scholarly journals Dynamics of serial position change in probe-recognition task

Psihologija ◽  
2002 ◽  
Vol 35 (3-4) ◽  
pp. 261-285
Author(s):  
Mario Fific

Relationship between practice and serial position effects was investigated, in order to obtain more evidence for underlying short-term memory processes. The investigated relationship is termed the dynamics of serial position change. To address this issue, the present study investigated mean latency, errors, and performed Ex-Gaussian convolution analysis. In six-block trials the probe-recognition task was used in the so-called fast experimental procedure. The serial position effect was significant in all six blocks. Both primacy and recency effects were detected, with primacy located in the first two blocks, producing a non-linear serial position effect. Although the serial position function became linear from the third block on, the convolution analysis revealed a non-linear change of the normal distribution parameter, suggesting special status of the last two serial positions. Further, separation of convolution parameters for serial position and practice was observed, suggesting different underlying mechanisms. In order to account for these findings, a strategy shift mechanism is suggested, rather then a mechanism based on changing the manner of memory scanning. Its influence is primarily located at the very beginning of the experimental session. The pattern of results of errors regarding the dynamics of serial position change closely paralleled those on reaction times. Several models of short-term memory were evaluated in order to account for these findings.

1998 ◽  
Vol 87 (1) ◽  
pp. 323-327 ◽  
Author(s):  
Raymond Bruyer ◽  
Mélanie Vanberten

Properties of short-term memory for faces (Exp. 1) were investigated in 40 young and 30 elderly persons and compared with short-term memory for nonverbal shapes (Exp. 2) with 30 new persons in a young group and an elderly one. Young subjects displayed a U-shaped curve for both kinds of stimuli, and elderly subjects displayed a U-shaped curve, but the recency effect was abolished for faces (in one condition). This suggests a possible specific short-term store for faces.


2020 ◽  
Vol 51 (6) ◽  
pp. 1358-1376
Author(s):  
Wei Xu ◽  
Yanan Jiang ◽  
Xiaoli Zhang ◽  
Yi Li ◽  
Run Zhang ◽  
...  

Abstract Deep learning has made significant advances in methodologies and practical applications in recent years. However, there is a lack of understanding on how the long short-term memory (LSTM) networks perform in river flow prediction. This paper assesses the performance of LSTM networks to understand the impact of network structures and parameters on river flow predictions. Two river basins with different characteristics, i.e., Hun river and Upper Yangtze river basins, are used as case studies for the 10-day average flow predictions and the daily flow predictions, respectively. The use of the fully connected layer with the activation function before the LSTM cell layer can substantially reduce learning efficiency. On the contrary, non-linear transformation following the LSTM cells is required to improve learning efficiency due to the different magnitudes of precipitation and flow. The batch size and the number of LSTM cells are sensitive parameters and should be carefully tuned to achieve a balance between learning efficiency and stability. Compared with several hydrological models, the LSTM network achieves good performance in terms of three evaluation criteria, i.e., coefficient of determination, Nash–Sutcliffe Efficiency and relative error, which demonstrates its powerful capacity in learning non-linear and complex processes in hydrological modelling.


1977 ◽  
Vol 9 (4) ◽  
pp. 319-323 ◽  
Author(s):  
Richard A. Magill ◽  
Martha Nann Dowell

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