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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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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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Cognitivism01:17

Cognitivism

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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.
Previously dominated by behaviorism, which prioritized observable behaviors and largely ignored mental processes, psychology transformed in the 1950s. Cognitive psychologists argue that understanding how we think and process...
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Artificial neural networks for model identification and parameter estimation in computational cognitive models.

Milena Rmus1, Ti-Fen Pan1, Liyu Xia1

  • 1UC Berkeley.

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Artificial neural networks (ANNs) offer a novel method for cognitive model fitting, bypassing complex likelihood calculations. This approach enables quantitative analysis of previously intractable cognitive models, advancing computational cognitive science.

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Area of Science:

  • Computational cognitive science
  • Cognitive modeling
  • Artificial intelligence

Background:

  • Cognitive models formalize human information processing and individual differences using parameters.
  • Model comparison identifies theories best explaining empirical data.
  • Traditional cognitive modeling relies on likelihood estimation, which is computationally intractable for many complex models.

Approach:

  • We introduce an artificial neural network (ANN) as a tool for cognitive model fitting.
  • This ANN approach bypasses computationally intensive likelihood estimation by directly mapping data to model parameters and identity.
  • The method is tested on cognitive models with inter-trial dependencies, like reinforcement learning models.

Key Points:

  • The ANN approach successfully performs both parameter estimation and model identification.
  • It accommodates cognitive models that are intractable for traditional likelihood-based fitting methods.
  • This expands the range of cognitive theories amenable to quantitative investigation.

Conclusions:

  • Artificial neural networks provide a viable alternative for fitting complex cognitive models.
  • This simulation-based approach broadens the scope of quantitative cognitive modeling.
  • Researchers can now explore a wider array of cognitive theories and their parameters.