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Related Concept Videos

Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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...
Decision Making01:20

Decision Making

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...
Reason and Intuition01:37

Reason and Intuition

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 brain can only use...
Decision Making: P-value Method01:09

Decision Making: P-value Method

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 have a...

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Response time distributions in rapid chess: a large-scale decision making experiment.

Mariano Sigman1, Pablo Etchemendy, Diego Fernández Slezak

  • 1Physics Department, School of Sciences, University of Buenos Aires Buenos Aires, Argentina.

Frontiers in Neuroscience
|October 30, 2010
PubMed
Summary

Decision-making in rapid chess reveals non-stationary response times (RTs) and correlated moves, challenging traditional algorithms. This study analyzes millions of decisions to understand player behavior and predict game outcomes.

Keywords:
Markovbrainbrain-computergamesintelligencemachine learningparallel computingplanning

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Area of Science:

  • Cognitive Science
  • Computational Psychology
  • Game Theory

Background:

  • Rapid chess offers a unique natural environment for studying decision-making under time constraints.
  • Vast datasets from online chess platforms surpass traditional experimental capabilities.
  • Existing sequential decision-making models often assume stationary response times and state-independent functions.

Purpose of the Study:

  • To analyze response times (RTs) and position values in rapid chess games.
  • To investigate the statistical properties of decision-making in chess.
  • To explore practical applications in player strength assessment and outcome prediction.

Main Methods:

  • Generated a large-scale database of response times and chess position evaluations from rapid games.
  • Analyzed response time distributions across different game stages.
  • Quantified correlations between successive moves' response times.
  • Evaluated blunders and score fluctuations for player strength prediction.
  • Developed a model combining time controls and position evaluation for winning likelihood estimation.

Main Results:

  • Response time distributions are long-tailed and vary significantly with game progression.
  • Response times for consecutive moves exhibit strong intra- and inter-player correlations.
  • Player strength can be predicted by analyzing blunders and score fluctuations.
  • Winning likelihood is reliably estimated using remaining time and position evaluation.

Conclusions:

  • The findings challenge fundamental assumptions of sequential decision-making models, indicating non-stationarity and interdependencies.
  • The study provides practical insights for chess software, enhancing player evaluation and game outcome prediction.
  • This research highlights the potential of large-scale behavioral data from games for understanding human cognition.