Short-term Memory Processes Research Articles

Piano sight-reading teaching and music education: A systematic literature review

Piano sight-reading competency, which is highly important for an aspiring musician who needs to face diverse musical problems, is an integral part of becoming a smooth performer. The aims of this systematic literature review concerning piano sight-reading pedagogy approaches between 2019 and 2024 are to determine the strengths and weaknesses of the peer-reviewed literature. The article examines cognitive, behavioral, and technological methods and tools of enhancing learning outcomes, based on the concept of cognitive load, constructivism, and behaviorist perspective. The cognitive strategies highlight the role of hand-eye coordination, short-term memory, and visual process; while the behavioral ones emphasize the importance of daily practice and feedback from the teacher. Emerging technologies, like VR and AI-driven platforms, are redefining education and offering unique ways of learning and forgetting. While achievements of the past are notable, challenges such as access and efficacious long-term approaches remain. The next step of research should be to focus on sustainable teaching methods and international perspectives to achieve homogeneous and effective sight-reading teaching worldwide. This essay provides an overview of integrated and adaptable teaching strategies that combine both traditional and modern tools for the development of versatile and confident musicians’ skills.

A probabilistic integration of LSTM and Gaussian process regression for uncertainty-aware reservoir water level predictions

ABSTRACT Reservoir-level forecasting, while being crucial for optimal operation, is challenged by complex physical processes and changing climate conditions. Machine learning approaches offer deterministic predictions but often neglect system physics and uncertainty. This article presents a probabilistic data-driven approach combining long short-term memory (LSTM) and Gaussian process regression (GPR) to provide both point forecasts and uncertainty estimates. The hybrid model leverages LSTM’s fitting capabilities with GPR’s robust Bayesian frameworks for uncertainty estimation in non-linear problems, offering accurate predictions without extensive high-fidelity modelling, and avoiding frequent training and parameter optimization. Evaluation with real reservoir data from India shows the model’s superiority over the vanilla LSTM for both univariate and multivariate scenarios. The proposed model achieved a Nash-Sutcliffe efficiency of 0.97 to 0.98, a mean biased error of −0.5634 to −1.0314 for 10-day forecasts, and a continuous ranked probability score of 5.80 and 1.87 for the Bhakra and Pong reservoirs, respectively.

Brackish water parameters monitoring dashboard using Internet of things and industry 4.0

INTRODUCTION: Brackish water aquaculture plays a crucial role in meeting the growing global demand for seafood. It offers an opportunity to diversify aquaculture production and reduce pressure on overexploited marine resources. OBJECTIVES: By harnessing the unique properties of brackish ecosystems, this practice contributes to food security, economic growth, and sustainable resource management, while also promoting the conservation of valuable marine habitats. The development of a cutting-edge Indigenous Water Quality Monitoring Prototype named "Aqua BuoySis" for precision brackish water aquaculture utilizing machine intelligence. METHODS: The prototype integrates sensors for Dissolved Oxygen (DO), pH, Temperature, Turbidity, and Total Dissolved Solids (TDS). These sensors are calibrated using a dynamic temperature-based machine-learning approach to ensure accuracy in real-time environments. Sensor calibration constants are uploaded to a server for comprehensive data calibration. RESULTS: The system collects data at 20-second intervals, associating it with specific pond IDs. Data refinement is achieved through Long Short-Term Memory (LSTM) processing. An Android and Web application, available in native languages such as Tamil and Telugu, has been developed to provide live updates to aqua farmers, facilitating informed decision-making. CONCLUSION: This technology represents a significant step towards enhancing precision in brackish water aquaculture through the fusion of machine intelligence and water quality management.

Long-term memory formation for voices during sleep in three-month-old infants

The ability to form long-term memories begins in early infancy. However, little is known about the specific mechanisms that guide memory formation during this developmental stage. We demonstrate the emergence of a long-term memory for a novel voice in three-month-old infants using the EEG mismatch response (MMR) to the word “baby”. In an oddball-paradigm, a frequent standard, and two rare deviant voices (novel and mother) were presented before (baseline), and after (test) familiarizing the infants with the novel voice and a subsequent nap. Only the mother deviant but not the novel deviant elicited a late frontal MMR (∼850 ms) at baseline, possibly reflecting a long-term memory representation for the mother’s voice. Yet, MMRs to the novel and mother deviant significantly increased in similarity after voice familiarization and sleep. Moreover, both MMRs showed an additional early (∼250 ms) frontal negative component that is potentially related to deviance processing in short-term memory. Enhanced spindle activity during the nap predicted an increase in late MMR amplitude to the novel deviant and increased MMR similarity between novel and mother deviant. Our findings indicate that the late positive MMR in infants might reflect emergent long-term memory that benefits from sleep spindles.

