We don't care how much you sweat: An epistemic framework for behavioral and brain science laboratory infrastructure

For decades, behavioral and brain research has advanced by isolating single variables or brain regions to study behavior and performance. However, it has become increasingly clear that reductionist methods struggle to capture the complex, dynamic, and context-dependent nature of human behavior. Developments in data analysis and artificial intelligence now enable unprecedented insights into complex datasets. Yet, while different strands of reform literature have advanced how we theorize, measure, and analyze, the infrastructural conditions under which data are generated, the laboratory, have received comparatively little conceptual attention. Here, devices are often siloed, proprietary, or limited to aggregated outputs, thereby constraining the questions that can be addressed. In this paper, we offer an epistemic framework for reasoning about laboratory infrastructure at the scale of the whole laboratory, understood not as a collection of individual instruments but as an integrated infrastructure in which multiple hardware systems co-exist, communicate, and jointly support the questions a research group can ask. Conceptually, we think of measurements in three epistemic layers: the surface layer (raw numeric outputs, e.g., from an electrodermal sensor), the proxy layer (physiological or behavioral subsystems, e.g., sweat gland activity), and the target layer (emergent phenomena or constructs such as working memory capacity, arousal or effort). These layers are epistemic in that they describe how meaning is inferred from signals and the type of losses and mismatches that can occur at or between them; e.g., when we study arousal or effort, we don't care about sweat gland activity per se, but rather as an imperfect intermediate proxy. We derive from these layers a practical rating scheme across seven infrastructure properties: signal digitization, signal fidelity, temporal alignment, real-time access, interoperability, transparency, and flexibility. Researchers can use these to evaluate and optimize their laboratory across modalities. Our framework complements existing modality-specific standards and community-developed integration tools by providing a shared decision logic at the scale of the lab as a whole.