摘要：A large body of research has shown that human adults are fast and accurate at enumerating arrays of ~1-4 items.This phenomenon has been called subitizing.Above this range, enumeration is slower and less accurate.The subitizing range has been related to individual differences in variables such as mathematical abilities, working memory, etc.The two most common methods for calculating subitizing range today – bilinear fit and sigmoid fit – have their strengths and weaknesses.By combining these two methods, we overcome their biggest limitations and come up with a novel way for calculating Individual Subitizing Range (ISR).This paper introduces this new method as well as empirical studies designed to test the new method.We replicated classic effects from the literature and obtain a high correlation with the sigmoid fit method.This paper includes a Matlab code for easy calculation of ISR as well as a ready-to-use experimental file for testing ISR.We hope that these tools would be of use to researchers studying individual differences in the subitizing range.
