Validating VLAB: A Cross-Platform Virtual Laboratory for Temporally Precise Measurement of Avoidance Conditioning and Latent Timing

Online behavioral research often struggles with precise temporal measurement due to hardware connection latency and contextual noise. The present study evaluated the methodological validity of VLAB, a new cross-platform virtual laboratory that executes events natively on user devices, for conducting an engaging, temporally precise associative learning task. Forty-eight participants completed a continuous avoidance conditioning task designed to assess latent timing and the scalar property under a 2 (Device: Smartphone vs. Computer) × 2 (Location: Laboratory vs. Outside) experimental design. Results demonstrated that VLAB reliably captured orderly temporal gradients and explicit discrimination between reinforced (6-s and 9-s) and non-reinforced warning signals. Crucially, a post-training instructional reversal test confirmed the presence of latent timing, proving participants precisely encoded the duration of non-reinforced cues. Furthermore, temporal performance generally conformed to the scalar property of timing, particularly on smartphones. While unmonitored ecological settings (outside the laboratory) introduced expected behavioral variability, device type itself did not preclude the collection of high-resolution continuous temporal data. In fact, smartphones often outperformed standard laboratory computers, likely due to the mechanical ease of touchscreens over standard computer mice for continuous tracking tasks. These findings validate VLAB as a robust, scalable tool for extending laboratory-grade, temporally constrained cognitive research to naturalistic environments without sacrificing methodological rigor.