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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).

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.

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.

Abstract Estimates of government expenditure for the next period are very important in the government, for instance for the Ministry of Finance of the Republic of Indonesia, because this can be taken into consideration in making policies regarding how much money the government should bear and whether there is sufficient availability of funds to finance it. As is the case in the health, education and social fields, modeling technology in machine learning is expected to be applied in the financial sector in government, namely in making modeling for spending predictions. In this study, it is proposed the application of Long Short-Term Memory (LSTM) Model for expenditure predictions. Experiments show that LSTM model using three hidden layers and the appropriate hyperparameters can produce Mean Square Error (MSE) performance of 0.2325, Root Mean Square Error (RMSE) of 0.4820, Mean Average Error (MAE) of 0.3292 and Mean Everage Presentage Error (MAPE) of 0.4214. This is better than conventional modeling using the Auto Regressive Integrated Moving Average (ARIMA) as a comparison model. ; Perkiraan pengeluaran belanja pemerintah untuk periode kedepan merupakan hal yang sangat penting di pemerintah dalam hal ini pada Kementerian Keuangan Republik Indonesia, karena hal ini dapat dijadikan bahan pertimbangan dalam mengambil kebijakan terkait berapa nilai uang yang harus ditanggung pemerintah serta apakah ada ketersediaan dana yang cukup dalam membiayai belanja tersebut untuk periode yang akan datang. Seperti halnya pada bidang kesehatan, pendidikan dan sosial, teknologi pemodelan pada Machine Learning diharapkan dapat diterapkan di bidang keuangan pada pemerintahan, yaitu dalam membuat pemodelan untuk prediksi belanja. Pada penelitian ini, diusulkan penerapan model Long Short-Term Memory (LSTM) untuk prediksi belanja. Eksperimen menunjukkan model LSTM dengan menggunakan tiga hidden layers dan hyperparameter yang tepat dapat menghasilkan performa Mean Square Error (MSE) sebesar 0.2325, Root Mean Square Error (RMSE) sebesar 0.4820, Mean Average Error (MAE) sebesar 0.3292 dan Mean Everage Presentage Error (MAPE) sebesar 0.4214. Ini lebih baik dibandingkan pemodelan konvensional menggunakan Auto Regressive Integrated Moving Average (ARIMA) sebagai model pembanding.

Three experiments were performed that compared recall for synthetic and natural lists of monosyllabic words. In the first experiment, presentation intervals of 1, 2, and 5 s per word were used. Although free recall was consistently poorer overall for the synthetic lists at all presentation rates, the decrement for synthetic stimuli did not increase differentially with faster rates. In a second experiment, zero, three, and six digits were presented visually for retention prior to free recall of each spoken word list in a preload paradigm. Fewer subjects were able to correctly recall all of the digits for the six-digit list than the three-digit list when the following word lists were synthetic. The third experiment required ordered recall of lists of natural and synthetic words. Differences in ordered recall between the synthetic and natural word lists were substantially larger for the primacy portion of the serial position curve than the recency portion. These results indicate that difficulties observed in the perception and comprehension of synthetic speech are due, in part, to increased processing demands in short-term memory.

The text classification process has been well studied, but there are still many improvements in the classification and feature preparation, which can optimize the performance of classification for specific applications. In the paper we implemented dictionary based approach and long-short term memory approach. In the first approach, dictionaries will be padded based on field's specific input and use automation technology to expand. The second approach, long short term memory used word2vec technique. This will help us in getting a comprehensive pipeline of end-to-end implementations. This is useful for many applications, such as sorting emails which are spam or ham, classifying news as political or sports-related news, etc