title: Modeling the approximate number system to quantify the contribution of visual stimulus features.
abstract: The approximate number system (ANS) subserves estimation of the number of items in a set. Typically, ANS function is assessed by requiring participants to compare the number of dots in two arrays. Accuracy is determined by the numerical ratio of the sets being compared, and each participant's Weber fraction (w) provides a quantitative index of ANS acuity. When making numerical comparisons, however, performance is also influenced by non-numerical features of the stimuli, such as the size and spacing of dots. Current models of numerosity comparison do not account for these effects and consequently lead to different estimates of w depending on the methods used to control for non-numerical features. Here we proffer a new model that teases apart the effects of ANS acuity from the effects of non-numerical stimulus features. The result is an estimate of w that is a more theoretically valid representation of numerical acuity and novel terms that denote the degree to which a participant's perception of number is affected by non-numerical features. We tested this model in a sample of 20 adults and found that, by correctly attributing errors due to non-numerical stimulus features, the w obtained was more reliable across different stimulus conditions. We found that although non-numerical features biased numerosity discriminations in all participants, number was the primary feature driving discriminations in most of them. Our findings support the idea that, while numerosity is a distinct visual quantity, the internal representation of number is tightly bound to the representation of other magnitudes. This tool for identifying the different effects of the numerical and non-numerical features of a stimulus has important implications not only for the behavioral investigation of the ANS, but also for the collection and analyses of neural data sets associated with ANS function.
authors.affiliation: Center for Cognitive Neuroscience, Duke University, Durham, NC, USA; Department of Neurobiology, Duke University, Durham, NC, USA. Electronic address: nicholas.dewind@duke.edu.
authors.forename: Nicholas K
authors.initials: NK
authors.lastname: DeWind
authors.affiliation: Center for Cognitive Neuroscience, Duke University, Durham, NC, USA; Department of Neurobiology, Duke University, Durham, NC, USA.
authors.forename: Geoffrey K
authors.initials: GK
authors.lastname: Adams
authors.affiliation: Center for Cognitive Neuroscience, Duke University, Durham, NC, USA; Department of Neurobiology, Duke University, Durham, NC, USA.
authors.forename: Michael L
authors.initials: ML
authors.lastname: Platt
authors.affiliation: Center for Cognitive Neuroscience, Duke University, Durham, NC, USA; Department of Psychology and Neuroscience, Duke University, Durham, NC, USA.
authors.forename: Elizabeth M
authors.initials: EM
authors.lastname: Brannon
mesh_terms: D000328:Adult; D005260:Female; D006801:Humans; D007600:Judgment; D008297:Male; D008433:Mathematics; D008960:Models, Psychological; D010775:Photic Stimulation; D055815:Young Adult
keywords: Approximate number system; Magnitude comparison; Number sense; Numerical cognition; Psychophysics