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Each of 52 (25 female and 27 male) high school students reproduced from memory 1000 eight-digit numbers after viewing each number for 5 s. Subjects were given unlimited time to reproduce the numbers and were allowed to change their reproductions. The range of errors was very large: from 71 to 2231 out of 8000 digits reproduced by each subject. Every subject showed a serial position effect and almost the same effect—about 70% of subjects made the greatest number of errors at the seventh digit. Female subjects made significantly more errors than did the males. Every subject improved his or her score by making changes. Data are given on the relative difficulty of recalling each of the 10 digits, the 100 doublets (pairs of digits), and the 1000 triplets (sets of three digits).

"Long- and Short-term Memory Structure in Political Decision Making" published on by Oxford University Press.

If temporal contexts contribute to serial order in short-term memory is not well understood. Few studies have shown that the temporal context is related to serial order. However, contradicting results from other studies show that there is no relationship between serial recognition and temporal recognition. There is previous research showed that there indeed existed a relationship between temporal recognition and serial recognition. The current study tries to replicate the results from this study through an alternative analysis pipeline to understand the robustness of these results. Using a drift diffusion model, we try to understand the robustness and replicability of the previous research-- Short-term recognition memory for serial order and timing. In results obtained from both the studies, we see that there is no relationship between temporal and serial recognition.

Communication between ground controllers and pilots was simulated in a short-term memory task in order to explore sources of memory errors in the air traffic control system. As expected from prior short-term memory research, two major determinants of error probability were (1) amount of information that the pilot has to process in a given time and (2) retention interval between the time information is transmitted from the controller and the time it is acted on (recalled) by the pilot. Additionally, the manner of encoding numerical information was varied. The result of this manipulation indicated that, as suggested by recent research in cognitive psychology, the current information-encoding scheme has substantial room for improvement in terms of minimizing memory failure.

ZusammenfassungMethoden der künstlichen Intelligenz haben sich in den letzten Jahren zu essenziellen Bestandteilen fast aller Bereiche von Wissenschaft und Technik entwickelt. Dies gilt auch für die Hydrologie: Vielschichtige neuronale Netzwerke – auch bekannt als Modelle des Deep Learning – ermöglichen hier Vorhersagen von Niederschlagsabflussmengen in zuvor unerreichter Präzision.Dieser Beitrag beleuchtet das Potenzial von Deep Learning für wasserwirtschaftliche Anwendungen. Der erste Teil des Artikels zeigt, wie sogenannte Long Short-Term Memory-Netzwerke – eine spezifisch für Zeitreihen entwickelte Methode des Deep Learnings – für die Niederschlags-Abfluss-Modellierung verwendet werden, und wie diese für eine Reihe hydrologischer Probleme bessere Ergebnisse als jedes andere bekannte hydrologische Modell erzielen. Der zweite Teil demonstriert wesentliche Eigenschaften der Long Short-Term Memory-Netzwerke. Zum einen zeigen wir, dass diese Netzwerke beliebige Daten verarbeiten können. Dies erlaubt es, mögliche synergetische Effekte aus unterschiedlichen meteorologischen Datensätzen zu extrahieren und damit die Modellgüte zu verbessern. Zum anderen stellen wir dar, wie relevante hydrologische Prozesse (wie z. B. das Akkumulieren und Schmelzen von Schnee) innerhalb der Modelle abgebildet werden, ohne dass diese spezifisch darauf trainiert wurden.

AbstractTwo experiments examined the hypothesis that developing visual attentional mechanisms influence infants' Visual Short‐Term Memory (VSTM) in the context of multiple items. Five‐ and 10‐month‐old infants (N = 76) received a change detection task in which arrays of three differently colored squares appeared and disappeared. On each trial one square changed color and one square was cued; sometimes the cued item was the changing item, and sometimes the changing item was not the cued item. Ten‐month‐old infants exhibited enhanced memory for the cued item when the cue was a spatial pre‐cue (Experiment 1) and 5‐month‐old infants exhibited enhanced memory for the cued item when the cue was relative motion (Experiment 2). These results demonstrate for the first time that infants younger than 6 months can encode information in VSTM about individual items in multiple‐object arrays, and that attention‐directing cues influence both perceptual and VSTM encoding of stimuli in infants as in adults.

This article reports the initial findings of a two-part test of tactile concentration and short-term memory involving the reproduction of the order of finger stimulation—a forward series and a backward series. A newly devised instrument, the Finger Knocking Box, was used for the mechanical stimulation of fingers and the recording of responses by 65 totally blind or partially sighted subjects of average intelligence.

AbstractOpponents of the efficient markets hypothesis argue that predictability reflects the psychological factors and "fads" of irrational investors in a speculative market. In that, conventional time series analysis often fails to give an accurate forecast for financial processes due to inherent noise patterns, fat tails, and nonlinear components. A recent stream of literature on behavioral finance has revealed that boundedly rational agents using simple rules of thumb for their decisions under uncertainty provides a more realistic description of human behavior than perfect rationality with optimal decision rules. Consequently, the application of technical analysis in trading could produce high returns. Machine learning techniques have been employed in economic systems in modeling nonlinearities and simulating human behavior. In this study, we expand the literature that evaluates return sign forecasting ability by introducing a recurrent neural network approach that combines heuristic learning and short‐term memory emulation, thus mimicking the decision‐making process of boundedly rational agents. We investigate the relative direction‐of‐change predictability of the neural network structure implied by the Lee–White–Granger test as well as compare it to other well‐established models for the DJIA index. Moreover, we examine the relationship between stock return volatility and returns. Overall, the proposed model presents high profitability, in particular during "bear" market periods.

Air temperature is one of the main factors for describing the weather behaviour in the earth. Since Indonesia is located on and near equator, then monitoring the air temperature is needed to determine either global climate change occurs or not. Climate change can have an impact on biological growth in various fields. For instance, climate change can affect the quality of production and growth of animal and plants. Therefore, air temperature prediction is important to meteorologists and Indonesian government to provide information in many sectors. Various prediction algorithms have been used to predict temperature and produce different accuracy. In this study, the deep learning method with Long Short-Term Memory (LSTM) model is used to predict air temperature. Here, the results show that LSTM model with one layer and Adaptive Moment Estimation (ADAM) optimizer produce accuracy which is 32% of , 0.068 of MAE and 0.99 of RMSE. Moreover, here, ADAM optimizer is found better than Stochastic Gradient Descent (SGD) optimizer.

Performance in a memory-span task using eight-letter sequences was explored as a function of presentation rate (.5, .75, 1.0, 2.0, and 3.0 sec/item) and presentation mode (visual, auditory, simultaneous visual and auditory, and mixed visual and auditory). Results indicate that performance in the mixed mode was inferior to the other three modes, but the other modes did not differ from each other. As presentation rate decreased, performance improved. These results are consistent with current theories of memory and indicate that the mode in which alphanumeric information is displayed is unimportant provided the modes are not mixed.