A Semi-Supervised Lie Detection Algorithm Based on Integrating Multiple Speech Emotional Features

When people tell lies, they often exhibit tension and emotional fluctuations, reflecting a complex psychological state. However, the scarcity of labeled data in datasets and the complexity of deception information pose significant challenges in extracting effective lie features, which severely restrict the accuracy of lie detection systems. To address this, this paper proposes a semi-supervised lie detection algorithm based on integrating multiple speech emotional features. Firstly, Long Short-Term Memory (LSTM) and Auto Encoder (AE) network process log Mel spectrogram features and acoustic statistical features, respectively, to capture the contextual links between similar features. Secondly, the joint attention model is used to learn the complementary relationship among different features to obtain feature representations with richer details. Lastly, the model combines the unsupervised loss Local Maximum Mean Discrepancy (LMMD) and supervised loss Jefferys multi-loss optimization to enhance the classification performance. Experimental results show that the algorithm proposed in this paper achieves better performance.

The role of executive functions in long-term memory: case report

The role of executive functions in long-term memory has been studied. We describe a single-case study, consisting of a 45-year-old male patient, hospitalized for right frontal stroke. After the stroke, the patient had memory alterations in everyday activities. However, performance in short-term memory tests was not significantly altered. Long-term memory assessments included pre- and post-stroke episodic, semantic, and procedural memories. Specific skills involved in the acquisition of new learning (auditory-verbal and visual reproduction) were also evaluated, as well as executive functions. The results evidence that short-term memory was not affected. Regarding long-term memory, significant differences were observed between pre- and post-stroke knowledge, the former being better preserved, which reveals anterograde amnesia. Pre-stroke long-term memory was also affected, but only with respect to episodic knowledge, with semantic and procedural memories preserved (episodic retrograde amnesia). Executive functions were altered as well, which could have been a factor affecting the acquisition and consolidation of new learning, despite the fact that short-term memory was not significantly altered. Therefore, executive functions might be a determinant factor in the acquisition of new learning, regardless of short-term memory processes, at least partially. According to the results of the present study, alterations in these functions might lead to anterograde amnesia. This entails the need to evaluate executive functions as an intrinsic part of memory evaluation.

Cognitive assessment in multiple sclerosis patients: electrophysiological and subjective studies

BackgroundMultiple sclerosis (MS) is a persistent inflammatory condition affecting the central nervous system, often associated with cognitive impairment. This impairment frequently presents itself as deficiencies in short-term memory, attention, and information processing speed. This research was designed to investigate P300 in patients with MS and its correlation with their cognitive function and their state of fatigue and depression. This work included two groups: the control group (20 subjects) and the study group (30 MS patients). Evaluation encompassed clinical history, audiological assessment, the Expanded Disability Status Scale (EDSS), the fatigue severity scale, the Beck Depression Inventory, Addenbrooke’s cognitive examination, and P300 component of event-related potentials (ERPs).ResultsThe study group exhibited varying degrees of fatigue and depression. The scores of Addenbrooke’s cognitive evaluation were reduced in the MS group. The MS group exhibited a considerably prolonged P300 latency compared to the control group.ConclusionThis study demonstrates the effectiveness of P300 in assessing cognitive function in persons diagnosed with MS.

