Temporal Prediction Errors Affect Short-Term Memory Scanning Response Time

2016 ◽  
Vol 63 (6) ◽  
pp. 333-342
Author(s):  
Roberto Limongi ◽  
Angélica M. Silva
Keyword(s):  
Response Time ◽  
Short Term Memory ◽  
Estimation Error ◽  
Predictive Coding ◽  
Time Estimation ◽  
Prediction Errors ◽  
Memory Scanning ◽  
Short Term ◽  
Term Memory ◽  
Temporal Prediction

Abstract. The Sternberg short-term memory scanning task has been used to unveil cognitive operations involved in time perception. Participants produce time intervals during the task, and the researcher explores how task performance affects interval production – where time estimation error is the dependent variable of interest. The perspective of predictive behavior regards time estimation error as a temporal prediction error (PE), an independent variable that controls cognition, behavior, and learning. Based on this perspective, we investigated whether temporal PEs affect short-term memory scanning. Participants performed temporal predictions while they maintained information in memory. Model inference revealed that PEs affected memory scanning response time independently of the memory-set size effect. We discuss the results within the context of formal and mechanistic models of short-term memory scanning and predictive coding, a Bayes-based theory of brain function. We state the hypothesis that our finding could be associated with weak frontostriatal connections and weak striatal activity.

Deep learning reservoir porosity prediction based on multilayer long short-term memory network

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. WA213-WA225
Author(s):  
Wei Chen ◽  
Liuqing Yang ◽  
Bei Zha ◽  
Mi Zhang ◽  
Yangkang Chen
Keyword(s):  
Oil And Gas ◽  
High Efficiency ◽  
Short Term Memory ◽  
Southern China ◽  
Prediction Errors ◽  
Short Term ◽  
Drilling Depth ◽  
Term Memory ◽  
Prediction Task ◽  
Long Short Term Memory

The cost of obtaining a complete porosity value using traditional coring methods is relatively high, and as the drilling depth increases, the difficulty of obtaining the porosity value also increases. Nowadays, the prediction of fine reservoir parameters for oil and gas exploration is becoming more and more important. Therefore, high-efficiency and low-cost prediction of porosity based on logging data is necessary. We have developed a machine-learning method based on the traditional long short-term memory (LSTM) model, called multilayer LSTM (MLSTM), to perform the porosity prediction task. We used three different wells in a block in southern China for the prediction task, including a training well and two test wells. One test well has the same logging data type as the training well, whereas the other test well differs from the training well in the logging depth and parameter types. Two different types of test data sets are used to detect the generalization ability of the network. A set of data was used to train the MLSTM network, and the hyperparameters of the network were adjusted through experimental accuracy feedback. We also tested the performance of the network using two sets of log data from different regions, including generalization and sensitivity of the network. During the training phase of the porosity prediction model, the developed MLSTM establishes a minimized objective function, uses the Adam optimization algorithm to update the weight of the network, and adjusts the network hyperparameters to select the best target according to the feedback of the network accuracy. Compared with conventional sequence neural networks, such as the gated recurrent unit and recurrent neural network, the logging data experiments show that MLSTM has better robustness and accuracy in depth sequence prediction. Especially, the porosity value at the depth inflection point can be better predicted when the trend of the depth sequence was predicted. This framework is expected to reduce the porosity prediction errors when data are insufficient and log depths are different.

scholarly journals Short-term memory scanning viewed as exemplar-based categorization.

2011 ◽  
Vol 118 (2) ◽  
pp. 280-315 ◽  
Author(s):  
Robert M. Nosofsky ◽  
Daniel R. Little ◽  
Christopher Donkin ◽  
Mario Fific
Keyword(s):  
Short Term Memory ◽  
Memory Scanning ◽  
Short Term ◽  
Term Memory

scholarly journals Order information in short-term memory and time estimation

1999 ◽  
Vol 27 (1) ◽  
pp. 54-62 ◽  
Author(s):  
Claudette Fortin ◽  
Nathalie Massé
Keyword(s):  
Short Term Memory ◽  
Time Estimation ◽  
Order Information ◽  
Short Term ◽  
Term Memory

scholarly journals The diffusion model of reaction time for recent negative probes

2020 ◽  
Vol 13 (1) ◽  
pp. 35-50
Author(s):  
B.B. Velichkovsky ◽  
F.R. Sultanova ◽  
D.V. Tatarinov ◽  
A.A. Kachina
Keyword(s):  
Diffusion Model ◽  
Drift Rate ◽  
Short Term Memory ◽  
Reaction Times ◽  
Long Term Memory ◽  
Memory Scanning ◽  
Short Term ◽  
Cognitive Representations ◽  
Term Memory ◽  
Scanning Task

The study investigates the problem of information displacement from short-term memory. In two experiments, reaction times for recent negative probes were analyzed in the Sternberg’s memory scanning task. The diffusion model of reaction times was used with parameters estimated with the fast-dm software. It was found (experiment 1) that recent negative probes are characterized by a reduction in the speed of information accumulation (drift rate). This suggests residual activation of irrelevant cognitive representation in memory after they have been displaced from short-term memory. It was also found (experiment 2) that negative probes semantically related to items in a preceding target set (semantic recent negative probes) are characterized by a similar decrease in the drift rate. This suggests activation spreading from irrelevant cognitive representations displaced from short-term memory along semantic connections and identifies activated long-term memory as the target of information displacement from short-term memory. Additional mechanisms of short-term memory scanning (negative priming and dynamic decision thresholds) are discussed.

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