Memory Decay Research Articles

An In Situ Exploration of Practicing Rugby Coaches’ Cognitions, Higher Psychological Functions, and Actions Using Think Aloud Protocol

Psychology-based research has been a characteristic of empirical inquiry in sport coaching for over 50 years, and cognitive function is widely accepted as a fundamental component of sport coaching expertise. Within the academic literature, much empirical research on coaches’ cognitions has tended to adopt retrospective approaches, such as postsession interviews or stimulated recall, thus capturing participant recall after the incident, training session, or competition. Methods such as these that rely on participants’ retrospective recall are prone to memory decay, reordering of accounts, and confirmation bias. The aim of this research was to collect a different type of data to what is generated with retrospective approaches and, rather, capture coaching cognitions in situ using think aloud protocol. The data captured were broken down into meaning units and analyzed using a reflexive thematic analysis. Situated in the practice of six experienced rugby coaches, findings revealed that think aloud protocol generated rich data. However, engaging think aloud protocol was problematic as the site of inquiry was confounded by multiple social interactions and required coaches to provide frequent instruction and feedback. The interaction between cognition and action is conceptualized by the tentative offering of a conceptual model that includes cognitive triggers and thresholds. The implications of these findings can help academics and coach developers to understand the complexity of capturing coaches’ in situ thinking within dynamic social environments.

The role of evaluatively conditioned stimuli in iconic memory.

In the present study, an attempt was made to replicate results found about the influence of valence on prioritisation and decay in iconic memory. Hereby, the evaluative conditioning effect was used to induce valence for formerly neutral stimuli. The effect is gained by pairing neutral stimuli with either positive, negative, or neutral images in a conditioning phase. Afterwards, the conditioned stimuli acted as targets in an iconic memory test. In the iconic memory test, targets were presented together with seven other stimuli on a circular placement on the screen for a short time. A cue delayed by either 17, 68, 221, 493, or 1003 ms pointed at the target to be reported. Participants rated the targets before and after the conditioning phase. In addition, the affective and neutral images used in the pairing procedure were rated at the end of the experiment. While no significant change in rating could be observed for the conditioned targets, a significant effect of conditioned valence was still present in the response times and the accuracy of the iconic memory test. Participants reacted the quickest in response to a cue for positively conditioned targets compared to neutral or negatively conditioned targets. Accuracy was highest for positively conditioned targets and was lowest for negatively conditioned targets. Unlike in prior studies, slower decay of information in iconic memory for negative targets was not revealed. Further research should be conducted to identify reasons for this inconsistency.

Seeking a solution

Hannah Burrow discusses the issue of memory decay in dental patients.

Fractional-Order Tabu Learning Neuron Models and Their Dynamics

In this paper, by replacing the exponential memory kernel function of a tabu learning single-neuron model with the power-law memory kernel function, a novel Caputo’s fractional-order tabu learning single-neuron model and a network of two interacting fractional-order tabu learning neurons are constructed firstly. Different from the integer-order tabu learning model, the order of the fractional-order derivative is used to measure the neuron’s memory decay rate and then the stabilities of the models are evaluated by the eigenvalues of the Jacobian matrix at the equilibrium point of the fractional-order models. By choosing the memory decay rate (or the order of the fractional-order derivative) as the bifurcation parameter, it is proved that Hopf bifurcation occurs in the fractional-order tabu learning single-neuron model where the value of bifurcation point in the fractional-order model is smaller than the integer-order model’s. By numerical simulations, it is shown that the fractional-order network with a lower memory decay rate is capable of producing tangent bifurcation as the learning rate increases from 0 to 0.4. When the learning rate is fixed and the memory decay increases, the fractional-order network enters into frequency synchronization firstly and then enters into amplitude synchronization. During the synchronization process, the oscillation frequency of the fractional-order tabu learning two-neuron network increases with an increase in the memory decay rate. This implies that the higher the memory decay rate of neurons, the higher the learning frequency will be.

Algorithmic Spaced Retrieval Enhances Long-Term Memory in Alzheimer Disease: Case-Control Pilot Study.

