuning attention based long-short term memory neural networks for Parkinson's disease detection using modified metaheuristics

Cuk A, Bezdan T, Jovanovic L, Antonijevic M, Stankovic M, Simic V, et al.

Sci Rep, 2024 Feb 21;14(1):4309.
PMID: 38383690 DOI: 10.1038/s41598-024-54680-y

Parkinson's disease (PD) is a progressively debilitating neurodegenerative disorder that primarily affects the dopaminergic system in the basal ganglia, impacting millions of individuals globally. The clinical manifestations of the disease include resting tremors, muscle rigidity, bradykinesia, and postural instability. Diagnosis relies mainly on clinical evaluation, lacking reliable diagnostic tests and being inherently imprecise and subjective. Early detection of PD is crucial for initiating treatments that, while unable to cure the chronic condition, can enhance the life quality of patients and alleviate symptoms. This study explores the potential of utilizing long-short term memory neural networks (LSTM) with attention mechanisms to detect Parkinson's disease based on dual-task walking test data. Given that the performance of networks is significantly inductance by architecture and training parameter choices, a modified version of the recently introduced crayfish optimization algorithm (COA) is proposed, specifically tailored to the requirements of this investigation. The proposed optimizer is assessed on a publicly accessible real-world clinical gait in Parkinson's disease dataset, and the results demonstrate its promise, achieving an accuracy of 87.4187 % for the best-constructed models.

Matched MeSH terms: Memory, Short-Term
The flashbulb-like nature of memory for the first COVID-19 case and the impact of the emergency. A cross-national survey

Lanciano T, Alfeo F, Curci A, Marin C, D'Uggento AM, Decarolis D, et al.

Memory, 2024 Feb;32(2):264-282.
PMID: 38315731 DOI: 10.1080/09658211.2024.2310554

Flashbulb memories (FBMs) refer to vivid and long-lasting autobiographical memories for the circumstances in which people learned of a shocking and consequential public event. A cross-national study across eleven countries aimed to investigate FBM formation following the first COVID-19 case news in each country and test the effect of pandemic-related variables on FBM. Participants had detailed memories of the date and others present when they heard the news, and had partially detailed memories of the place, activity, and news source. China had the highest FBM specificity. All countries considered the COVID-19 emergency as highly significant at both the individual and global level. The Classification and Regression Tree Analysis revealed that FBM specificity might be influenced by participants' age, subjective severity (assessment of COVID-19 impact in each country and relative to others), residing in an area with stringent COVID-19 protection measures, and expecting the pandemic effects. Hierarchical regression models demonstrated that age and subjective severity negatively predicted FBM specificity, whereas sex, pandemic impact expectedness, and rehearsal showed positive associations in the total sample. Subjective severity negatively affected FBM specificity in Turkey, whereas pandemic impact expectedness positively influenced FBM specificity in China and negatively in Denmark.

Matched MeSH terms: Memory, Episodic*
Fulltext Forecasting stock prices changes using long-short term memory neural network with symbolic genetic programming

Li Q, Kamaruddin N, Yuhaniz SS, Al-Jaifi HAA

Sci Rep, 2024 Jan 03;14(1):422.
PMID: 38172568 DOI: 10.1038/s41598-023-50783-0

This study introduces an augmented Long-Short Term Memory (LSTM) neural network architecture, integrating Symbolic Genetic Programming (SGP), with the objective of forecasting cross-sectional price returns across a comprehensive dataset comprising 4500 listed stocks in the Chinese market over the period from 2014 to 2022. Using the S&P Alpha Pool Dataset for China as basic input, this architecture incorporates data augmentation and feature extraction techniques. The result of this study demonstrates significant improvements in Rank Information coefficient (Rank IC) and IC information ratio (ICIR) by 1128% and 5360% respectively when it is applied to fundamental indicators. For technical indicators, the hybrid model achieves a 206% increase in Rank IC and an impressive surge of 2752% in ICIR. Furthermore, the proposed hybrid SGP-LSTM model outperforms major Chinese stock indexes, generating average annualized excess returns of 31.00%, 24.48%, and 16.38% compared to the CSI 300 index, CSI 500 index, and the average portfolio, respectively. These findings highlight the effectiveness of SGP-LSTM model in improving the accuracy of cross-sectional stock return predictions and provide valuable insights for fund managers, traders, and financial analysts.

