The Conductome: A Bayesian Classifier Approach to Predicting and Understanding Behaviour

Abstract

Predicting and understanding behaviour is a primary objective of many disciplines, especially human behaviour, as it is the cause of many of the worlds most pressing problems. Although it is a fundamental concept in multiple disciplines, there is no agreed operational definition of what it is. Neither is there a generally agreed theoretical framework for predicting it. Here we propose a data-driven approach, using the "Conductome" -- the complete set of factors that both predict and explain a behaviour -- to operationalise a discipline-neutral definition of behaviour that is based on an ensemble of stimulus/response measurements of a system, showing that it must be determined through a process of statistical inference. As the prediction of behaviour can be characterised as a classification problem, we argue that Bayesian classifiers offer a promising framework in which explainable prediction models that can approximate the Conductome can be developed. We show the efficacy of the framework using a dataset of 1075 persons, with over 3000 features, constructing a model for predicting sedentariness, a behaviour that is a known risk factor for obesity and metabolic disease. We analyse the effect size, coverage, statistical significance and potential causality of a subset of 396 features associated with 58 variables.of different types.