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Analysing humanly generated random number sequences: a pattern-based approach.

Marc-André Schulz1, Barbara Schmalbach, Peter Brugger

  • 1Department of Psychiatry and Psychotherapy, RWTH Aachen University, Aachen, Germany.

Plos One
|July 31, 2012
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Summary
This summary is machine-generated.

Human random number sequences exhibit predictable patterns. A novel pattern analysis, using Damerau-Levenshtein distance, accurately predicts sequences and identifies individuals based on their unique number generation style.

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Area of Science:

  • Cognitive psychology
  • Computational neuroscience
  • Data analysis

Background:

  • Human-generated random number sequences are not truly random and contain biases.
  • These biases reflect the brain's internal random number generation processes.
  • Analyzing these patterns can reveal individual characteristics.

Purpose of the Study:

  • To introduce and validate a pattern-based analysis for human random number sequences.
  • To develop a model for predicting future numbers and identifying individuals based on sequence patterns.
  • To quantify the predictability and individuality of human random number generation.

Main Methods:

  • Twenty healthy subjects generated two sequences of 300 numbers each.
  • Pattern analysis employed the Damerau-Levenshtein distance to measure sequence similarity.
  • A predictive model was built using a history of seven preceding numbers.

Main Results:

  • The predictive model achieved a mean correct prediction rate of 27% (up to 46% individually), significantly above chance (11%).
  • An algorithm could identify a "foreign" sequence from a different subject with up to 88% accuracy.
  • Statistical information from the same subject yielded higher prediction success than from different subjects.

Conclusions:

  • Pattern-based analysis using Damerau-Levenshtein distance effectively predicts human random number sequences.
  • This method can identify unique, person-specific information within these sequences.
  • The findings offer insights into the cognitive mechanisms underlying random number generation.