Spaced retrieval is a learning technique that involves engaging in repeated memory testing after increasingly lengthy intervals of time. Spaced retrieval has been shown to improve long-term memory in Alzheimer disease (AD), but it has historically been difficult to implement in the everyday lives of individuals with AD. This research aims to determine, in people with mild cognitive impairment (MCI) due to AD, the efficacy and feasibility of a mobile app that combines spaced retrieval with a machine learning algorithm to enhance memory retention. Specifically, the app prompts users to answer questions during brief daily sessions, and a machine learning algorithm tracks each user's rate of forgetting to determine the optimal spacing schedule to prevent anticipated forgetting. In this pilot study, 61 participants (young adults: n=21, 34%; healthy older adults: n=20, 33%; people with MCI due to AD: n=20, 33%) used the app for 4 weeks to learn new facts and relearn forgotten name-face associations. Participation during the 4-week period was characterized by using the app once per day to answer 15 questions about the facts and names. After the 4-week learning phase, participants completed 2 recognition memory tests approximately 1 week apart, which tested memory for information they had studied using the app as well as information they had not studied. After using the mobile app for 1 month, every person with MCI due to AD demonstrated improvements in memory for new facts that they had studied via the app compared to baseline (P<.001). All but one person with MCI due to AD (19/20, 95%) showed improvements of more than 10 percentage points, comparable to the improvements shown by young adults and healthy older adults. Memory for name-face associations was similarly improved for all participant groups after using the app but to a lesser degree. Furthermore, for both new facts and name-face associations, we found no memory decay for any participant group after they took a break of approximately 1 week from using the app at the end of the study. Regarding usability, of the 20 people with MCI due to AD, 16 (80%) self-adhered to the app's automated practice schedule, and half of them (n=10, 50%) expressed an interest in continuing to use it. These results demonstrate early evidence that spaced retrieval mobile apps are both feasible for people with early-stage AD to use in their everyday lives and effective for supporting memory retention of recently learned facts and name-face associations.

Impact of Zeeman and hyperfine interactions on the magnetic properties of paramagnetic metal Ions: I. Local interactions of the electron spin

The anisotropic Zeeman interaction of an ion, and the strong hyperfine interaction with its own nucleus, can significantly influence its interactions with the local environment. These effects, including the reduction of the effective magnetic moment of the electron spin and the phase memory decay rate, are studied theoretically. Analytical expressions describing the mean magnetic moment of the electron spin are obtained. The results of the theoretical analysis and accompanying numerical computations show that the strong hyperfine interaction of the ion reduces its effective magnetic moment. In particular, a 7% reduction is found for the scandium endofullerene Sc2@C80(CH2Ph) under conditions typical of an X-band EPR experiment.

Data from &amp;ldquo;A Registered Report Testing the Effect of Sleep on DRM False Memory: Greater Lure and Veridical Recall but Fewer Intrusions After Sleep&amp;rdquo;

This paper describes a rich dataset from a registered report investigating sleep’s effect on false memory in the Deese-Roediger-McDermott (DRM) paradigm. 534 young adults completed free recall either shortly or 12 hours after studying lists of semantic associates (e.g., hospital, nurse). Collected online, our recall data showcase high data quality, replicating classic behavioural effects (e.g., serial position curve). The dataset contains raw recall data with original spelling and recall order, accompanied by demographic information (e.g., gender, time-of-day preference). Its versatility supports reuse in modelling memory decay and search processes, understanding lexical effects and individual differences, and benchmarking online memory studies.

Neural Mechanisms of Visual Search and Working Memory

Visual search is a useful experimental paradigm investigating various aspects of attention. For efficient search, participants must avoid revisiting previously viewed objects. Inhibitory tagging and inhibition of return are phenomena related to this process, but their neural mechanisms are yet to be elucidated. Recent studies have shown that the rate of revisit behavior during visual search correlates with working memory capacity. This suggests that top-down signals from the frontal executive system alter the priority map that guides attention and eye movements. With this in mind, we have developed a novel visual search paradigm with many identical distractors and an evaluation model that assesses multiple parameters of working memory. The model incorporated memory capacity, memory decay, and utility rate, and when applied to data obtained from experimental animals, these parameters could be reliably evaluated. Furthermore, using the behavioral paradigm and model, we found that systemic administration of subanesthetic dose of ketamine decreased utility rate and memory capacity, while nicotine administration increased utility rate. Since our behavioral paradigm does not require complex instruction, it can be applied to a variety of patients in future clinical studies.

