Protocol for measuring context-dependent cost-benefit decision-making in humans using a web application

We present a protocol for measuring naturalistic and normalized decision-making in humans across four contexts (approach-avoid, moral, social, and probabilistic) using a web application. We describe steps for session setup, eye tracker calibration, and heart rate monitoring. In each session, a participant encounters a story, rates rewards and costs relevant within that context, and then evaluates various cost-reward pairings in context. For complete details on the use and execution of this protocol, please refer to Rakocevic et al.<sup>1</sup>.

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Publication Details

Journal
STAR Protocols
Published
2025-09-13
DOI
https://doi.org/10.1016/j.xpro.2025.104077
Primary Topic
Human-Automation Interaction and Safety
Type
article
Field-Weighted Citation Impact
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article

Protocol for measuring context-dependent cost-benefit decision-making in humans using a web application

Lara I. Rakocevic, Ki A. Goosens, Alexander Friedman, Raquel Ibáñez Alcalá
STAR Protocols
Human-Automation Interaction and Safety
article

Protocol for measuring context-dependent cost-benefit decision-making in humans using a web application

Lara I. Rakocevic, Ki A. Goosens, Alexander Friedman, Raquel Ibáñez Alcalá
article en

Abstract

We present a protocol for measuring naturalistic and normalized decision-making in humans across four contexts (approach-avoid, moral, social, and probabilistic) using a web application. We describe steps for session setup, eye tracker calibration, and heart rate monitoring. In each session, a participant encounters a story, rates rewards and costs relevant within that context, and then evaluates various cost-reward pairings in context. For complete details on the use and execution of this protocol, please refer to Rakocevic et al.<sup>1</sup>.

STAR ProtocolsVol. 6(4)
Allen Institute for Brain Science (US), The University of Texas at El Paso (US), Icahn School of Medicine at Mount Sinai (US)
Openalex Percentile: Top 72%
Human-Automation Interaction and Safety
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