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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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Using artificial neural networks to reveal the human confidence computation.

Medha Shekhar1,2, Herrick Fung1, Krish Saxena1

  • 1School of Psychology, Georgia Institute of Technology, Atlanta, Georgia, United States of America.

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Human confidence judgments, especially in complex tasks, are better explained by a model focusing on the difference between the top two choices (Top2Diff) rather than broader evidence. This finding advances understanding of cognitive decision-making processes.

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Human confidence judgments are crucial for decision accuracy but poorly understood for naturalistic, multi-choice tasks.
  • Existing cognitive models are limited, primarily addressing simple, two-choice scenarios.

Purpose of the Study:

  • To investigate mechanisms of confidence in multi-alternative, naturalistic decision-making.
  • To compare various confidence strategies using a novel convolutional neural network (CNN) model.

Main Methods:

  • Utilized a CNN model (RTNet) to analyze confidence strategies in an eight-alternative digit discrimination task (N=60).
  • Compared strategies based on evidence distribution (full vs. subset) and evidence type (posterior vs. raw).

Main Results:

  • The Top2Diff model, using raw evidence difference between the top two choices, showed superior quantitative and qualitative fits.
  • This model also provided the best predictions of human confidence ratings.

Conclusions:

  • Human confidence appears to rely on a specific subset of evidence, challenging existing theories like the Bayesian confidence hypothesis.
  • CNNs offer a powerful framework for modeling human confidence in complex decision-making environments.