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What's next? Judging sequences of binary events.

An T Oskarsson1, Leaf Van Boven, Gary H McClelland

  • 1Department of Psychology, University of Colorado at Boulder, USA. an.oskarsson@colorado.edu

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|March 4, 2009
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Summary
This summary is machine-generated.

People often use naive mental models to judge random sequences, leading to beliefs like the gambler's fallacy and hot hand. These models are based on perceived generator characteristics, influencing predictions about events.

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Area of Science:

  • Cognitive Psychology
  • Behavioral Economics
  • Decision Science

Background:

  • Research on sequence judgments reveals common biases such as the gambler's fallacy and hot hand beliefs.
  • These cognitive biases manifest across diverse domains including random devices, finance, and sports performance.

Purpose of the Study:

  • To review existing research on human judgments of random and nonrandom sequences.
  • To propose an explanation-based mental models framework for understanding these judgments.
  • To introduce a Markov process framework for analyzing sequence beliefs and actual event sequences.

Main Methods:

  • Literature review of studies on gambler's fallacy and hot hand beliefs.
  • Development of a mental models framework based on perceived sequence generator characteristics (randomness, intentionality, control, goal complexity).
  • Proposal of a Markov process framework for theoretical notation and sequence analysis.

Main Results:

  • People frequently employ "naive complex models" to interpret and predict event sequences in everyday settings.
  • These mental models are shaped by beliefs about the underlying mechanisms generating the events.
  • The proposed mental models and Markov process frameworks offer tools for describing and analyzing sequence judgments.

Conclusions:

  • Understanding naive mental models is crucial for explaining human inferences about sequences.
  • The mental models framework provides a structured approach to analyzing beliefs about randomness and control.
  • Markov processes offer a formal method for evaluating both subjective beliefs and objective event sequences.