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Related Concept Videos

Decision Making01:20

Decision Making

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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
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Design Example: Deciding Thickness of Lubricating Fluid in a Shaft01:23

Design Example: Deciding Thickness of Lubricating Fluid in a Shaft

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Effective lubrication between a rotating shaft and its bearing housing is essential in rotating machinery to minimize friction, wear, and energy loss. With carefully controlled thickness and viscosity, the lubricant layer prevents metal-to-metal contact, ensuring smooth operation.
To calculate the required thickness of the lubricant layer, the tangential velocity at the shaft's surface must first be determined. This velocity is calculated by converting the rotational speed to angular velocity...
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Decision Making: P-value Method01:09

Decision Making: P-value Method

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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
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Decision Making: Traditional Method01:14

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The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
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Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

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Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
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Sampling Plans01:23

Sampling Plans

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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...
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Related Experiment Video

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Proactive Information Sampling in Value-Based Decision-Making: Deciding When and Where to Saccade.

Mingyu Song1,2, Xingyu Wang1,3, Hang Zhang1,4,5

  • 1School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China.

Frontiers in Human Neuroscience
|February 27, 2019
PubMed
Summary

This study introduces a Bayesian framework for how people gather information before making decisions. It explains how individuals strategically sample evidence from different sources to maximize learning and guide choices.

Keywords:
Bayesian inferencedecision-makingdrift-diffusion modeleye-trackinginformation sampling

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Area of Science:

  • Cognitive Neuroscience
  • Decision Science
  • Computational Neuroscience

Background:

  • Evidence accumulation models decision-making but often overlooks information acquisition.
  • The process of sequentially sampling information from multiple sources remains poorly understood.

Purpose of the Study:

  • To propose a theoretical framework for proactive evidence sampling in decision-making.
  • To model how individuals allocate attention to maximize information gain from noisy sources.

Main Methods:

  • Developed a Bayesian framework to update beliefs from different information sources.
  • Integrated resource allocation for sampling (saccades) to maximize information gain.
  • Tested the framework against human choice and eye-movement data in a value-based decision task.

Main Results:

  • The proposed framework successfully explains human choice behavior.
  • The model accurately predicts participants' saccade patterns during evidence gathering.
  • Demonstrates proactive allocation of sampling resources to optimize information acquisition.

Conclusions:

  • The framework provides a novel account of evidence preparation in decision-making.
  • Highlights the role of active information seeking in optimizing choices.
  • Offers testable predictions for future eye-tracking studies in decision science.