A model to analyze age-related differences in human-generated head-tail sequences

Abstract

The generation of random-like sequences is a common task for assessing high-level cognitive abilities, such as inhibition, sustained attention and working memory. In general, many studies have shown a detrimental effect of aging on pseudo-random productions. The performance of participants in random generation tasks has typically been assessed by measures of randomness such as, among others, entropy and algorithmic complexity that are calculated from the series of responses produced by the subject. We focus on analyzing the mental model of randomness that people implicitly use when producing random series. We propose a novel latent class model based on Markov chains that aims to classify individuals into homogeneous classes according to the way they generate head-tail series. Our results reveal that there are significant age-related differences in the way individuals produce random-like sequences. Specifically, the group of healthy adults implicitly uses a simpler mental mechanism, in terms of memory requirements, compared to the group of younger participants. Author summaryIt is well known that, in general, people deviate from randomness as they attempt to mentally generate head-tail sequences as randomly as possible. The extensive literature on this topic has shown that human-generated head-tail series tend to have more alternations than would be expected by chance. However, it seems unrealistic to suppose that all individuals generate sequences based on the same random mental model. We conducted an experiment in which 331 individuals were asked to mentally simulate a fair coin: 69 healthy older adults with an age [≥] 60 and 262 Biology students with an age between 18 and 20. We found that the way in which random sequences are generated varies between subjects. A similar approach could be used to analyze differences in random generation tasks between subjects with different disorders and healthy subjects.