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Related Concept Videos

Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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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.
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...
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Decision Making01:20

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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 P-value method includes calculating the P- value using the sample data and interpreting it.
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Reason and Intuition01:37

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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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Variability: Analysis01:11

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Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
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Strategies for Assessing and Addressing Confounding01:25

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Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
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Disentangling sources of variability in decision-making.

Jade S Duffy1, Mark A Bellgrove2, Peter R Murphy3

  • 1Trinity College Institute of Neuroscience and School of Psychology, Trinity College Dublin, Dublin, Ireland.

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Understanding intra-individual variability (IIV) in decision-making is crucial. New computational models and analyses now link IIV components to neural processes, improving insights into choice behavior in health and disease.

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Area of Science:

  • Cognitive Neuroscience
  • Computational Psychiatry
  • Decision Science

Background:

  • Trial-to-trial variability in choice timing and accuracy is a pervasive feature of decision-making.
  • This intra-individual variability (IIV) is a key phenotype in clinical disorders, yet its sources are poorly understood.
  • Existing computational models have limitations in fully parsing the origins of IIV.

Purpose of the Study:

  • To review current limitations of algorithmic models in understanding decision-making variability.
  • To highlight recent advances enabling the linkage of IIV components to neural processes.
  • To demonstrate new avenues for analyzing the neural origins of IIV for a holistic understanding of decision-making.

Main Methods:

  • Discussion of limitations in current algorithmic models for decision-making variability.
  • Highlighting advances in behavioral paradigm design and cross-trial analyses of neural dynamics.
  • Focus on the development of neurally grounded computational models.

Main Results:

  • Recent advances allow for the systematic analysis of distinct components of intra-individual variability.
  • These methods enable the linking of specific IIV components to well-defined neural processes.
  • Progress is being made in understanding the neural underpinnings of choice behavior variability.

Conclusions:

  • New computational and analytical methods are crucial for dissecting the neural origins of decision-making variability.
  • These advancements facilitate a more refined and holistic understanding of decision-making in both healthy and clinical populations.
  • This approach opens new avenues for studying the neural basis of choice behavior and its disorders.