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Related Concept Videos

Sampling Plans01:23

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
Systematic Sampling Method01:17

Systematic Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
Systematic sampling is one of the simplest methods...
Convenience Sampling Method00:55

Convenience Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population.
Convenience sampling is a non-random method of sample selection; this method selects individuals that are easily accessible and may result in biased data. For example, a marketing...
Stratified Sampling Method01:16

Stratified Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
Sampling Methods: Overview01:06

Sampling Methods: Overview

A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of sampling...
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...

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Related Experiment Video

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Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
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A Sequential Sampling Approach to the Integration of Habits and Goals.

Chao Zhang1, Arlette van Wissen2, Ron Dotsch3

  • 1Human-Technology Interaction Group, Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, The Netherlands.

Computational Brain & Behavior
|July 16, 2026
PubMed
Summary

Habit-goal conflicts are explained by a new model where habit and goal values dynamically integrate within a sequential sampling framework, eliminating the need for arbitration. This approach simplifies understanding habit-goal competition in decision-making.

Keywords:
Computational modelingDecision field theoryHabit formationHabit-goal conflictReinforcement learningSequential sampling models

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Area of Science:

  • Cognitive Neuroscience
  • Computational Psychology
  • Behavioral Economics

Background:

  • Habits frequently conflict with goal-directed behaviors, a phenomenon of significant interest across multiple scientific disciplines.
  • Existing computational models often attribute these conflicts to competition between learning systems managed by a central unit.

Purpose of the Study:

  • To propose a more parsimonious computational model for habit-goal conflicts.
  • To explain these conflicts through dynamic integration of habit and goal values within a sequential sampling framework, without requiring arbitration.

Main Methods:

  • Developed a computational model extending the multialternative decision field theory.
  • Incorporated assumptions where habits influence preference accumulation starting points.
  • Modeled goal importance and relevance to determine sampling probabilities of goal attributes.

Main Results:

  • Simulations successfully reproduced key empirical findings from classic devaluation, devaluation with concurrent schedule, and reversal learning paradigms.
  • The model demonstrated an ability to predict gradual changes in decision times.
  • Parameter recovery analysis using approximate Bayesian computation confirmed the model's empirical data fitting potential.

Conclusions:

  • The proposed dynamic integration model offers a simpler explanation for habit-goal conflicts compared to arbitration-based models.
  • This framework has implications for refining habit theories and informing applied health psychology interventions.
  • The model provides a testable framework for future empirical research on decision-making under habit-goal conflict.