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Related Concept Videos

Typical Model Studies01:30

Typical Model Studies

Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
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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.
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Related Experiment Video

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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Exemplar models as a mechanism for performing Bayesian inference.

Lei Shi1, Thomas L Griffiths, Naomi H Feldman

  • 1University of California, 3210 Tolman Hall #1650, Berkeley, CA 94720-1650, USA.

Psychonomic Bulletin & Review
|August 13, 2010
PubMed
Summary
This summary is machine-generated.

Exemplar models, a type of psychological process model, can perform approximate Bayesian inference using importance sampling. These models effectively explain human performance in various cognitive tasks with few examples.

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Psychological Process Modeling

Background:

  • Probabilistic models are increasingly studied as explanations for human cognition.
  • Existing research often focuses on abstract problems and optimal solutions, not implementation mechanisms.
  • Exemplar models, using stored examples, are effective for tasks like categorization and learning.

Purpose of the Study:

  • To demonstrate that exemplar models can implement approximate Bayesian inference.
  • To explore exemplar models as a potential mechanism for cognitive processes.

Main Methods:

  • Utilized exemplar models to perform importance sampling, a Monte Carlo approximation technique.
  • Simulated Bayesian inference across diverse cognitive tasks including speech perception and memory reconstruction.

Main Results:

  • Exemplar models successfully performed approximate Bayesian inference.
  • Simulations showed exemplar models could account for human performance using few exemplars.
  • Effectiveness was demonstrated across simple and complex prior distributions.

Conclusions:

  • Exemplar models offer a viable mechanism for implementing approximate Bayesian inference in cognition.
  • This provides a potential process-level explanation for how humans perform Bayesian computations.