An adaptive and interpretable SOH estimation method for lithium-ion batteries based-on relaxation voltage cross-scale features and multi-LSTM-RFR2

Accurate estimation of state of health (SOH) is crucial for ensuring reliable and safe operations of lithium-ion batteries across diverse engineering scenarios. However, current SOH estimation methods face challenges of reliance on specific working conditions, inadequate quality of degradation features, and limited precision and interpretability of estimation models. To address these issues, this paper presents an adaptive and interpretable SOH estimation method based on cross-scale features and a framework with multiple long short-term memory processes and two-level random forest regression (Multi-LSTM-RFR2). First, relaxation voltage, adapting to multiple charging-discharging conditions, is utilized as a source for degradation feature construction. In this aspect, multiple value features, variation features, statistic features, and parameter features are created from four scales: point data, local data, global data, and extended data. Then, the Multi-LSTM-RFR2 interpretable framework employs multi-LSTM processes to handle time-series inputs from multiple features, incorporating a two-level RFR mechanism to fuse and optimize pre-estimated SOH results. Finally, the importance of features is studied from both single and category feature perspectives for interpretability analysis. Degradation experiments with 24 commercial 18,650 battery cells under multiple charging-discharging conditions are used to validate effectiveness of the proposed method. The average values of four evaluation indicators in terms of mean absolute error, mean absolute percentage error, root mean squared error, and R-squared are 1.07 %, 1.33 %, 1.28 %, and 0.9654, respectively.

Risk factors for domain-specific neurocognitive outcome in pediatric survivors of a brain tumor in the posterior fossa-Results of the HIT 2000 trial.

Neurocognition can be severely affected in pediatric brain tumor survivors. We analyzed the association of cognitive functioning with radiotherapy dose, postoperative cerebellar mutism syndrome (pCMS), hydrocephalus, intraventricular methotrexate (MTX) application, tumor localization, and biology in pediatric survivors of a posterior fossa tumor. Subdomain-specific neurocognitive outcome data from 279 relapse-free survivors of the HIT-2000 trial (241 medulloblastoma and 38 infratentorial ependymoma) using the Neuropsychological Basic Diagnostic tool based on Cattell-Horn-Carroll's model for intelligence were analyzed. Cognitive performance 5.14 years (mean; range = 1.52-13.02) after diagnosis was significantly below normal for all subtests. Processing speed and psychomotor abilities were most affected. Influencing factors were domain-specific: CSI-dose had a strong impact on most subtests. pCMS was associated with psychomotor abilities (β = -0.25 to -0.16) and processing speed (β = -0.32). Postoperative hydrocephalus correlated with crystallized intelligence (β = -0.20) and short-term memory (β = -0.15), age with crystallized intelligence (β = 0.15) and psychomotor abilities (β = -0.16 and β = -0.17). Scores for fluid intelligence (β = -0.23), short-term memory (β = -0.17) and visual processing (β = -0.25) declined, and scores for selective attention improved (β = 0.29) with time after diagnosis. The dose of CSI was strongly associated with neurocognitive outcomes. Low psychomotor abilities and processing speed both in patients treated with and without CSI suggest a strong contribution of the tumor and its surgery on these functions. Future research therefore should analyze strategies to both reduce CSI dose and toxicity caused by other treatment modalities.

Neurocognitive and psycho-emotional profile of children with disabilities

Objective Neurocognitive deficits in attention, short-term memory, and sequential information processing are present in children with a variety of disabilities, whereas language and visuospatial abilities vary. Method We compared the performance of 59 children (mean age, 15 years) with learning disabilities (n = 18), Down syndrome (n = 21), and intellectual disabilities (n = 20). A series of neuropsychological tests were used to evaluate the neurocognitive processes of memory, attention, visuospatial perception, and executive function. To better understand what emotions they experience, we assessed emotions like anxiety, depression, and positive and negative mood. Results The performance of children with Down syndrome was statistically significantly different from that of other groups, indicating lower performance (p = 0.001). In comparison to other groups, children with Down syndrome performed significantly worse across all cognitive domains. Additionally, there were no statistically significant differences between groups and low emotional functioning scores across the board for all children. People with DS frequently have distinctive neurocognitive and neurobehavioral profiles that appear during particular developmental phases and have many distinct strengths and weaknesses that should be respected as they mature over the course of their lives. The current findings have substantial consequences for interventions that are focused on achieving the best results.