On stability of equations with an infinite distributed delay

For scalar equations of population dynamics with an infinite distributed delay x′(t)=r(t)[∫−∞tf(x(s))dsR(t,s)−x(t)], x(t)=φ(t), t⩽t0, where f is the delayed production function, we consider asymptotic stability of the zero and a positive equilibrium K. It is assumed that the initial distribution is an arbitrary continuous function. Introducing conditions on the memory decay, we characterize functions f such that any solution with nonnegative nontrivial initial conditions tends to a positive equilibrium. The differences between finite and infinite delays are outlined, in particular, we present an example when the weak Allee effect (meaning that f′(0)=1 together with f(x)>x , x∈(0,K) ) which has no effect in the finite delay case (all solutions are persistent) can lead to extinction in the case of an infinite delay.

When do consumers buy during online promotions? A theoretical and empirical investigation

An increasing number of merchants are using online platforms to promote their products; however, much is still unknown about how consumers behave in response to online promotions. This study investigates factors affecting consumers' purchase intentions and purchase behaviors during online promotions. We classify consumers into two categories, one mainly affected by the time pressure of promotion and the other primarily subject to the effect of memory decay. We then propose an analytical model to capture the market demand during an online promotion. Our analytical result indicates that there exist four types of demand patterns during online promotions, i.e., U-shape, inverted U-shape, monotonically increasing, and monotonically decreasing. We subsequently explore factors that can affect the type of demand patterns, such as the product type (nondurable and durable goods), duration of the promotion, discount level, and product category. Our empirical analyses of real-world promotion and sales data from a B2C e-commerce platform validate the analytical results. The type of demand curves depends on the characteristics of the goods and promotions. For instance, the inverted U-shape demand curve appears only for nondurable consumer goods, whereas the U-shape curve exists only for durable consumer goods. Finally, in a series of counterfactual analyses based on the proposed model, we show how revenues change during an online promotion in response to varying parameters of promotions and derive some interesting observations. These findings provide important insights to online retailers and can help them better understand their consumers and optimize their product promotion strategies.

Long-Horizon Associative Learning Explains Human Sensitivity to Statistical and Network Structures in Auditory Sequences.

Networks are a useful mathematical tool for capturing the complexity of the world. In a previous behavioral study, we showed that human adults were sensitive to the high-level network structure underlying auditory sequences, even when presented with incomplete information. Their performance was best explained by a mathematical model compatible with associative learning principles, based on the integration of the transition probabilities between adjacent and nonadjacent elements with a memory decay. In the present study, we explored the neural correlates of this hypothesis via magnetoencephalography (MEG). Participants (N = 23, 16 females) passively listened to sequences of tones organized in a sparse community network structure comprising two communities. An early difference (∼150 ms) was observed in the brain responses to tone transitions with similar transition probability but occurring either within or between communities. This result implies a rapid and automatic encoding of the sequence structure. Using time-resolved decoding, we estimated the duration and overlap of the representation of each tone. The decoding performance exhibited exponential decay, resulting in a significant overlap between the representations of successive tones. Based on this extended decay profile, we estimated a long-horizon associative learning novelty index for each transition and found a correlation of this measure with the MEG signal. Overall, our study sheds light on the neural mechanisms underlying human sensitivity to network structures and highlights the potential role of Hebbian-like mechanisms in supporting learning at various temporal scales.

TIAE-DSIN: A time interval aware deep session interest network for click-through rate prediction

The users’ click prediction holds significant commercial value in online purchase platforms. Currently, some session-based interest extraction models overlook the impact of temporal interval factors on the distribution pattern of behaviors within a session. To address the above issue, this paper designs a module named session-based time-aware gated recurrent units (ST-GRU) to integrate the temporal interval factors into session sequence. The ST-GRU module utilizes a memory decay function, enabling the simulation process to identify the users’ memory changes within each session. Subsequently, to account for the interdependence between different sessions, this paper introduces bi-directional gated recurrent units (Bi-GRU) with an attention mechanism to learn interaction relations of user interests. Additionally, for high-dimensional potential features, this paper integrates above modules and proposes a method called time interval aware deep session interest network (TIAE-DSIN) model. Correspondingly, comparative experiments are carried out on three public datasets, and the obtained results indicate that the AUC of TIAE-DSIN model is superior to other session interest extraction models.

