Algorithmic Complexity for Short Binary Strings Applied to Psychology: A Primer

TL;DR

This paper advocates for using algorithmic complexity as a universal measure of randomness in short binary sequences, demonstrating its application to psychological data and re-analyzing classical experiments.

Contribution

It introduces a novel approach based on algorithmic probability to assess randomness, unifying multiple measures into a single, comprehensive metric.

Findings

Re-analysis of Radio Zenith data supports the measure's effectiveness.

Algorithmic complexity provides a more holistic assessment of randomness.

The method simplifies the evaluation process in psychological studies.

Abstract

Since human randomness production has been studied and widely used to assess executive functions (especially inhibition), many measures have been suggested to assess the degree to which a sequence is random-like. However, each of them focuses on one feature of randomness, leading authors to have to use multiple measures. Here we describe and advocate for the use of the accepted universal measure for randomness based on algorithmic complexity, by means of a novel previously presented technique using the the definition of algorithmic probability. A re-analysis of the classical Radio Zenith data in the light of the proposed measure and methodology is provided as a study case of an application.