In debates in philosophy and cognitive science concerning short-term memory mechanisms and perceptual experience, most discussion has focused on the working memory and the various forms of sensory memory such as iconic memory. In this paper, I present a summary of some evidence for a proposed further form of memory termed conceptual short-term memory. I go on to outline some of the ways in which this additional distinctive sort of short-term memory might be of (...) relevance to ongoing philosophical debates, specifically in relation to questions about high-level perceptual phenomenology, the relationship between consciousness and reportability, and the boundary between cognition and perception. I conclude that conceptual short-term memory offers a promising new direction of research and philosophical investigation. (shrink)

Classification learning is a preeminent human ability within the animal kingdom but the key mechanisms of brain networks regulating learning remain mostly elusive. Recent neuroimaging advancements have depicted human brain as a complex graph machinery where brain regions are nodes and coherent activities among them represent the functional connections. While long-term motor memories have been found to alter functional connectivity in the resting human brain, a graph topological investigation of the short-time effects of learning are still not widely (...) investigated. For instance, classification learning is known to orchestrate rapid modulation of diverse memory systems like short-term and visual working memories but how the brain functional connectome accommodates such modulations is unclear. We used publicly available repositories (openfmri.org) selecting three experiments, two focused on short-term classification learning along two consecutive runs where learning was promoted by trial-by-trial feedback errors, while a further experiment was used as supplementary control. We analyzed the functional connectivity extracted from BOLD fMRI signals, and estimated the graph information processing in the cerebral networks. The information processing capability, characterized by complex network statistics, significantly improved over runs, together with the subject classification accuracy. Instead, null-learning experiments, where feedbacks came with poor consistency, did not provoke any significant change in the functional connectivity over runs. We propose that learning induces fast modifications in the overall brain network dynamics, definitely ameliorating the short-term potential of the brain to process and integrate information, a dynamic consistently orchestrated by modulations of the functional connections among specific brain regions. (shrink)

Short-term traffic flow prediction is an effective means for intelligent transportation system to mitigate traffic congestion. However, traffic flow data with temporal features and periodic characteristics are vulnerable to weather effects, making short-term traffic flow prediction a challenging issue. However, the existing models do not consider the influence of weather changes on traffic flow, leading to poor performance under some extreme conditions. In view of the rich features of traffic data and the characteristic of being vulnerable (...) to external weather conditions, the prediction model based on traffic data has certain limitations, so it is necessary to conduct research studies on traffic flow prediction driven by both the traffic data and weather data. This paper proposes a combined framework of stacked autoencoder and radial basis function neural network to predict traffic flow, which can effectively capture the temporal correlation and periodicity of traffic flow data and disturbance of weather factors. Firstly, SAE is used to process the traffic flow data in multiple time slices to acquire a preliminary prediction. Then, RBF is used to capture the relation between weather disturbance and periodicity of traffic flow so as to gain another prediction. Finally, another RBF is used for the fusion of the above two predictions on decision level, obtaining a reconstructed prediction with higher accuracy. The effectiveness and robustness of the proposed model are verified by experiments. (shrink)

Previous studies of verbal short-term memory indicate that STM for serial order may be linked to language development and developmental language disorder. To clarify whether a domain-general mechanism is impaired in DLD, we studied the relations between age, non-verbal serial STM, and language competence. We hypothesized that non-verbal serial STM differences between groups of children with DLD and typically developing children are linked to their language acquisition differences. Fifty-one children with DLD and sixty-six TD children participated as part (...) of the HelSLI project in this cross-sectional study. The children were 4–6-year-old monolingual native Finnish speakers. They completed several tests of language and cognitive functioning, as well as new game-like tests of visual and auditory non-verbal serial STM. We used regression analyses to examine how serial STM moderates the effect of age on language. A non-verbal composite measure of serial visual and auditory STM moderated cross-sectional development of receptive language in the children with DLD. This moderation was not observed in the TD children. However, we found more rapid cross-sectional development of non-verbal serial STM in the TD children than in the children with DLD. The results suggest that children with DLD may be more likely to have compromised general serial STM processing and that superior non-verbal serial STM may be associated with better language acquisition in children with DLD. (shrink)

Sub-Saharan emerging countries experience electrical shortages resulting in power rationing, which ends up hampering economic activities. This paper proposes an approach for very short-term blackout forecast in grid-tied PV systems operating in low reliability weak electric grids of emerging countries. A pilot project was implemented in Arusha-Tanzania; it mainly comprised of a PV-inverter and a lead-acid battery bank connected to the local electricity utility company, Tanzania Electric Supply Company Limited. A very short-term power outage prediction model (...) framework based on a hybrid random forest algorithm was developed using open-source Python machine learning libraries and using a dataset generated from the pilot project’s experimental microgrid. Input data sampled at a 15-minute interval included day of the month, weekday, hour, supply voltage, utility line frequency, and previous days’ blackout profiles. The model was composed of an adaptive similar day module that predicts 15 minutes ahead from a sliding window lookup table spanning 2 weeks prior to the prediction target day, after which ASD prediction was fused with RF prediction, giving a final optimised RF-ASD blackout prediction model. Furthermore, the efficacy analysis of the short-term blackout prediction of the formulated RF, ASD, and RF-ASD regression and classification algorithms was compared. Considering the stochastic nature of blackouts, their performance was found to be fair in short-term blackout predictions of the test site’s weak grid using limited input data from the point of coupling of the user. The models developed were only able to predict blackouts if they occurred frequently and contiguously, but they performed poorly if they were sparse or dispersed. (shrink)