Individual features of the temporal dynamics of inhibitory processes in visual short-term memory depending on the content of stimuli and memorization training

Introduction. Age-related changes in cognitive functions are caused by various processes: degrading inhibitory control, decrease in the volume of working memory, or a slowdown in the speed of thought processes. However, there is no unanimous opinion on the dynamics of these processes and their interactions yet. Based on this, our research goal became finding the temporal dynamics and individual relationship of the effects of proactive interference (Retrival-Induced Forgeting (RIF)) and learning to remember (Retrieval-Based Learning (RBL)) depending on the content of the presented stimuli as well as the age-related specifics of memorization when training the short-term visual memory. Materials and methods. Memory training was carried out in a mode independently organized by the study participants using a computerized memorization program in a proactive interference model, including five sessions of visual presentation of stimuli. University students participated in the first part of the study, aimed at clarifying the meaning of the content of two different sets of stimuli, (220 people, 20.4 ± 1.9 years old), whereas second part aimed to determine the age-related characteristics of visual short-term memory training in a group of students, (70 subjects, 21.7±2.5 years) and a group of elderly women (25 subjects, 64.4±6.3 years). Research results. The results of the memory trace retrieval analysis showed that the RIF effect dominates over the RBL effect regardless of the content of the stimuli, but at different rates of its development depending on the set of stimuli used. With significant individual diversity in the combination of memory strategies, the RIF effect dominated at the initial stage of stimulus presentation (67.6% of cases) and gradually replaced by RBL (56.2% in the fifth run of the experiment). Analysis of age-related characteristics of the effectiveness of short-term visual memory training showed that when it is independently organized, maintaining activity is observed in 32% of university students and 50% in the elderly group. Along with this. Only average of ten approaches to complete this task accompanied by a further decrease in the number of training participants. Comparison of the results of the first three training sessions revealed a significant increase in the memory index in both groups compared to the initial value and the presence of a RIF effect with a further more rapid increase in the efficiency of memorization in students. Conclusion. Proactive interference while memorization of visual stimuli repeatedly presented in five sessions, appears regardless of their content, age of the subjects and memorization training. The efficiency of memorization due to training grows faster in the group of students and accompanied by the combination of the RIF and RBL effects that is characterized by individual diversity without a significant effect on the final result of the memory retrieval.

Integrating LSTM and NLP techniques for essay generation

This research explores the potential of Long Short-Term Memory (LSTM) and Natural Language Processing (NLP) for automated essay generation. The goal of the study is to create a model that can produce high-quality essays that are not only grammatically correct but also semantically meaningful and contextually relevant. The rise of NLP and Deep Learning has made it possible to generate text that is coherent and semantically sound. In this research, there is an advantage by leveraging the ability of LSTMs to capture long-term dependencies and context within the text, and combining it with NLP techniques, such as word embeddings, to process and encode textual data. The results of experiments show that the proposed model can effectively generate essays that are coherent, contextually relevant, and semantically meaningful. This is a significant advancement in the field of text generation and has potential applications in areas such as education, content creation, and language translation. In education, for example, the model could be used to generate essays for language proficiency tests or as a writing aid for students. In content creation, it could be used to generate articles, blog posts, and other written content. In language translation, the model could be used to generate essays in the target language that are semantically and contextually equivalent to the source language essay. The findings of this study contribute to the advancement of NLP and deep learning techniques in the area of text generation and open up new avenues for future research. Short after the proposed model was deployed, it was discovered that it outscored Multi-Topic Aware LSTM (MTA-LSTM), Topic-Attention LSTM (TAT-LSTM), and Topic-Averaged LSTM (TAV-LSTM) in human evaluation by 6.84 percent, 25.40 percent, and 34.94 percent, respectively. Furthermore, it enhanced automatic BLEU score evaluation scores by 11.68 percent, 26.23 percent, and 54.11 percent in MTA-LSTM, TAT-LSTM, and TAV- LSTM, respectively.

Monitoring carbon emissions using deep learning and statistical process control: a strategy for impact assessment of governments’ carbon reduction policies

Across the globe, governments are developing policies and strategies to reduce carbon emissions to address climate change. Monitoring the impact of governments’ carbon reduction policies can significantly enhance our ability to combat climate change and meet emissions reduction targets. One promising area in this regard is the role of artificial intelligence (AI) in carbon reduction policy and strategy monitoring. While researchers have explored applications of AI on data from various sources, including sensors, satellites, and social media, to identify areas for carbon emissions reduction, AI applications in tracking the effect of governments’ carbon reduction plans have been limited. This study presents an AI framework based on long short-term memory (LSTM) and statistical process control (SPC) for the monitoring of variations in carbon emissions, using UK annual CO2 emission (per capita) data, covering a period between 1750 and 2021. This paper used LSTM to develop a surrogate model for the UK’s carbon emissions characteristics and behaviours. As observed in our experiments, LSTM has better predictive abilities than ARIMA, Exponential Smoothing and feedforward artificial neural networks (ANN) in predicting CO2 emissions on a yearly prediction horizon. Using the deviation of the recorded emission data from the surrogate process, the variations and trends in these behaviours are then analysed using SPC, specifically Shewhart individual/moving range control charts. The result shows several assignable variations between the mid-1990s and 2021, which correlate with some notable UK government commitments to lower carbon emissions within this period. The framework presented in this paper can help identify periods of significant deviations from a country’s normal CO2 emissions, which can potentially result from the government’s carbon reduction policies or activities that can alter the amount of CO2 emissions.

