AbstractMotor behaviour scientists often use tasks where participants' performance can deviate in the x and y directions from the goal-target. Researchers often disregard this and measure performance on these 2-dimensional tasks using 1-dimensional systems. This is commonly done using a circular bullseye surrounded by concentric rings. Performance is scored using points where the bullseye is worth 10 points and each subsequent ring decreases in value by 1 point. This scoring system ignores that individual trials can vary in 360 degrees around the bullseye and in the distance from the centre of the bullseye (Reeve et al. 1994). As such, there is no isomorphic relationship between a participant's score on a trial and the actual performance response (Fischman 2015). The reported error scores in such experiments are inaccurate, oversimplified, and arbitrary, which may lead to incorrect conclusions about motor learning. The continued use of 1-dimensional systems for 2-dimensional tasks is likely due to the ease of use as compared to the more time consuming measurement of the x and y deviations. Here we created a free and open source 2-dimensional error score program using Python that automatically measures the x and y deviations of an object from the goal-target. The program leverages an inexpensive Raspberry Pi Zero and Pi HQ Camera with a wide angle lens. Data collection and analysis is significantly faster with this system compared to manual measurements. Our program provides an efficient and cost-effective (< $200) solution to the use of inappropriate performance measures with 2-dimensional tasks.