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Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Decision Making: P-value Method01:09

Decision Making: P-value Method

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 have a...
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
What are Estimates?01:06

What are Estimates?

It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such as the mean,...
Decision Making01:20

Decision Making

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...
Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...

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Related Experiment Videos

Continuous evolution of statistical estimators for optimal decision-making.

Ian Saunders1, Sethu Vijayakumar

  • 1Institute of Perception, Action and Behaviour, School of Informatics, University of Edinburgh, Edinburgh, United Kingdom. i.saunders@sms.ed.ac.uk

Plos One
|July 5, 2012
PubMed
Summary
This summary is machine-generated.

Humans continuously estimate sensory information and its uncertainty for optimal decision-making. This study reveals the underlying mechanisms for real-time mean and uncertainty estimation, crucial for perception and choice.

Related Experiment Videos

Area of Science:

  • Cognitive psychology
  • Neuroscience
  • Decision science

Background:

  • Human decision-making often involves integrating uncertain sensory information.
  • Statistical-optimality in perception suggests access to sensory and decision uncertainty.
  • Mechanisms for estimating uncertainty are not well understood.

Purpose of the Study:

  • To investigate the mechanisms of continuous uncertainty estimation in humans.
  • To explore how humans track evolving sensory information and its reliability over time.

Main Methods:

  • Developed a novel visual tracking experiment.
  • Subjects continuously reported perceived mean and uncertainty of noisy visual cues.
  • Measured accumulation of sensory information and estimation of objective uncertainty.

Main Results:

  • Human subjects accumulate sensory information to form continuous estimates of the mean.
  • Estimates are constrained by natural kinematic limitations like sensorimotor latency.
  • Subjects possess a measure of continuous objective uncertainty, rapidly acquired but with limitations.

Conclusions:

  • Provides the first direct evidence for continuous mean and uncertainty estimation mechanisms in humans.
  • These mechanisms are vital for statistically optimal decision-making and perception.
  • Findings shed light on how the brain handles uncertainty in dynamic environments.