This paper provides an interpretation of Hans-Jörg Rheinberger’s notions of epistemic things and historical epistemology . I argue that Rheinberger’s approach articulates a unique contribution to current debates about integrated HPS, and I propose some modifications and extensions of this contribution. Drawing on examples from memory research, I show that Rheinberger is right to highlight a particular feature of many objects of empirical research (“epistemic things”)—especially in the contexts of exploratory experimentation—namely our lack of knowledge about them. I argue that (...) this analysis needs to be supplemented with an account of what scientists do know, and in particular, how they are able to attribute rudimentary empirical contours to objects of research. These contours are closely connected to paradigmatic research designs, which in turn are tied to basic methodological rules for the exploration of the purported phenomena. I suggest that we engage with such rules in order to develop our own normative (epistemological) categories, and I tie this proposal to the idea of a methodological naturalism in philosophy of science. (shrink)

As one of the key technologies for accelerating the construction of the ubiquitous Internet of Things, demand response not only guides users to participate in power market operations but also increases the randomness of grid operations and the difficulty of load forecasting. In order to solve the problem of rough feature engineering processing and low prediction accuracy, a short-term load forecasting model of LSTM neural network considering demand response is proposed. First of all, in view of the strong (...) randomness and complexity of input features, the weighted method is used to process multiple input features to strengthen the contribution of effective features and tap the potential value of features. Secondly, an improved genetic algorithm is used to obtain the best LSTM parameters; finally, the special gate structure of the LSTM model is used to selectively control the influence of input variables on the model parameters and perform load forecasting. The experimental results show that the research has high prediction accuracy and application value and provides a new way for the development of power load forecasting. (shrink)

Although visual long-term memory (VLTM) and visual short-term memory (VSTM) can be distinguished from each other (and from visual sensory storage [SS]), they are embodied within the same modality-specific brain regions, but in very different ways: VLTM as patterns of connectivity and VSTM as patterns of activity. Perception and VSTM do not “activate” VLTM. They use VLTM to create novel patterns of activity relevant to novel circumstances.

The view that short-term memory should be conceived of as being a process based on the activation of long-term memory is inconsistent with neuropsychological evidence. Data from brain-damaged patients, showing specific patterns of impairment, are compatible with a vision of memory as a multiple-component system, whose different aspects, in neurologically unimpaired subjects, show a high degree of interaction.

Previous research has shown that short-term fasting in healthy individuals is associated with changes in risky decision-making. The current experiment was designed to examine the influence of short-term fasting in healthy individuals on four types of impulsivity: reflection impulsivity, risky decision-making, delay aversion, and action inhibition. Participants were tested twice, once when fasted for 20 hours, and once when satiated. Participants demonstrated impaired action inhibition when fasted; committing significantly more errors of commission during a food-related Affective (...) Shifting Task. Participants also displayed decreased reflection impulsivity when fasted, opening significantly more boxes during the Information Sampling Task (IST). There were no significant differences in performance between fasted and satiated sessions for risky decision-making or delay aversion. These findings may have implications for understanding eating disorders such as Bulimia Nervosa (BN). Although BN has been characterised as a disorder of poor impulse control, inconsistent findings when comparing individuals with BN and healthy individuals on behavioural measures of impulsivity question this characterisation. Since individuals with BN undergo periods of short-term fasting, the inconsistent findings could be due to differences in the levels of satiation of participants. The current results indicate that fasting can affect performance on the IST, a measure of impulsivity previously studied in BN, whilst performance on other impulsivity measures were unaffected. However, the results from the IST were contrary to the original hypothesis and should be replicated before specific conclusions can be made. (shrink)

Future work is needed to establish that pure short-term memory is a coherent individual difference attribute that is separable from traditional compound short-term memory measures. Psychometric support for latent pure short-term memory capacity will provide an important starting point for future fine-grained analyses of the intrinsic factors that influence individual differences in math skills.

There is ample discussion of MBA self-serving values in the corporate social responsibility literature, and yet empirical studies regarding the corporate manifestations and consequences of those values are scant. In a comprehensive study of major US public corporations, we find that MBA CEOs are more apt than their non-MBA counterparts to engage in short-term strategic expedients such as positive earnings management and suppression of R&D, which in turn are followed by compromised firm market valuations.

