Considerations for setting up an S-tier behavioral and brain science laboratory

For decades, behavioral and brain research has advanced by isolating single variables or brain regions to study behavior and performance. While informative, it has become increasingly clear that such reductionist methods struggle to capture the complex, dynamic, and context-dependent nature of human behavior. Although advances in data analysis, artificial intelligence, and open data standards now enable unprecedented insights into complex datasets, comparatively little attention has been paid to the place where data originate: the laboratory. Here, devices are often siloed, proprietary, or limited to aggregated outputs, thereby constraining the questions a laboratory can address. For laboratory infrastructure to keep pace with advances toward complex systems research, it is helpful to first clarify what a measure represents in the behavioral and brain sciences. In this paper, we think of measurements in three epistemic layers: the surface layer (raw numeric outputs), the proxy layer (physiological or behavioral subsystems), and the target layer (emergent constructs such as arousal, effort, or performance). 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. Importantly, this framework offers practical implications for how laboratory devices can be selected and evaluated: We derive a set of practical desiderata to guide infrastructure decisions, emphasizing the need for high-resolution, high-fidelity, interoperable, transparent, real-time, and flexible measurement systems. We believe that adopting (and improving) such a ranking scheme will help communication and decision-making for setting up the best possible laboratory infrastructure to study of complex, dynamic behavior.