Study of Short-Term and Long-Term Memories by Hodgkin–Huxley Memristor

Long-term memory (LTM ) and short-term memory (STM ) and their evolution from one to the other are important mechanisms to understand brain memory. We use the Hodgkin–Huxley (HH ) model, a well-tested and closest model to biological neurons and synapses, to shine some light on LTM and STM memorization mechanisms. The role of [Formula: see text] and [Formula: see text] ion channels playing in LTM and STM process is carefully examined by using three different types of input signals, namely, a step DC voltage, a positive part of sinusoidal wave and periodic square signal with read voltage. Results are analyzed based on first-order [Formula: see text] memristor and second-order [Formula: see text] memristor.

Maximizing financial management efficiency with a novel machine learning algorithm

Any firm must successfully manage its financial assets to succeed. To make wise choices for handling resources, possibilities for investment, and allocation of funds, accountants rely on reliable forecasting of finances. To enhance the effectiveness of Financial Management (FM), this research develops hybrid long short-term memory and hierarchical analytic process (HLSTM-AHP) technique. The LSTM approach is applied to develop the financial evaluation system, while the AHP approach is employed to establish the weightings of economic variables incorporated into the LSTM framework. To show how well the suggested HLSTM-AHP approach works at enhancing FM effectiveness, real-time accounting information from a company that is publicly traded are implemented. To further address the issue of anomalous data regarding finances, this research employs a unique sampling data collected by linear discriminant analysis (LDA) to develop a multiscale convolutional neural network (MCNN), which improves the framework's forecasting performance and demonstrates conclusively that machine earning (ML) is practicable in the study of FM forecasting, with plenty of opportunity for future investigations.

Spatial and Spectral EEG Signal Analysis with Case Study of Slogans on Consumer’s Behaviour

Neuromarketing utilizes neuroscientific techniques to investigate consumer behavior, providing valuable insights beyond traditional research methods such as questionnaires and interviews which may not provide a complete understanding of consumer decision-making processes.Electroencephalography (EEG) has emerged as a promising tool for analyzing consumer responses to marketing stimuli. Nevertheless, the neural processing of slogans and their impact on short-term memory recall using EEG signals remains understudied. This research aims to bridge this gap by examining the neural activity associated with the recall of slogans using EEG analysis. By employing a spatial selection and spectral processingmethod, which involves Butterworth BPF filtering and L2-norm normalization to identify optimal channel combinations, active brain areas involved in slogan processing can be identified. Results reveal prominent activation in the frontal and occipital regions, particularly the F4 channel, indicating active recall and visual processing in individuals who correctly respond to slogans. These findings underscore the significance of slogans as visual marketing stimuli and offer insights for effective branding strategies. Leveraging EEG signals and understanding short-term memory processes enables marketers to optimize the impact of slogans on consumer engagement and brand recognition.

Nobel prize winners Arvid Carlsson, Paul Greengard and Eric Kandel: the research of signal transduction in the nervous system

For many decades, scientists have tried to unravel the mysteries of the nervous system – the complex phenomenon that receives messages, processes information, and sends signals to the rest of the body. The most important scientific discoveries of the 19th and the 20th centuries paved the way for the 2000 Nobel Prize in Physiology or Medicine awarded to Arvid Carlsson, Paul Greengard and Eric Kandel “for their discoveries concerning signal transduction in the nervous system”. So, the beginning of the new millennium was “marked” by pioneering research into the chemical transmission of signals in the central nervous system, which created the foundation for a deeper understanding of the mediatory role of dopamine, the processes of slow synaptic transmission, short-term and long-term memory, and the mechanisms of action of antipsychotic and antidepressant drugs. The paper aims to outline the main stages of scientific activities of a Swedish neuropharmacologist Per Arvid Emil Carlsson and the American neurobiologists Paul Greengard and Eric Richard Kandel. Keywords: Arvid Carlsson, brain, dopamine, Eric Kandel, learning, memory, nervous system, Paul Greengard, slow synaptic transmission, the 2000 Nobel Prize in Physiology or Medicine