Matched MeSH terms: Memory, Short-Term*; Memory, Long-Term
Fulltext Leveraging transfer learning with deep learning for crime prediction

Butt UM, Letchmunan S, Hassan FH, Koh TW

PLoS One, 2024;19(4):e0296486.
PMID: 38630687 DOI: 10.1371/journal.pone.0296486

Crime remains a crucial concern regarding ensuring a safe and secure environment for the public. Numerous efforts have been made to predict crime, emphasizing the importance of employing deep learning approaches for precise predictions. However, sufficient crime data and resources for training state-of-the-art deep learning-based crime prediction systems pose a challenge. To address this issue, this study adopts the transfer learning paradigm. Moreover, this study fine-tunes state-of-the-art statistical and deep learning methods, including Simple Moving Averages (SMA), Weighted Moving Averages (WMA), Exponential Moving Averages (EMA), Long Short Term Memory (LSTM), Bi-directional Long Short Term Memory (BiLSTMs), and Convolutional Neural Networks and Long Short Term Memory (CNN-LSTM) for crime prediction. Primarily, this study proposed a BiLSTM based transfer learning architecture due to its high accuracy in predicting weekly and monthly crime trends. The transfer learning paradigm leverages the fine-tuned BiLSTM model to transfer crime knowledge from one neighbourhood to another. The proposed method is evaluated on Chicago, New York, and Lahore crime datasets. Experimental results demonstrate the superiority of transfer learning with BiLSTM, achieving low error values and reduced execution time. These prediction results can significantly enhance the efficiency of law enforcement agencies in controlling and preventing crime.

Matched MeSH terms: Memory, Long-Term
Fulltext Protective effects of Centella asiatica extract on spatial memory and learning deficits in animal model of systemic inflammation induced by lipopolysaccharide

Md Pisar M, Chee BJ, Long I, Osman A

Ann Med, 2023 Dec;55(1):2224970.
PMID: 37318144 DOI: 10.1080/07853890.2023.2224970

BACKGROUND AND AIM: Centella asiatica (L.) Urb. (Apiaceae) is a renowned medicinal plant being used in the Ayurvedic system for its pharmacological effects on the central nervous system such as rejuvenating, sedative, anxiolytic and memory-enhancing properties. The present study was designed to investigate the effect of Centella asiatica (CA) extract on inflammatory responses induced by lipopolysaccharide (LPS) and resulting changes in cognitive behavior.
MATERIALS AND METHODS: Adult male Sprague-Dawley rats were divided into 4 groups as control, LPS, CA and LPS + CA. The treatments with LPS (5 mg/kg) were intraperitoneally (i.p) injected on day 4 and CA ethanol extract (200 mg/kg) were given orally for 14 days. Morris Water Maze (MWM) test was performed to assess spatial learning and memory performance. Acute oral toxicity of the extract at the highest dose of 5000 mg/kg was also conducted.
RESULTS: Single administration of LPS was able to significantly elicit learning and memory impairment (p

Matched MeSH terms: Spatial Memory*
A Novel Classification Model Using Optimal Long Short-Term Memory for Classification of COVID-19 from CT Images

Vinothini R, Niranjana G, Yakub F

J Digit Imaging, 2023 Dec;36(6):2480-2493.
PMID: 37491543 DOI: 10.1007/s10278-023-00852-7

The human respiratory system is affected when an individual is infected with COVID-19, which became a global pandemic in 2020 and affected millions of people worldwide. However, accurate diagnosis of COVID-19 can be challenging due to small variations in typical and COVID-19 pneumonia, as well as the complexities involved in classifying infection regions. Currently, various deep learning (DL)-based methods are being introduced for the automatic detection of COVID-19 using computerized tomography (CT) scan images. In this paper, we propose the pelican optimization algorithm-based long short-term memory (POA-LSTM) method for classifying coronavirus using CT scan images. The data preprocessing technique is used to convert raw image data into a suitable format for subsequent steps. Here, we develop a general framework called no new U-Net (nnU-Net) for region of interest (ROI) segmentation in medical images. We apply a set of heuristic guidelines derived from the domain to systematically optimize the ROI segmentation task, which represents the dataset's key properties. Furthermore, high-resolution net (HRNet) is a standard neural network design developed for feature extraction. HRNet chooses the top-down strategy over the bottom-up method after considering the two options. It first detects the subject, generates a bounding box around the object and then estimates the relevant feature. The POA is used to minimize the subjective influence of manually selected parameters and enhance the LSTM's parameters. Thus, the POA-LSTM is used for the classification process, achieving higher performance for each performance metric such as accuracy, sensitivity, F1-score, precision, and specificity of 99%, 98.67%, 98.88%, 98.72%, and 98.43%, respectively.

