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

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
