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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.
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Decision Making: P-value Method01:09

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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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Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now? 
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The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
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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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Learning optimal decisions with confidence.

Jan Drugowitsch1, André G Mendonça2, Zachary F Mainen2

  • 1Department of Neurobiology, Harvard Medical School, Boston, MA 02115; jan_drugowitsch@hms.harvard.edu.

Proceedings of the National Academy of Sciences of the United States of America
|November 17, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Bayesian learning rule for diffusion decision models (DDMs), enabling optimal decision-making from sensory inputs. The rule adapts learning rates based on decision confidence and environmental volatility.

Keywords:
confidencedecision makingdiffusion modelsoptimality

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Area of Science:

  • Computational Neuroscience
  • Decision Science
  • Machine Learning

Background:

  • Diffusion decision models (DDMs) are widely used for decision-making under uncertainty.
  • Current DDMs lack a normative theory for learning network weights from feedback.
  • Brain sensory input differs from DDM's simplified neuron-antineuron pair structure.

Purpose of the Study:

  • To derive a normative theory for learning in DDMs.
  • To develop a Bayesian rule for learning optimal linear combinations of DDM inputs.
  • To investigate how learning rules adapt to feedback and environmental volatility.

Main Methods:

  • Derivation of a Bayesian learning rule for DDM input weights.
  • Incorporation of weight uncertainty via a covariance matrix.
  • Analysis of learning rate modulation by decision confidence and choice difficulty.

Main Results:

  • The derived rule learns near-optimal linear combinations of inputs based on trial-by-trial feedback.
  • Learning rate is proportional to confidence for incorrect decisions and inversely proportional for correct ones.
  • A bias towards repeating choices in volatile environments is predicted, modulated by difficulty.

Conclusions:

  • The new Bayesian learning rule provides a normative account for learning in DDMs.
  • The rule offers insights into how biases emerge and influence optimal decision-making.
  • This work bridges the gap between simplified DDM architectures and biological neural processing.