Measuring Human Behavior Through Controlled Perturbations: A Framework for Behavioral System Identification

TL;DR

This paper introduces a framework for measuring human behavior by applying controlled perturbations and system identification techniques, aiming to uncover underlying behavioral mechanisms.

Contribution

It proposes a novel methodological approach that treats behavioral measurement as a dynamical system identification problem using structured inputs and multimodal data.

Findings

Framework enables more precise identification of behavioral mechanisms.

Integration of immersive tech and computational modeling facilitates closed-loop experiments.

Advances support moving from descriptive models to understanding generative processes.

Abstract

The measurement of human behavior remains a central challenge across the behavioral sciences. Traditional approaches typically rely on passive observation of responses collected under static or weakly controlled conditions, limiting the identifiability of the underlying generative processes. As a result, different behavioral mechanisms may produce indistinguishable observations, constraining both inference and theoretical development. In this paper, we propose a methodological framework for behavioral measurement based on controlled perturbations. From this perspective, behavior is conceptualized as the observable output of a dynamical system, and measurement is reframed as a problem of system identification. Experimental environments act as measurement instruments that apply structured inputs (perturbations) and record behavioral trajectories as outputs over time. We outline the core components of this framework, including the design of perturbations, the role of temporal resolution, and the integration of multimodal data streams. We further discuss how advances in immersive technologies, programmable environments, and computational modeling enable the implementation of closed-loop experimental systems, where perturbation, observation, and model updating are tightly coupled. The proposed approach provides a principled basis for moving from descriptive and predictive models toward the identification of generative behavioral mechanisms. By integrating psychometrics, experimental design, and dynamical modeling, this framework contributes to the development of a more rigorous and reproducible methodology for the measurement of human behavior.