Social Dominance Tube Test

The social dominance tube test is a widely used assay for determining hierarchical rank among cage-mate rodents by exploiting competitive behavior in a narrow transparent tube that permits only single-file passage. Two mice are placed at opposite ends of a tube (typically 30 cm long, 3.5 cm inner diameter for mice) and released simultaneously. Because the tube is too narrow for the animals to pass each other, one mouse must retreat by walking backward, and the mouse that forces the other to withdraw is designated the winner of that bout. The test is completed in a round-robin fashion across all unique dyadic pairings within a cage, generating a complete dominance matrix from which individual rank can be computed.

The primary outcome is the win percentage for each animal across all pairings, which reliably maps onto stable social hierarchies validated by other measures such as barbering patterns, urine marking, and resource competition assays. Additional metrics include retreat latency (time from meeting in the tube to the loser beginning backward movement), the number of push-back events where the eventual loser momentarily advances before ultimately retreating, and total bout duration. The tube test is sensitive to manipulations of medial prefrontal cortex function, as optogenetic activation of mPFC pyramidal neurons in subordinate mice can acutely reverse dominance outcomes, demonstrating the neural specificity of this behavioral readout.

ConductMaze provides a dedicated tube test module that uses lateral-view camera tracking to monitor both animals from introduction through resolution of each bout. The system automatically detects the moment the two mice meet inside the tube, identifies forward versus backward locomotion for each individual using directional body-axis analysis, and logs the retreat event as the decisive outcome. Automated round-robin scheduling generates the optimal pairing order for each cage, computes Elo ratings or David scores from the resulting win-loss matrix, and produces hierarchy stability metrics across repeated testing sessions.