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Related Concept Videos

Introduction to Cognitive Psychology01:20

Introduction to Cognitive Psychology

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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.
This field emerged in the mid-20th century, following a period dominated by behaviorism, which...
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Decision Making01:20

Decision Making

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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
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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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Cognitivism01:17

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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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Decision Making: Traditional Method01:14

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The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Harnessing Computational Complexity Theory to Model Human Decision-making and Cognition.

Juan Pablo Franco1, Carsten Murawski1

  • 1Centre for Brain, Mind and Markets, The University of Melbourne.

Cognitive Science
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Computational complexity theory offers a new framework for understanding human cognition. This approach helps explain how limited cognitive resources manage complex tasks and influences behavior.

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Decision Science

Background:

  • Cognitive science seeks to understand how humans process information and navigate complex environments.
  • Human cognitive resources are limited, posing challenges for processing vast amounts of data.

Purpose of the Study:

  • To propose computational complexity theory as a framework for understanding cognitive mechanisms.
  • To explore the relationship between task complexity and human behavior.

Main Methods:

  • Applying computational complexity theory to analyze cognitive tasks.
  • Presenting empirical evidence to support the proposed framework.

Main Results:

  • Computational complexity theory provides a robust framework for evaluating cognitive resource requirements.
  • The theory offers insights into how task complexity influences information processing demands and human behavior.

Conclusions:

  • Computational complexity theory can significantly advance cognitive science by elucidating the interplay between task demands and cognitive system function.
  • Further research is needed to explore its application in human decision-making and broader cognitive science.