Matched MeSH terms: Memory, Short-Term
Fulltext Prediction of dengue cases using the attention-based long short-term memory (LSTM) approach

Majeed MA, Shafri HZM, Wayayok A, Zulkafli Z

Geospat Health, 2023 May 25;18(1).
PMID: 37246539 DOI: 10.4081/gh.2023.1176

This research proposes a 'temporal attention' addition for long-short term memory (LSTM) models for dengue prediction. The number of monthly dengue cases was collected for each of five Malaysian states i.e. Selangor, Kelantan, Johor, Pulau Pinang, and Melaka from 2011 to 2016. Climatic, demographic, geographic and temporal attributes were used as covariates. The proposed LSTM models with temporal attention was compared with several benchmark models including a linear support vector machine (LSVM), a radial basis function support vector machine (RBFSVM), a decision tree (DT), a shallow neural network (SANN) and a deep neural network (D-ANN). In addition, experiments were conducted to analyze the impact of look-back settings on each model performance. The results showed that the attention LSTM (A-LSTM) model performed best, with the stacked, attention LSTM (SA-LSTM) one in second place. The LSTM and stacked LSTM (S-LSTM) models performed almost identically but with the accuracy improved by the attention mechanism was added. Indeed, they were both found to be superior to the benchmark models mentioned above. The best results were obtained when all attributes were included in the model. The four models (LSTM, S-LSTM, A-LSTM and SA-LSTM) were able to accurately predict dengue presence 1-6 months ahead. Our findings provide a more accurate dengue prediction model than previously used, with the prospect of also applying this approach in other geographic areas.

Matched MeSH terms: Memory, Short-Term*
Fulltext Multi-View Human Action Recognition Using Skeleton Based-FineKNN with Extraneous Frame Scrapping Technique

Malik NUR, Sheikh UU, Abu-Bakar SAR, Channa A

Sensors (Basel), 2023 Mar 02;23(5).
PMID: 36904953 DOI: 10.3390/s23052745

Human action recognition (HAR) is one of the most active research topics in the field of computer vision. Even though this area is well-researched, HAR algorithms such as 3D Convolution Neural Networks (CNN), Two-stream Networks, and CNN-LSTM (Long Short-Term Memory) suffer from highly complex models. These algorithms involve a huge number of weights adjustments during the training phase, and as a consequence, require high-end configuration machines for real-time HAR applications. Therefore, this paper presents an extraneous frame scrapping technique that employs 2D skeleton features with a Fine-KNN classifier-based HAR system to overcome the dimensionality problems.To illustrate the efficacy of our proposed method, two contemporary datasets i.e., Multi-Camera Action Dataset (MCAD) and INRIA Xmas Motion Acquisition Sequences (IXMAS) dataset was used in experiment. We used the OpenPose technique to extract the 2D information, The proposed method was compared with CNN-LSTM, and other State of the art methods. Results obtained confirm the potential of our technique. The proposed OpenPose-FineKNN with Extraneous Frame Scrapping Technique achieved an accuracy of 89.75% on MCAD dataset and 90.97% on IXMAS dataset better than existing technique.

Matched MeSH terms: Memory, Long-Term
The effects of search-irrelevant working memory content on visual search

Calleja MO, Willoughby AR

Atten Percept Psychophys, 2023 Feb;85(2):293-300.
PMID: 36596986 DOI: 10.3758/s13414-022-02634-9

Previous experiments investigating visual search have shown that distractors that are semantically related to a search target can capture attention and slow the search process. In two experiments, we examine if distractors exactly matching, or semantically related to, search-irrelevant information held in working memory (WM) can also influence visual search while ruling out potential effects of color similarity. Participants first viewed and memorized an image of an everyday object, then they determined if a target item was present or absent in a two-object search array. On exact-match trials, the memorized object appeared as a distractor; on semantic-match trials, an object semantically related to the memorized object appeared as a distractor. Both exact-match and semantic-match distractors slowed search when the target was present in the search array. Our findings extend previous findings by demonstrating WM-driven attentional guidance by complex objects rather than simple features. The results also suggest that visual search can be influenced by distractors sharing only semantic features with a search-irrelevant, but active, WM representation.

