Behavioral and neural data suggest that visual attention spreads along contour segments to bind
them into a unified object representation. Such attentional labeling segregates the target contour from
distractors in a process known as mental contour tracing. A recurrent competitive map is developed
to simulate the dynamics of mental contour tracing. In the model, local excitation opposes global
inhibition and enables enhanced activity to propagate on the path offered by the contour. The extent of
local excitatory interactions is modulated by the output of the multi-scale contour detection network,
which constrains the speed of activity spreading in a scale-dependent manner. Furthermore, an Ljunction
detection network enables tracing to switch direction at the L-junctions, but not at the Xor
T-junctions, thereby preventing spillover to a distractor contour. Computer simulations reveal that
the model exhibits a monotonic increase in tracing time as a function of the distance to be traced.
Also, the speed of tracing increases with decreasing proximity to the distractor contour and with the
reduced curvature of the contours. The proposed model demonstrated how an elaborated version of
the winner-takes-all network can implement a complex cognitive operation such as contour tracing.