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Related Concept Videos

Cognitive Learning01:21

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
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Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem-solving, as well as other cognitive processes. Cognitive psychology studies how information is processed and manipulated in remembering, thinking, and knowing.
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Cognitive psychology emerged as a significant field in the mid-20th century. It focused on understanding humans' internal mental processes. This approach emphasizes how people perceive, remember, think, and solve problems—elements critical to human cognition.
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Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or...
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Social psychology examines the complex interplay between individual mental processes and social interactions. Historically, the field was divided into two domains: social behavior and social cognition. Researchers focusing on social behavior analyzed actions within social contexts, such as conformity, aggression, or cooperation. Meanwhile, social cognition researchers investigated how people perceive, interpret, and mentally represent their social environments. However, modern perspectives no...
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Related Experiment Video

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PsychRNN: An Accessible and Flexible Python Package for Training Recurrent Neural Network Models on Cognitive Tasks.

Daniel B Ehrlich1, Jasmine T Stone2, David Brandfonbrener2,3

  • 1Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06520-8074.

Eneuro
|December 17, 2020
PubMed
Summary

PsychRNN is a new Python package making recurrent neural networks (RNNs) accessible for neuroscience research. It simplifies training RNNs on cognitive tasks, enabling new investigations into neural computation and behavior.

Keywords:
cognitive taskcomputational modeldeep learningrecurrent neural networktraining

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Area of Science:

  • Computational Neuroscience
  • Systems Neuroscience
  • Cognitive Neuroscience

Background:

  • Recurrent neural networks (RNNs) are increasingly used in neuroscience for modeling cognitive tasks.
  • Training RNNs with deep learning methods presents technical barriers for many researchers.
  • Existing tools limit the investigation of neural representations and circuit mechanisms.

Purpose of the Study:

  • Introduce PsychRNN, an accessible Python package for training RNNs on cognitive tasks.
  • Lower the technical barrier for researchers to use RNNs in computational and systems neuroscience.
  • Facilitate the study of neural computations and behavior using trained RNN models.

Main Methods:

  • Developed PsychRNN, a Python package using TensorFlow as a backend.
  • Enabled task definition and RNN training using only Python and NumPy.
  • Implemented features for neurobiological constraints, modular task specification, and curriculum learning (task shaping).

Main Results:

  • PsychRNN provides an accessible framework for training RNNs without deep-learning expertise.
  • The package supports neurobiologically relevant constraints and modular task design.
  • Task shaping (curriculum learning) can be implemented to study learning trajectories.

Conclusions:

  • PsychRNN lowers the barrier to entry for using trained RNNs in neuroscience research.
  • The framework facilitates the investigation of neural representations and circuit mechanisms underlying cognition.
  • PsychRNN enables flexible and extensible research into computational models of the brain.