Matched MeSH terms: Memory, Short-Term*
Fulltext The Effect of Dataset Imbalance on the Performance of SCADA Intrusion Detection Systems

Balla A, Habaebi MH, Elsheikh EAA, Islam MR, Suliman FM

Sensors (Basel), 2023 Jan 09;23(2).
PMID: 36679553 DOI: 10.3390/s23020758

Integrating IoT devices in SCADA systems has provided efficient and improved data collection and transmission technologies. This enhancement comes with significant security challenges, exposing traditionally isolated systems to the public internet. Effective and highly reliable security devices, such as intrusion detection system (IDSs) and intrusion prevention systems (IPS), are critical. Countless studies used deep learning algorithms to design an efficient IDS; however, the fundamental issue of imbalanced datasets was not fully addressed. In our research, we examined the impact of data imbalance on developing an effective SCADA-based IDS. To investigate the impact of various data balancing techniques, we chose two unbalanced datasets, the Morris power dataset, and CICIDS2017 dataset, including random sampling, one-sided selection (OSS), near-miss, SMOTE, and ADASYN. For binary classification, convolutional neural networks were coupled with long short-term memory (CNN-LSTM). The system's effectiveness was determined by the confusion matrix, which includes evaluation metrics, such as accuracy, precision, detection rate, and F1-score. Four experiments on the two datasets demonstrate the impact of the data imbalance. This research aims to help security researchers in understanding imbalanced datasets and their impact on DL SCADA-IDS.

Matched MeSH terms: Memory, Long-Term
COGMED working memory training in children with Attention Deficit/Hyperactivity Disorder (ADHD): A feasibility study in Saudi Arabia

Almarzouki AF, Bellato A, Al-Saad MS, Al-Jabri B

Appl Neuropsychol Child, 2023;12(3):202-213.
PMID: 35549563 DOI: 10.1080/21622965.2022.2070020

Working memory training has been proven effective for improving cognitive functioning in patients with Attention Deficit/Hyperactivity Disorder (ADHD). However, the feasibility of this type of training for children in Saudi Arabia has not been previously explored. We investigated the feasibility of implementing Cogmed Working Memory Training (CWMT) in a sample of 29 Saudi children with ADHD. We found no significant demographic or clinical differences between compliant and noncompliant children. Although compliant children were initially better at following instructions and reported better improvements in working memory and math skills compared to those who did not complete the CWMT, all children who participated in the program showed improvements in performing the CWMT tasks. Most parents found the Cogmed training feasible for their children, were satisfied and keen to continue with the program, and felt the training helped them to address their problems. Most children did not encounter any difficulties in using the software, and many families were, therefore, likely to continue using the techniques from the program. We conclude that CWMT for children with ADHD is feasible in Saudi Arabia. Larger case-controlled studies are needed to thoroughly investigate the effects of CWMT compared to other interventions in Saudi children with ADHD.

Matched MeSH terms: Memory, Short-Term*
Developments in the functions of autobiographical memory: An advanced review

Sow F, Dijkstra K, Janssen SMJ

Wiley Interdiscip Rev Cogn Sci, 2023;14(3):e1625.
PMID: 36165349 DOI: 10.1002/wcs.1625

In this advanced review, the development of the three most commonly used functions of autobiographical memory-directing behavior, social bonding, and self-continuity-and the support they have received in the literature are discussed. Support for this tripartite model often comes from correlational studies that use self-report measures, but participants in these studies may not be aware that they retrieved autobiographical memories to fulfill certain goals. Not only is more experimental research needed to confirm the findings from correlational studies, this kind of research needs to be more rigorous. Moreover, the functions of the tripartite model may not be the only autobiographical memory functions that can be distinguished. For example, there is already substantial support for the emotion-regulation function. Although memories can be used for multiple functions, patterns between aspects of the event (e.g., emotional valence) or memory (e.g., specificity) and their functionality have been found. In addition, individual differences (e.g., cultural background, depression symptoms) and situational factors (e.g., is there a goal that needs to be fulfilled) may regulate the functional deployment of autobiographical memories. Future research should therefore extend its focus on the conditions in which these functions can be observed. This article is categorized under: Psychology > Memory.