Estimate exponential memory decay in hidden Markov model and its applications to inference

Inference in hidden Markov model has been challenging in terms of scalability due to dependencies in the observation data. In this paper, we utilize the inherent memory decay in hidden Markov models, such that the forward and backward probabilities can be carried out with subsequences, enabling efficient inference over long sequences of observations. We formulate this forward filtering process in the setting of the random dynamical system and there exist Lyapunov exponents in the i.i.d random matrices production. And the rate of the memory decay is known as λ2−λ1, the gap of the top two Lyapunov exponents almost surely. An efficient and accurate algorithm is proposed to numerically estimate the gap after the soft-max parametrization. The length of subsequences B given the controlled error ϵ is B≈log(ϵ)/(λ2−λ1). We theoretically prove the validity of the algorithm and demonstrate the effectiveness with numerical examples. The method developed here can be applied to widely used algorithms, such as mini-batch stochastic gradient method. Moreover, the continuity of Lyapunov spectrum ensures the estimated B could be reused for the nearby parameter during the inference.

Easier comparison of bets in evaluation does not reduce classical preference reversals: Evidence against a context-dependent explanation.

In preference reversals, subjects express different rankings over a set of alternatives depending on how preferences are elicited. In classical reversal tasks, for instance, subjects often select a safe bet over a risky one when given a choice between the two in a pair, but then assign a higher monetary evaluation to the risky bet. Motivated by a rich literature on context-dependent preferences, we conjecture that comparisons across bets in a pair can influence both Choice and Evaluation. Yet deciders are less likely to mentally compare the bets in the latter case, as bets are typically evaluated in isolation. This asymmetry between Choice and Evaluation is, we surmise, one cause of the reversals. If we further assume that memory decay affects mental comparisons in Evaluation, the account predicts order and timing effects on the reversal probability. We run several treatments designed to facilitate or hinder the retrieval from memory of the alternative bet during evaluation of a bet. However, the reversal rate does not vary across treatments in the predicted direction, and we find no systematic order or timing effects. We conclude that reversals are not influenced by the ease with which subjects recall the alternative bet during the evaluations, which suggests in turn that a relatively smaller frequency of comparisons across bets during the (typically isolated) evaluations is not a significant cause of reversals.

Mind and Machine: A Philosophical Examination of Matt Carter’s “Minds &amp; Computers: An Introduction to the Philosophy of Artificial Intelligence”

In his book “Minds and Computers: An Introduction to the Philosophy of Artificial Intelligence”, Matt Carter presents a comprehensive exploration of the philosophical questions surrounding artificial intelligence (AI). Carter argues that the development of AI is not merely a technological challenge but fundamentally a philosophical one. He delves into key issues like the nature of mental states, the limits of introspection, the implications of memory decay, and the functionalist framework that allows for the possibility of AI. Carter contrasts functionalism with reductive materialism, highlighting how the former accommodates the concept of artificial minds. He also emphasizes the significance of computationalism, which combines functionalism with computational theory to provide a robust explanation for mental processes. The book further discusses formal systems, the role of register machines in computation, and the inherent challenges in AI research, particularly in developing natural languag processing systems. Carter’s work underscores the limitations of current computational models of cognition while remaining hopeful that advances in neuroscience may lead to stronger AI systems in the future. This book provides critical insights for researchers in AI and cognitive science, motivating further inquiry into the philosophical foundations of artificial intelligence and its practical implications.

Mechanistic description of spontaneous loss of memory persistent activity based on neuronal synaptic strength

Persistent neural activity associated with working memory (WM) lasts for a limited time duration. Current theories suggest that its termination is actively obtained via inhibitory currents, and there is currently no theory regarding the possibility of a passive memory-loss mechanism that terminates memory persistent activity. Here, we develop an analytical-framework, based on synaptic strength, and show via simulations and fitting to wet-lab experiments, that passive memory-loss might be a result of an ionic-current long-term plateau, i.e., very slow reduction of memory followed by abrupt loss. We describe analytically the plateau, when the memory state is just below criticality. These results, including the plateau, are supported by experiments performed on rats. Moreover, we show that even just above criticality, forgetfulness can occur due to neuronal noise with ionic-current fluctuations, yielding a plateau, representing memory with very slow decay, and eventually a fast memory decay. Our results could have implications for developing new medications, targeted against memory impairments, through modifying neuronal noise.

