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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.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
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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 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.
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...
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Reason and Intuition01:37

Reason and Intuition

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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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Updated: Jan 6, 2026

A Modified Lean and Release Technique to Emphasize Response Inhibition and Action Selection in Reactive Balance
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Balancing model-based and memory-free action selection under competitive pressure.

Atsushi Kikumoto1, Ulrich Mayr1

  • 1Department of Psychology, University of Oregon, Eugene, United States.

Elife
|October 3, 2019
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Summary
This summary is machine-generated.

In competitive games, people switch between predicting opponents and random actions. Winning reinforces prediction, while losing triggers random choices to maintain unpredictability.

Keywords:
EEGanterior cingulatecompetitive behaviorfeedback-related processeshumanmodel-based choiceneurosciencestochastic choice

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Area of Science:

  • Behavioral economics
  • Neuroscience
  • Game theory

Background:

  • Winning in competitive scenarios relies on unpredictable actions.
  • Strategies include model-based counter-prediction and stochastic selection.
  • Understanding the cognitive mechanisms behind these strategies is crucial.

Purpose of the Study:

  • To investigate how individuals switch between model-based and stochastic strategies in competitive games.
  • To examine the neural correlates of strategy selection using electroencephalography (EEG).
  • To understand how feedback (wins/losses) influences strategy and opponent modeling.

Main Methods:

  • Five experiments using a matching-pennies game variant with simulated and human opponents.
  • Behavioral analysis of action selection and strategy switching.
  • Mid-frontal EEG recording to measure feedback-related neural activity.

Main Results:

  • Participants toggled between model-based and stochastic strategies based on recent outcomes.
  • Wins prompted model-based strategy use, leveraging opponent's strategy and history.
  • Losses led to stochastic strategy, suppressing internal models and enhancing unpredictability.
  • EEG data showed feedback-related activity after wins, reflecting opponent strategy, but was absent after losses.

Conclusions:

  • A mixed-strategy approach balances exploiting opponent predictability and maintaining one's own unpredictability.
  • Neural activity patterns correlate with strategy use and feedback processing.
  • Negative feedback suppresses internal opponent models, promoting a shift to random action selection.