Matched MeSH terms: Memory, Episodic*
Fulltext Fake news stance detection using selective features and FakeNET

Aljrees T, Cheng X, Ahmed MM, Umer M, Majeed R, Alnowaiser K, et al.

PLoS One, 2023;18(7):e0287298.
PMID: 37523404 DOI: 10.1371/journal.pone.0287298

The proliferation of fake news has severe effects on society and individuals on multiple fronts. With fast-paced online content generation, has come the challenging problem of fake news content. Consequently, automated systems to make a timely judgment of fake news have become the need of the hour. The performance of such systems heavily relies on feature engineering and requires an appropriate feature set to increase performance and robustness. In this context, this study employs two methods for reducing the number of feature dimensions including Chi-square and principal component analysis (PCA). These methods are employed with a hybrid neural network architecture of convolutional neural network (CNN) and long short-term memory (LSTM) model called FakeNET. The use of PCA and Chi-square aims at utilizing appropriate feature vectors for better performance and lower computational complexity. A multi-class dataset is used comprising 'agree', 'disagree', 'discuss', and 'unrelated' classes obtained from the Fake News Challenges (FNC) website. Further contextual features for identifying bogus news are obtained through PCA and Chi-Square, which are given nonlinear characteristics. The purpose of this study is to locate the article's perspective concerning the headline. The proposed approach yields gains of 0.04 in accuracy and 0.20 in the F1 score, respectively. As per the experimental results, PCA achieves a higher accuracy of 0.978 than both Chi-square and state-of-the-art approaches.

Matched MeSH terms: Memory, Long-Term
Fulltext Monkeypox genome mutation analysis using a timeseries model based on long short-term memory

Pathan RK, Uddin MA, Paul AM, Uddin MI, Hamd ZY, Aljuaid H, et al.

PLoS One, 2023;18(8):e0290045.
PMID: 37611023 DOI: 10.1371/journal.pone.0290045

Monkeypox is a double-stranded DNA virus with an envelope and is a member of the Poxviridae family's Orthopoxvirus genus. This virus can transmit from human to human through direct contact with respiratory secretions, infected animals and humans, or contaminated objects and causing mutations in the human body. In May 2022, several monkeypox affected cases were found in many countries. Because of its transmitting characteristics, on July 23, 2022, a nationwide public health emergency was proclaimed by WHO due to the monkeypox virus. This study analyzed the gene mutation rate that is collected from the most recent NCBI monkeypox dataset. The collected data is prepared to independently identify the nucleotide and codon mutation. Additionally, depending on the size and availability of the gene dataset, the computed mutation rate is split into three categories: Canada, Germany, and the rest of the world. In this study, the genome mutation rate of the monkeypox virus is predicted using a deep learning-based Long Short-Term Memory (LSTM) model and compared with Gated Recurrent Unit (GRU) model. The LSTM model shows "Root Mean Square Error" (RMSE) values of 0.09 and 0.08 for testing and training, respectively. Using this time series analysis method, the prospective mutation rate of the 50th patient has been predicted. Note that this is a new report on the monkeypox gene mutation. It is found that the nucleotide mutation rates are decreasing, and the balance between bi-directional rates are maintained.

Matched MeSH terms: Memory, Short-Term
Fulltext Associations between social connections and cognition: a global collaborative individual participant data meta-analysis

Samtani S, Mahalingam G, Lam BCP, Lipnicki DM, Lima-Costa MF, Blay SL, et al.

