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Introduction to Cognitive Psychology01:20

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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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Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction
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Approximations of algorithmic and structural complexity validate cognitive-behavioral experimental results.

Hector Zenil1,2,3, James A R Marshall4, Jesper Tegnér5

  • 1Machine Learning Group, Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom.

Frontiers in Computational Neuroscience
|February 10, 2023
PubMed
Summary
This summary is machine-generated.

We developed a new method to measure behavioral complexity using algorithmic (Kolmogorov) complexity and logical depth. This tool reveals biases in decision-making for ants, fruit flies, rats, and humans.

Keywords:
Shannon Entropyant behaviorbehavioral biasesbehavioral sequencescommunication complexitytradeoffs of complexity measures

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Area of Science:

  • Cognitive Science
  • Animal Behavior
  • Artificial Intelligence

Background:

  • Objectively characterizing behavioral complexity is crucial for understanding cognition and AI.
  • Existing methods struggle with short behavioral sequences.
  • Algorithmic (Kolmogorov) complexity and logical depth offer new quantitative approaches.

Purpose of the Study:

  • To develop and apply a computational toolbox for objectively measuring behavioral complexity.
  • To analyze decision-making processes in animal behavior and human cognition.
  • To investigate the role of environmental factors and algorithmic biases.

Main Methods:

  • Numerical approximation of algorithmic (Kolmogorov) complexity (K).
  • Approximation of structural (Bennett's Logical Depth) complexity (LD).
  • Application to studies of ant foraging, fruit fly flight, and rat predator-prey strategies.

Main Results:

  • Ants modulate behavioral complexity based on environmental conditions.
  • Fruit flies in featureless environments deviate from random flight, suggesting navigational strategies.
  • Rat behavior in competition simulates algorithmic randomness, matching competitor complexity.
  • Human perception of randomness indicates algorithmic biases in reasoning.

Conclusions:

  • The developed toolbox provides an objective measure of behavioral complexity.
  • Animal and human decision-making exhibits algorithmic biases, not just faulty computation.
  • Environmental constraints significantly influence internal decision processes.