Impaired smooth pursuit has long been considered an endophenotype of schizophrenia; however, pursuit metrics do not currently offer diagnostic utility or the ability to predict treatment efficacy (
Lieberman, Small, & Girgis, 2019). Further, smooth pursuit gain is reduced in a range of conditions including autism, attention-deficit/hyperactivity disorder, and Parkinson's disease, suggesting that oculomotor impairments may simply reflect generalized neurological impairments, challenging their use as a biomarker (
Bansal, Ford, & Spering, 2018;
Fletcher & Sharpe, 1988;
Hong et al., 2008;
Levy, Sereno, Gooding, & O'Driscoll, 2010;
Mani, Asper, & Khuu, 2018;
Takarae, Minshew, Luna, Krisky, & Sweeney, 2004). Many previous studies of smooth pursuit have relied on a small number of stimulus trajectories and a limited number of performance metrics, so a simple way to expand the potential utility of oculomotor testing is to use more tests and calculate more metrics. In a 15-minute test session involving pursuit of constant-velocity targets, the distributions of 10 metrics were characterized in a population of 41 participants, and test–retest reliability was demonstrated in six participants (
Liston & Stone, 2014). Whereas
Liston and Stone (2014) tested multiple speeds and directions, they did not examine the reliability of these measures in a large cohort. The idea of an “oculomotor signature” was more thoroughly developed in a study involving 1058 individuals and assessment of test–retest reliability in 105 individuals (
Bargary et al., 2017). This study extracted 21 metrics from a 25-minute testing session involving saccades, anti-saccades, and smooth pursuit. Promisingly for biomarker development, both studies demonstrated that, even with healthy populations, there are large interindividual variations in metrics of timing, accuracy, precision, and smoothness of tracking eye movements, with most metrics showing twofold or greater differences across participants. Although most metrics were strongly correlated within individuals, both studies (
Bargary et al., 2017;
Liston & Stone, 2014) examined correlations between data measured in the same session and sometimes the same trials, making the correlations susceptible to artificial inflation due to session-level factors such as vigilance or motivation. A more recent study with 50 participants used eight distinct tasks requiring saccades, pursuit, or perceptual reports of stationary and moving targets (
Goettker & Gegenfurtner, 2024). The authors reported correlations between saccadic and pursuit performance, but only if the relevant sensory information was matched. In this case, sensory information refers to whether behavior is guided by information about stimulus position or velocity. Although
Bargary et al. (2017) and
Goettker and Gegenfurtner (2024) demonstrated test–retest reliability in a large cohort, they only used horizontal pursuit, and the reliability of directional biases remains unclear.