Lancet Healthy Longev, 2022 Nov;3(11):e740-e753.
PMID: 36273484 DOI: 10.1016/S2666-7568(22)00199-4

BACKGROUND: Poor social connections (eg, small networks, infrequent interactions, and loneliness) are modifiable risk factors for cognitive decline. Existing meta-analyses are limited by reporting aggregate responses, a focus on global cognition, and combining social measures into single constructs. We aimed to investigate the association between social connection markers and the rate of annual change in cognition (ie, global and domain-specific), as well as sex differences, using an individual participant data meta-analysis.
METHODS: We harmonised data from 13 longitudinal cohort studies of ageing in North America, South America, Europe, Africa, Asia, and Australia. Studies were eligible for inclusion if they had baseline data for social connection markers and at least two waves of cognitive scores. Follow-up periods ranged from 0 years to 15 years across cohorts. We included participants with cognitive data for at least two waves and social connection data for at least one wave. We then identified and excluded people with dementia at baseline. Primary outcomes were annual rates of change in global cognition and cognitive domain scores over time until final follow-up within each cohort study analysed by use of an individual participant data meta-analysis. Linear mixed models within cohorts used baseline social connection markers as predictors of the primary outcomes. Effects were pooled in two stages using random-effects meta-analyses. We assessed the primary outcomes in the main (partially adjusted) and fully adjusted models. Partially adjusted models controlled for age, sex, and education; fully adjusted models additionally controlled for diabetes, hypertension, smoking, cardiovascular risk, and depression.
FINDINGS: Of the 40 006 participants in the 13 cohort studies, we excluded 1392 people with dementia at baseline. 38 614 individual participants were included in our analyses. For the main models, being in a relationship or married predicted slower global cognitive decline (b=0·010, 95% CI 0·000-0·019) than did being single or never married; living with others predicted slower global cognitive (b=0·007, 0·002-0·012), memory (b=0·017, 0·006-0·028), and language (b=0·008, 0·000-0·015) decline than did living alone; and weekly interactions with family and friends (b=0·016, 0·006-0·026) and weekly community group engagement (b=0·030, 0·007-0·052) predicted slower memory decline than did no interactions and no engagement. Never feeling lonely predicted slower global cognitive (b=0·047, 95% CI 0·018-0·075) and executive function (b=0·047, 0·017-0·077) decline than did often feeling lonely. Degree of social support, having a confidante, and relationship satisfaction did not predict cognitive decline across global cognition or cognitive domains. Heterogeneity was low (I2=0·00-15·11%) for all but two of the significant findings (association between slower memory decline and living with others [I2=58·33%] and community group engagement, I2=37·54-72·19%), suggesting robust results across studies.
INTERPRETATION: Good social connections (ie, living with others, weekly community group engagement, interacting weekly with family and friends, and never feeling lonely) are associated with slower cognitive decline.
FUNDING: EU Joint Programme-Neurodegenerative Disease Research grant, funded by the National Health and Medical Research Council Australia, and the US National Institute on Aging of the US National Institutes of Health.

Matched MeSH terms: Memory Disorders
Views on Memory and Treatment of Cognitive Impairment in 17th-Century Spain: Juan Gutiérrez de Godoy's Work

Palma JA, Palma F

Neurology, 2022 Sep 05;99(10):424-427.
PMID: 35794022 DOI: 10.1212/WNL.0000000000201025

Memory and its care were significant sociocultural and scientific topics in early modern Spain. Although a major interest in memory was related to its legal and rhetorical implications, medical treatises discussing memory, cognitive impairment, and its treatment began to appear in the 16th and 17th century. Among these treatises, Disputationes phylosophicæ ac medicæ super libros Aristotelis de memoria, et reminiscentia (Philosophical and medical arguments on Aristotle's "De memoria et reminiscentia"), published in 1629 by the physician Juan Gutiérrez de Godoy, is unique in that it is entirely devoted to the medical aspects of memory. Although many of its concepts are now superseded, the treatise is valuable to understand the views on memory and cognitive impairment in 17th-century Spain and their sources, as Gutiérrez quoted many classical, medieval, and contemporary scholars and physicians. The book, written in Latin, is exclusively devoted to memory from a physiologic and medical point of view, with chapters on the classification of memory loss, a description of its causes (including old age, something not widely recognized before), and several chapters on its prevention and treatment, with a fascinating emphasis on confectio anacardina, or anacardium, an intranasal concoction made with the "marking nut," the fruit of the Semecarpus anacardium tree (also known as Malacca bean), with alleged memory-enhancing properties. We review Gutiérrez's Disputationes phylosophicæ, putting it into the wider intellectual and social context in the Europe of its time, and discuss the relevance and purported neuropharmacologic effects of anacardina.

