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The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
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An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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Confidence modulates exploration and exploitation in value-based learning.

Annika Boldt1,2, Charles Blundell3, Benedetto De Martino1

  • 1Institute of Cognitive Neuroscience, University College London, 17 Queen Square, London, UK.

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Humans use uncertainty in their beliefs to balance exploring new options and exploiting known ones. This belief confidence influences decision confidence and metacognitive insight.

Keywords:
confidenceexploration–exploitation dilemmametacognitionuncertaintyvalue-based choice

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Area of Science:

  • Cognitive Psychology
  • Neuroscience
  • Decision Science

Background:

  • Uncertainty is a fundamental aspect of cognitive processing.
  • Agents often report internal noise via confidence judgments.
  • Value-based decision-making involves balancing exploration and exploitation.

Purpose of the Study:

  • To investigate how agents track and report internal uncertainty.
  • To examine the role of uncertainty in value beliefs for arbitrating exploration-exploitation.
  • To link value uncertainty to metacognition in decision-making.

Main Methods:

  • A novel multi-armed bandit paradigm was employed.
  • Studied belief formation and the evolution of belief confidence over time.
  • Analyzed the relationship between uncertainty, exploration, and metacognitive insight.

Main Results:

  • Agents utilized uncertainty in value beliefs to guide exploration-exploitation decisions.
  • Lower confidence in value representations led to increased exploration.
  • Belief confidence influenced decision confidence.
  • Higher metacognitive insight correlated with better environmental uncertainty tracking.

Conclusions:

  • Uncertainty representations are crucial for cognitive control.
  • Belief confidence serves as a key signal for metacognitive processes.
  • Metacognition enhances the ability to navigate environmental uncertainty.