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Area of Science:

  • Cognitive psychology
  • Behavioral neuroscience
  • Computational statistics

Background:

  • Human random number generation (RNG) is often assumed to be unbiased.
  • However, subtle, person-specific biases may exist in pseudo-random digit sequences.
  • Understanding these biases is crucial for cognitive modeling and behavioral analysis.

Purpose of the Study:

  • To investigate whether individuals exhibit unique, consistent strategies in random number generation.
  • To develop a quantitative method for assessing the similarity of number sequences.
  • To determine if these individual patterns can be used to identify the generator of a sequence.

Main Methods:

  • Collected pseudo-random number sequences from 115 healthy participants.
  • Developed a novel quantification method based on Damerau-Levenshtein edit distance to measure sequence similarity.
  • Utilized machine learning (AUC analysis) to classify sequence pairs as 'same-author' or 'different-author'.

Main Results:

  • Sequences generated by the same individual could be distinguished from those generated by different individuals with 96.5% Area Under the Curve (AUC) accuracy using only 300 digits.
  • The observed phenomenon is attributed to consistent individual preferences and pattern inhibition.
  • These unique behavioral patterns remain stable over a one-week period.

Conclusions:

  • Random number generation is not purely random but reflects stable, person-specific cognitive strategies.
  • These individual-specific patterns act as a reliable 'cognitive fingerprint'.
  • The findings have implications for understanding cognitive processes, behavioral analysis, and potentially detecting deception or forged data.