Evidence map›Paper›PMID 42639132›Full record

ArticleFrontiers in psychology2026

Measuring human behavior through controlled perturbations: a framework for reconstructing behavioral systems.

Pietro Cipresso

Abstract read
In one paragraph

Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Pietro CipressoDepartment of Psychology, University of Turin, Turin, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Human behavior is typically measured by relating observed responses to latent traits inferred from statistical models. Although this approach has produced powerful tools for psychological assessment, it largely treats behavior as a static property of individuals rather than as the observable output of an evolving dynamical system. Yet most behavioral phenomena emerge from continuous interactions between internal states and environmental conditions, generating trajectories of actions, decisions, and physiological responses over time. Here we propose a framework in which human behavior is measured by reconstructing the dynamical systems that generate it through controlled perturbations. In this perspective, experimental environments act as measurement instruments that probe behavioral dynamics through structured perturbations. Behavioral observations are interpreted as the outputs of stochastic controlled dynamical systems, and behavioral measurement becomes a system identification problem: generative models are reconstructed so as to reproduce the trajectory distributions produced by human behavior under equivalent perturbation conditions. We formalize this framework using stochastic dynamical systems, introduce criteria for reconstruction and functional equivalence under unseen perturbations, and analyze the roles of perturbation design, identifiability, and experimental domain boundaries. The approach is illustrated across multiple behavioral domains, including affective dynamics, cognitive flexibility, stress reactivity, and social interaction. By integrating controlled perturbations, dynamical modeling, and trajectory-level validation, this framework reframes behavioral measurement as the reconstruction of generative behavioral dynamics. More broadly, it suggests a path toward a metrology of human behavior in which programmable experimental environments, such as immersive virtual reality platforms, serve as instruments capable of systematically probing and characterizing behavioral systems.

Indexed as

behavioral measurementbehavioral trajectoriescomputational psychometricsdynamical systemsexperimental designperturbation-based experimentsquantitative psychologysystem identification

Identifiers

PMID42639132
PMCPMC13500639

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.