LAUSR

Abstract Visual fixation (i.e., holding gaze on a specific visual object or location of interest) has been shown to be influenced by activity in the rostral pole of the intermediate layers of the superior colliculus (SCi)—a sensory–motor integration nucleus in the midbrain involved in visual fixation and saccadic eye movement generation. Neurons in the rostral SCi discharge tonically during visual fixation and pause during saccades to locations beyond their foveal visual-sensory or saccadic-motor response fields. Injection of muscimol to deactivate rostral SCi neurons also leads to an increase in fixation instability. However, the precise role of rostral SCi activity for controlling visual fixation has not been established and is actively debated. Here, we address whether this activity reflects signals related to task demands (i.e., maintaining visual fixation) or foveal visual stimulus properties. Two non-human primates performed an oculomotor task that required fixation of a central fixation point (FP) of varying luminance at the start of each trial. During this fixation period, we measured fixational saccades (≤ 2° of the FP, including microsaccades) and fixation-error saccades (> 2° from the FP) in combination with activity from the rostral SCi. Fixation of the lowest FP luminance increased the latency (onset time relative to initial FP foveation) for both fixational and fixation-error saccades. Fifty percent of the rostral SCi neurons exhibited activity that opposed the change in FP luminance and correlated with delayed fixational saccades and increased fixation-error saccades. Twenty-two percent of rostral SCi neurons exhibited activity that followed the change in FP luminance and correlated with earlier fixational saccades and decreased fixation-error saccades. This suggests the rostral SCi contains both sensory-driven and task-related motor signals related to foveal sensory stimuli and visual fixation. This evidence supports a role for the rostral SCi in gaze stabilization and can help inform artificial computational models of vision.