In _Short-Term Psychodynamic Therapy with Children in Crisis, _Elisabeth Cleve presents the therapeutic stories of four children who have experienced trauma or are displaying dramatic clinical symptoms such as low self-esteem and anxiety. Exploring the situation between the individual child and the therapist, the therapeutic space and their experiences, each chapter follows the sessions and the progress made, concluding with a follow-up after the end of therapy. Cleve explores each case as it progresses, emphasising the inner strength of the (...) children and including the interactions between the therapist and the children’s parents. The focus of the psychotherapeutic encounter is in each case to help the child face the trauma, mourn what had been suffered and then move on in life with renewed strength. The final chapters explore the ethics of sharing case material and present Cleve’s reflections on working with traumatised children, and the book also includes forewords by Lars H. Gustafsson, paediatrician and associate professor of social medicine, and Björn Salomonsson, child psychoanalyst and researcher at Karolinska Institutet in Sweden. This warm and readable work will be insightful reading for child psychologists and psychotherapists and other clinicians working with children who have experienced trauma. It will also be of interest to readers wishing to learn more about the processes of psychotherapy with children. (shrink)

Short-term limb immobilization results in skeletal muscle decline, but the underlying mechanisms are incompletely understood. This study aimed to determine the neurophysiologic basis of immobilization-induced skeletal muscle decline, and whether repetitive Transcranial Magnetic Stimulation could prevent any decline. Twenty-four healthy young males underwent unilateral limb immobilization for 72 h. Subjects were randomized between daily rTMS using six 20 Hz pulse trains of 1.5 s duration with a 60 s inter-train-interval delivered at 90% resting Motor Threshold, or Sham rTMS (...) throughout immobilization. Maximal grip strength, EMG activity, arm volume, and composition were determined at 0 and 72 h. Motor Evoked Potentials were determined daily throughout immobilization to index motor excitability. Immobilization induced a significant reduction in motor excitability across time. The rTMS intervention increased motor excitability at 0 h. Despite daily rTMS treatment, there was still a significant reduction in motor excitability, loss in EMG activity, and a loss of maximal grip strength after immobilization. Interestingly, the increase in biceps and posterior forearm skinfold thickness with immobilization in Sham treatment was not observed following rTMS treatment. Reduced MEPs drive the loss of strength with immobilization. Repetitive Transcranial Magnetic Stimulation cannot prevent this loss of strength but further investigation and optimization of neuroplasticity protocols may have therapeutic benefit. (shrink)

ObjectiveTo investigate the short-term effect of music therapy combined with binaural frequency difference therapy on patients with consciousness disorder.Materials and methodsNinety patients with definite diagnosis of disorders of consciousness were selected. These patients were randomly divided into control group, experiment 1 group and experiment 2 group, with 30 patients in each group. The control group was treated with routine clinical treatment and rehabilitation. In experiment 1 group, music therapy was added to the control group. In experimental group 2, (...) music therapy combined with binaural α frequency difference therapy was added to the control group. All patients were assessed before and after 30 treatments. The assessment items included Glasgow Coma Scale, Coma Recovery Scale revised, electroencephalogram, upper somatosensory evoked potential, and brainstem auditory evoked potential.ResultsBefore treatment, there were no significant differences in GCS score, CRS-R score, USEP, BAEP, and EEG scores among the three groups. After 30 times of treatment, GCS score, CRS-R score, USEP, BAEP, and EEG scores in 3 groups were significantly higher than those before treatment. And the consciousness rate of experimental group 2 was better than experimental group 1, experimental group 1 was better than the control group and the difference was statistically significant.ConclusionMusic therapy combined with binaural α frequency difference therapy is more effective in stimulating DOC patients. (shrink)

The parking of cars is a globally recognized problem, especially at locations where there is a high demand for empty parking spaces. Drivers tend to cruise additional distances while searching for empty parking spaces during peak hours leading to problems, such as pollution, congestion, and driver frustration. Providing short-term predictions of parking availability would facilitate the driver in making informed decisions and planning their arrival to be able to choose parking locations with higher availability. Therefore, the aim of (...) this study is to provide short-term predictions of available parking spaces with a low volume of data. The open parking lot provides parking spaces free of charge and one such parking lot, located beside a shopping center, was selected for this study. Parking availability data for 21 days were collected where 19 days were used for training, while multiple periods of the remaining 2 days were used to test and evaluate the prediction methods. The test dataset consists of data from a weekday and a weekend. Based on the reviewed literature, three prediction methods suitable for short-term prediction were selected, namely, long short-term memory, seasonal autoregressive integrated moving average with exogenous variables, and the Ensemble-based method. The LSTM method is a deep learning-based method, while SARIMAX is a regression-based method, and the Ensemble method is based on decision trees and random forest to provide predictions. The performance of the three prediction methods with a low volume of data and the use of visitor trends data as an exogenous variable was evaluated. Based on the test prediction results, the LSTM and Ensemble-based methods provided better short-term predictions at multiple times on a weekday, while the Ensemble-based method provided better predictions over the weekend. However, the use of visitor trend data did not facilitate improving the predictions of SARIMAX and the Ensemble-based method, while it improved the LSTM prediction for the weekend. (shrink)