Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for k_a Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...

Clearance Models: Noncompartmental Models

Clearance Models: Noncompartmental Models

Clearance is a pharmacokinetic parameter traditionally defined by compartment models, signifying the rate at which a drug is expelled from the body. However, a noncompartmental model offers an alternative method for assessing clearance, primarily employing empirical data obtained after administering a single drug dose.
The noncompartmental approach capitalizes on extensive sampling data, correlating the volume of distribution to systemic exposure and the administered dosage. This method enables...

Theory of Attribution I: Correspondent Inference Theory

Theory of Attribution I: Correspondent Inference Theory

Correspondent inference theory, proposed by Jones and Davis in 1965, seeks to explain how individuals infer stable personality traits from observed behaviors. It suggests that people attribute actions to underlying dispositions rather than external circumstances, particularly when the behavior appears intentional and socially significant.Voluntary Behavior and Dispositional AttributionAccording to this theory, individuals are more likely to attribute behavior to personal traits when it appears...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

pyhgf: A neural network library for predictive coding.

PLoS computational biology·2026

Same author

Scale-invariant evolution: Comment on "homo informatio" by Michael Walker.

Physics of life reviews·2026

Same author

It Is What It Isn't: Introducing a Constraint-Based Approach to Structure Learning.

Entropy (Basel, Switzerland)·2026

Same author

The body does not keep the score: trauma, predictive coding, and the restoration of metastability.

Frontiers in systems neuroscience·2026

Same author

The dysconnection hypothesis of schizophrenia: a 30-year update.

World psychiatry : official journal of the World Psychiatric Association (WPA)·2026

Same author

The methodological foundations of lesion network mapping remain sound.

bioRxiv : the preprint server for biology·2026

Search research articles

Related Experiment Video

Updated: Apr 19, 2026

A Tactile Automated Passive-Finger Stimulator TAPS

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

Optimal inference with suboptimal models: addiction and active Bayesian inference.

Philipp Schwartenbeck¹, Thomas H B FitzGerald², Christoph Mathys²

¹The Wellcome Trust Centre for Neuroimaging, UCL, 12 Queen Square, London WC1N 3BG, UK; Institute for Cognitive Neuroscience, University of Salzburg, Salzburg, Austria; Neuroscience Institute, Christian-Doppler-Klinik, Paracelsus Medical University Salzburg, Salzburg, Austria.

Medical Hypotheses

|January 7, 2015

Summary

This summary is machine-generated.

Behavioral choices are optimally inferred from an agent's beliefs, not environmental truths. Suboptimal behavior highlights the need to refine understanding of the generative models underlying Bayesian inference.

More Related Videos

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Related Experiment Videos

Last Updated: Apr 19, 2026

A Tactile Automated Passive-Finger Stimulator TAPS

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Area of Science:

Cognitive Science
Neuroscience
Decision Theory

Background:

Optimal behavior is often defined by environmental accuracy, not agent beliefs.
Casting behavior as active (Bayesian) inference emphasizes the agent's internal generative model.
Suboptimal behavior may stem from prior beliefs rather than flawed inference processes.

Purpose of the Study:

To distinguish between normative and belief-based accounts of optimal behavior.
To reframe suboptimal and pathological behaviors as consequences of generative models.
To explore implications for bounded rationality and addiction using a novel task.

Main Methods:

Conceptual analysis of Bayesian inference in decision-making.
Modeling of choice behavior using a generative model framework.
Simulation of addictive behavior in a 'limited offer' task.

Main Results:

Optimal inference is relative to an agent's beliefs and generative model.
Pathological behavior arises from specific prior beliefs within an optimal inference framework.
Simulations provide testable hypotheses for addictive choice behavior.

Conclusions:

Behavioral optimality should be assessed against an agent's generative model, not objective reality.
Understanding prior beliefs is crucial for explaining suboptimal and pathological decision-making.
The Bayesian inference framework offers a robust account of rational and irrational behaviors.