Diary studies in research: More than a research method

This paper proposes diary studies as a methodological choice for addressing crucial questions in qualitative process research. Diary studies unearth within-individual relationships that capture individual experiences and sense-making of these experiences in a processual manner. Therefore, using diary studies not only addresses the need for more dynamic methodological designs in qualitative research, but is advantageous for providing a process view of how the individual engages with the constructs studied. While process-studies, studied longitudinally, are often fraught with challenges of attrition or disengagement, this paper discusses how a methodological choice which is perceived as being beneficial by the participant can sustain engagement and consequently, research rigour. On one hand, a comparison of interview and diary data from the same participants show evidence of memory decay in interviews and reinforce the rigour of diary studies in the study of processual phenomenon. On the other hand, this study also showcases how researcher know-how, unintentionally imparted through reflective and processual questions asked in the study positively impacts the participants, and when reflected on, enhanced learning. This paper therefore demonstrates the ability of a research method to benefit both the research and the researched, and suggests a step-by-step design to implement a rigorous and impactful diary study using user-friendly forms and reflective questioning.

IGZO/SnOx-based dynamic memristor with fading memory effect for reservoir computing

We investigate a synaptic device with short-term memory characteristics using IGZO/SnOx as the switching layer. The thickness and components of each layer are analyzed by using x-ray photoelectron spectroscopy and transmission electron microscopy. The memristor exhibits analog resistive switching and a volatile feature with current decay over time. Moreover, through ten cycles of potentiation and depression, we demonstrate stable conductance modulation, leading to high-accuracy Modified National Institute of Standards and Technology pattern recognition. We effectively emulate the learning system of a biological synapse, including paired-pulse facilitation, spiking-amplitude-dependent plasticity, and spiking-rate-dependent plasticity (SRDP) by pulse trains. Ultimately, 4-bit reservoir computing divided into 16 states is incarnated using a pulse stream considering short-term memory plasticity and decay properties.

Fractional derivative method for anomalous aquitard flow in a leaky aquifer system with depth-decaying aquitard hydraulic conductivity

Aquitards significantly affect groundwater flow in multi-aquifer systems through adjacent aquifer leakage. Despite this, studies focusing on their heterogeneity and the non-conventional diffusion patterns of their flow are still limited. In this study, a factional derivative approach was first extended to explore the time-dependent behavior of flow transport in the aquitard. Two analytical solutions were derived for specific discharges in independent aquitards under different boundary conditions. The findings revealed that aquitard flow exhibits obvious anomalous diffusion behaviors, characterized by slower decay and heavy-tailed specific discharge data. The fractional derivative model provided a more accurate representation of this behavior than traditional models, as evidenced by its superior agreement with experimental data. Moreover, a transient model for pumping tests in a leaky aquifer system was developed, incorporating the memory effect of anomalous flow and vertical heterogeneity in aquitards. Relevant semi-analytical solutions were derived to explore the impacts of memory factor β and decay exponent of aquitard hydraulic conductivity (K) on the leakage aquifer system. Theoretical results demonstrated that stronger memory effect reduces drawdowns in the aquitard and confined aquifer during mid-to-late times. A larger dimensionless decay exponent (Ad) decreases aquitard drawdown and increases aquifer drawdown at late times. Sensitivity analysis showed aquitard drawdown experiences two peaks in sensitivity to β and Ad at early- or mid-times, affected by memory effect and decay exponent of aquitard K, signifying maximal impact at these specific intervals. This study provides a practical model to effectively manage groundwater resources by accurately reflecting aquitard memory and heterogeneity effects.

Integrating resource memory and cue-based territoriality to simulate movement dynamics: a process-explicit and pattern-oriented approach

Animal movement is central to ecology and of paramount importance to understand processes underlying the emergence of empirical space-use patterns. In this study, we investigated how integrating two well-established movement processes in the field of theoretical movement ecology, (i) memory-based resource use; and (ii) cue- or scent-based territoriality, affects emergent space-use patterns. We integrated the two mechanisms of space-use into a single individual-based modelling framework, and then quantified how varying degrees of each process affects emergent space-use patterns. Both mechanisms together led to the formation of exclusive, resource- and population density-dependent utilisation distributions, which were responsive to perturbations in the conspecific environment (i.e., removing individuals). Utilisation distribution patterns (area and overlap) were primarily influenced by memory and scent decay rate parameters, controlling an animal's inherent exploratory tendency and its desire to avoid conspecifics respectively. Model application to a population of feral cats demonstrated that general space-use measures (e.g., median UD area, resident and transient dynamics) could be approximated through simulation, though replication of finer-scale space use patterns (e.g., cumulative UD area) was poorly matched. Though application of mechanistic movement models remains both challenging and limited, the potential of such endeavours to ultimately predict population response cannot be overstated.