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Related Concept Videos

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
Compartment Models: Two-Compartment Model01:20

Compartment Models: Two-Compartment Model

The two-compartment model divides the body into central and peripheral compartments to account for varying blood perfusion rates among organs and tissues, affecting drug distribution. The central compartment includes blood and highly perfused tissues with rapid drug distribution, while the peripheral compartment contains tissues with slower drug distribution. After a single IV bolus dose, the drug concentration is high in plasma and low in tissues. The drug distribution between compartments...
Compartment Models: Single-Compartment Model01:14

Compartment Models: Single-Compartment Model

The single-compartment model serves as a simplified representation of the human body. This model assumes that the body functions as a single, well-mixed open compartment. When a drug is administered intravenously, it enters the body and quickly distributes uniformly. The drug then undergoes biotransformation and elimination, ultimately leaving the body. The volume of this compartment is referred to as the apparent volume of distribution into which the drug can uniformly distribute. In this...
Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model01:14

Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model

The link model is a fundamental pharmacokinetic-pharmacodynamic (PK–PD) approach to account for delayed drug responses when the observed effect does not immediately correlate with the drug's plasma concentration peak. This delay is mathematically addressed by introducing an effect compartment concentration, Ce, which is kinetically linked to the plasma concentration, Cp, via a first-order rate constant, ke0. The linkage allows for a more accurate prediction of drug effects over time. A higher...
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
One-Compartment Open Model for Extravascular Administration: First-Order Absorption Model01:15

One-Compartment Open Model for Extravascular Administration: First-Order Absorption Model

The first-order absorption model for extravascular administration describes the rate at which a drug is absorbed and eliminated, following the principles of first-order kinetics. This model is vital as it provides a mathematical representation of drug behavior within the body. It also allows for the prediction and interpretation of drug absorption and elimination based on the rate of change in drug concentration over time. This model can be visualized as a plasma concentration-time profile...

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Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0
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Diffusion versus linear ballistic accumulation: different models for response time with different conclusions about

Andrew Heathcote1, Brett Hayes

  • 1School of Psychology, The University of Newcastle, Australia. andrew.heathcote@newcastle.edu.au

Canadian Journal of Experimental Psychology = Revue Canadienne De Psychologie Experimentale
|June 13, 2012
PubMed
Summary

Evidence accumulation models like diffusion and linear ballistic accumulator show similar results for evidence rate, but differ in response caution and nondecision time. The linear ballistic accumulator offers a simpler explanation for practice effects.

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Area of Science:

  • Cognitive Psychology
  • Computational Neuroscience
  • Decision Science

Background:

  • Two main classes of evidence-accumulation models, diffusion and racing accumulator pairs, dominate rapid binary choice research.
  • The Ratcliff diffusion (RD) and linear ballistic accumulator (LBA) models are the least similar within their respective classes.
  • Previous research showed model mimicry when only evidence accumulation rates differed.

Purpose of the Study:

  • To investigate potential divergent inferences between RD and LBA models when response caution and nondecision time parameters vary.
  • To examine the fit of RD and LBA models to a dataset with a practice manipulation not previously surveyed.
  • To compare the explanatory power of LBA and RD models for practice effects.

Main Methods:

  • Simulations comparing RD and LBA model performance under varying parameter conditions.
  • Analysis of a dataset from Dutilh et al. (2009) using a practice manipulation.
  • Model fitting and comparison of RD and LBA to the experimental data.

Main Results:

  • Simulations revealed trade-offs between response caution and nondecision time parameters, potentially leading to divergent inferences.
  • RD model fits to the Dutilh et al. data indicated practice affected all parameters.
  • The LBA model provided a simpler and alternative account of practice effects in this dataset.

Conclusions:

  • While RD and LBA models can yield equivalent inferences under certain conditions, differences in response caution and nondecision time can lead to divergent conclusions.
  • The LBA model offers a more parsimonious explanation for practice effects observed in the examined dataset.
  • Findings highlight the importance of considering model assumptions and parameter trade-offs in evidence accumulation modeling.