Cognitive abilities and creativity: The role of working memory and visual processing

Recent studies have revealed the importance of cognitive abilities in creative thinking. However, most research addressed adults and only a few studies have examined the ways these correlations are manifested among young children. The present study explores the role of various cognitive abilities in creativity among school children. Measures of creativity, visual-spatial working memory (VSWM), verbal short-term memory (STM), working memory (WM), and visual processing (VP) were administered to 331 students in Grades 4 and 5. Cluster analysis was used to group students' creativity levels. A multivariate analysis of variance (MANOVA) was conducted to test differences in cognitive abilities across the three clusters. Group differences between high and moderate level creativity students and low creativity students were found regarding VP abilities in the following tests: VSWM, visual discrimination (VD), and Raven's Colored Progressive Matrices (RCPM). Group differences between high creativity students and low creativity students were also found on verbal STM and WM. Additionally, structural equation modeling (SEM) analysis revealed that VP can significantly account for unique variances associated with creativity, while verbal STM and WM are not significantly related to creativity. These findings enlighten the cognitive processes underlying creativity in young children.

Residential green space improves cognitive performances in primary schoolchildren independent of traffic-related air pollution exposure

BackgroundCognitive performances of schoolchildren have been adversely associated with both recent and chronic exposure to ambient air pollution at the residence. In addition, growing evidence indicates that exposure to green space is associated with a wide range of health benefits. Therefore, we aimed to investigate if surrounding green space at the residence improves cognitive performance of primary schoolchildren while taking into account air pollution exposure.MethodsCognitive performance tests were administered repeatedly to a total of 307 primary schoolchildren aged 9-12y, living in Flanders, Belgium (2012–2014). These tests covered three cognitive domains: attention (Stroop and Continuous Performance Tests), short-term memory (Digit Span Forward and Backward Tests), and visual information processing speed (Digit-Symbol and Pattern Comparison Tests). Green space exposure was estimated within several radii around their current residence (50 m to 2000 m), using a aerial photo-derived high-resolution (1 m2) land cover map. Furthermore, air pollution exposure to PM2.5 and NO2 during the year before examination was modelled for the child’s residence using a spatial–temporal interpolation method.ResultsAn improvement of the children’s attention was found with more residential green space exposure independent of traffic-related air pollution. For an interquartile range increment (21%) of green space within 100 m of the residence, a significantly lower mean reaction time was observed independent of NO2 for both the sustained-selective (-9.74 ms, 95% CI: -16.6 to -2.9 ms, p = 0.006) and the selective attention outcomes (-65.90 ms, 95% CI: -117.0 to -14.8 ms, p = 0.01). Moreover, green space exposure within a large radius (2000 m) around the residence was significantly associated with a better performance in short-term memory (Digit-Span Forward Test) and a higher visual information processing speed (Pattern Comparison Test), taking into account traffic-related exposure. However, all associations were attenuated after taking into account long-term residential PM2.5 exposure.ConclusionsOur panel study showed that exposure to residential surrounding green space was associated with better cognitive performances at 9–12 years of age, taking into account traffic-related air pollution exposure. These findings support the necessity to build attractive green spaces in the residential environment to promote healthy cognitive development in children.

Neural waves and short-term memory in a neural net model.

We show that recognizable neural waveforms are reproduced in the model described in previous work. In so doing, we reproduce close matches to certain observed, though filtered, EEG-like measurements in closed mathematical form, to good approximations. Such neural waves represent the responses of individual networks to external and endogenous inputs and are presumably the carriers of the information used to perform computations in actual brains, which are complexes of interconnected networks. Then, we apply these findings to a question arising in short-term memory processing in humans. Namely, we show how the anomalously small number of reliable retrievals from short-term memory found in certain trials of the Sternberg task is related to the relative frequencies of the neural waves involved. This finding justifies the hypothesis of phase-coding, which has been posited as an explanation of this effect.