Dot Array Stimulus Toolbox: An Open-Source Solution for Generating and Analyzing Non-Symbolic Number Stimuli

Dot arrays are central to numerical cognition research, yet existing tools for generating and analyzing these stimuli are either proprietary, require commercial software, or lack validation. We present an open-source Python toolbox with two components: a generator that creates dot arrays with precisely specified parameters, and an analyzer that extracts visual parameters from any dot array image. Both are accessible through a web-based interface requiring no programming expertise. To validate the analyzer, we generated 500 stimuli with known ground truth across five parameter sets varying in numerosity (5-30 elements) and element size variability (SD = 0-6 pixels). The analyzer achieved near-perfect recovery of numerosity (r > .99, 99.8% exact match), cumulative surface area (r = .99), average element size (r = .99), and convex hull area (r = 1.00). Accuracy was uniformly high across all numerosity ranges and size variability conditions tested. All code, stimuli, and analysis scripts are freely available at https://github.com/laurenaulet/dot-array-stimulus-toolbox and https://osf.io/ytw2x.