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Related Concept Videos

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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Reason and Intuition01:37

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The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
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Reasoning01:30

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Reasoning is the action of thinking about something in a logical, sensible way. It is integral to problem-solving, decision-making, and critical thinking. Reasoning can be inductive or deductive. Reasoning involves transforming information into conclusions, which is essential for problem-solving, decision-making, and critical thinking.
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Deductive Reasoning01:16

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Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning, which means that it uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid.
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Cognitive Dissonance01:38

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Social psychologists have documented that feeling good about ourselves and maintaining positive self-esteem is a powerful motivator of human behavior (Tavris & Aronson, 2008). In the United States, members of the predominant culture typically think very highly of themselves and view themselves as good people who are above average on many desirable traits (Ehrlinger, Gilovich, & Ross, 2005). Often, our behavior, attitudes, and beliefs are affected when we experience a threat to our...
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Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
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C. elegans Positive Butanone Learning, Short-term, and Long-term Associative Memory Assays
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Short-term cognitive networks, flexible reasoning and nonsynaptic learning.

Gonzalo Nápoles1, Frank Vanhoenshoven1, Koen Vanhoof1

  • 1Faculty of Business Economics, Hasselt University, Belgium.

Neural Networks : the Official Journal of the International Neural Network Society
|April 12, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces Short-term Cognitive Networks, a novel neural system improving upon Fuzzy Cognitive Maps for knowledge-based inference. It offers enhanced prediction accuracy and a new learning algorithm for machine learning applications.

Keywords:
Cognitive mappingModelingNonsynaptic learningShort-term memorySimulation

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Neuroscience

Background:

  • Existing machine learning methods for automated reasoning often lack the ability to infer from predefined knowledge structures.
  • Fuzzy Cognitive Maps (FCMs) offer flexibility in handling external knowledge but suffer from limited prediction horizons and underdeveloped learning algorithms.
  • Accurate prediction and extended inference capabilities are crucial for advancing knowledge-based reasoning systems.

Purpose of the Study:

  • To introduce a novel neural system, Short-term Cognitive Networks (SCNs), designed to overcome limitations in existing FCMs for knowledge-based inference.
  • To develop a nonsynaptic learning algorithm that enhances network performance without altering the established weight matrix.
  • To derive a reliable stop condition for the learning algorithm to prevent non-productive iterations and ensure significant error reduction.

Main Methods:

  • Development of Short-term Cognitive Networks (SCNs), a recurrent neural network model.
  • Implementation of a nonsynaptic learning algorithm for performance enhancement.
  • Derivation of a stop condition to optimize the learning process and global simulation error.

Main Results:

  • The proposed SCN model demonstrates effectiveness in regression and pattern completion tasks.
  • The nonsynaptic learning algorithm successfully improves network performance.
  • The derived stop condition effectively prevents unnecessary iterations while minimizing global simulation error.

Conclusions:

  • Short-term Cognitive Networks offer a promising advancement over traditional FCMs for knowledge-based inference and prediction.
  • The novel nonsynaptic learning algorithm and derived stop condition contribute to more accurate and efficient machine learning models.
  • This research addresses key limitations in FCMs, paving the way for more robust automated reasoning systems.