Matched MeSH terms: Memory Disorders/therapy
Fulltext Examining relations between performance on non-verbal executive function and verbal self-regulation tasks in demographically-diverse populations

Dutra NB, Chen L, Anum A, Burger O, Davis HE, Dzokoto VA, et al.

Dev Sci, 2022 Sep;25(5):e13228.
PMID: 35025126 DOI: 10.1111/desc.13228

Self-regulation is a widely studied construct, generally assumed to be cognitively supported by executive functions (EFs). There is a lack of clarity and consensus over the roles of specific components of EFs in self-regulation. The current study examines the relations between performance on (a) a self-regulation task (Heads, Toes, Knees Shoulders Task) and (b) two EF tasks (Knox Cube and Beads Tasks) that measure different components of updating: working memory and short-term memory, respectively. We compared 107 8- to 13-year-old children (64 females) across demographically-diverse populations in four low and middle-income countries, including: Tanna, Vanuatu; Keningau, Malaysia; Saltpond, Ghana; and Natal, Brazil. The communities we studied vary in market integration/urbanicity as well as level of access, structure, and quality of schooling. We found that performance on the visuospatial working memory task (Knox Cube) and the visuospatial short-term memory task (Beads) are each independently associated with performance on the self-regulation task, even when controlling for schooling and location effects. These effects were robust across demographically-diverse populations of children in low-and middle-income countries. We conclude that this study found evidence supporting visuospatial working memory and visuospatial short-term memory as distinct cognitive processes which each support the development of self-regulation.

Matched MeSH terms: Memory, Short-Term/physiology
The eyes of the past: larger pupil size for autobiographical memories retrieved from field perspective

El Haj M, Janssen SMJ, Lenoble Q, Robin F, Gallouj K

Neurol Sci, 2022 Jan;43(1):661-666.
PMID: 33959825 DOI: 10.1007/s10072-021-05297-w

BACKGROUND: Visual perspective during memory retrieval has mainly been evaluated with methodologies based on introspection and subjective reports. The current study investigates whether visual perspective can be evaluated with a physiological measurement: pupil dilation.
METHODS: While their pupil diameter was measured with an eye-tracker, forty-five participants retrieved one memory from a field perspective (i.e., as viewed through our own eyes) and one memory from an observer perspective (i.e., as viewed from a spectator's standpoint). After retrieval, participants rated the emotional intensity of the memories.
RESULTS: Analysis demonstrated larger pupils during the retrieval of memories from a field perspective and higher emotional intensity for memories retrieved from a field perspective.
DISCUSSION: The larger pupils for memories recalled from a field perspective could, however, not be attributed to their higher emotional intensity. These findings suggest that pupil dilation could be used as a physiological assessment of visual perspective during memory retrieval.

Matched MeSH terms: Memory, Episodic*
Fulltext Genistein: A Potential Natural Lead Molecule for New Drug Design and Development for Treating Memory Impairment

Fuloria S, Yusri MAA, Sekar M, Gan SH, Rani NNIM, Lum PT, et al.

Molecules, 2022 Jan 01;27(1).
PMID: 35011497 DOI: 10.3390/molecules27010265

Genistein is a naturally occurring polyphenolic molecule in the isoflavones group which is well known for its neuroprotection. In this review, we summarize the efficacy of genistein in attenuating the effects of memory impairment (MI) in animals. Scopus, PubMed, and Web of Science databases were used to find the relevant articles and discuss the effects of genistein in the brain, including its pharmacokinetics, bioavailability, behavioral effects, and some of the potential mechanisms of action on memory in several animal models. The results of the preclinical studies highly suggested that genistein is highly effective in enhancing the cognitive performance of the MI animal models, specifically in the memory domain, including spatial, recognition, retention, and reference memories, through its ability to reduce oxidative stress and attenuate neuroinflammation. This review also highlighted challenges and opportunities to improve the drug delivery of genistein for treating MI. Along with that, the possible structural modifications and derivatives of genistein to improve its physicochemical and drug-likeness properties are also discussed. The outcomes of the review proved that genistein can enhance the cognitive performance and ameliorate MI in different preclinical studies, thus indicating its potential as a natural lead for the design and development of a novel neuroprotective drug.

Matched MeSH terms: Memory Disorders/drug therapy*; Memory Disorders/metabolism

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