Numerosity representation is encoded in human subcortex

Abstract

Certain numerical abilities appear to be relatively ubiquitous in the animal kingdom, including the ability to recognize and differentiate relative quantities. This skill is present in human adults and children, as well as in nonhuman primates and, perhaps surprisingly, is also demonstrated by lower species such as mosquitofish and spiders, despite the absence of cortical computation available to primates. This ubiquity of numerical competence suggests that representations that connect to numerical tasks are likely subserved by evolutionarily conserved regions of the nervous system. Here, we test the hypothesis that the evaluation of relative numerical quantities is subserved by lower-order brain structures in humans. Using a monocular/dichoptic paradigm, across four experiments, we show that the discrimination of displays, consisting of both large (5-80) and small (1-4) numbers of dots, is facilitated in the monocular, subcortical portions of the visual system. This is only the case, however, when observers evaluate larger ratios of 3:1 or 4:1, but not smaller ratios, closer to 1:1. This profile of competence matches closely the skill with which newborn infants and other species can discriminate numerical quantity. These findings suggest conservation of ontogenetically and phylogenetically lower-order systems in adults' numerical abilities. The involvement of subcortical structures in representing numerical quantities provokes a reconsideration of current theories of the neural basis of numerical cognition, inasmuch as it bolsters the crossspecies continuity of the biological system for numerical abilities.subcortex | numerical cognition | vision | development | phylogeny

Serial dependence in numerosity perception

“…While the most commonly acknowledged neural correlate of numerosity perception resides in the parietal cortex (e.g., Piazza, Izard, Pinel, Le Bihan, & Dehaene, 2004 ; Harvey, Klein, Petridou, & Dumoulin, 2013 ; Castaldi, Aagten-Murphy, Tosetti, Burr, & Morrone, 2016 ), recent studies demonstrated that numerical processing starts much earlier than that (e.g., Roggeman et al, 2011 ; Cavdaroglu, Katz, & Knops, 2015 ; Collins et al, 2017 ; Park et al, 2016 ; Fornaciai et al, 2017 ). In particular Collins and colleagues ( 2017 ) showed evidence for the involvement of subcortex in representing numerical information by exploiting the idea that the encoding of a visual image is facilitated when the same information is presented previously to the same eye compared to when the information was presented to the different eye. Here we reasoned that if numerical processing starts as early as in the subcortex, serial dependence for numerosity perception may be rooted in that structure.…”

“…In Experiment 1, we first address the possibility that serial dependence in numerosity perception may arise from neuronal processing in subcortical regions, as suggested by a recent study showing numerosity encoding in subcortex (Collins, Park, & Behrmann, 2017 ). To do so, we either presented all the stimuli to the same eye or segregated inducer and reference stimuli to different eyes, thus tapping into either monocular subcortical visual pathways, or cortical binocular processing.…”

Challenging the neurobiological link between number sense and symbolic numerical abilities

“…104 Behavioral evidence comparing monoptic versus dichoptic presentation of numerical stimuli even suggests that numerical information is processed in subcortical structures, before the integration of visual information from both eyes occurs. 105 Still, even if numerical information is processed in early visual areas, debate remains about where in the processing hierarchy perception of number emerges. 106,107 Frequently, the methods used to demonstrate number processing in the parietal lobe have also been used to show number-related processing in other parts of the brain.…”

Excessive visual crowding effects in developmental dyscalculia

“…Given that reaction time and accuracy can often inversely trade off with each other, we reduced the number of variables by calculating the inverse efficiency score (IES, Collins et al, 2017), corresponding to the reaction time (RT) divided by the proportion of correct responses. From the TEDI-MATH results, we computed: 1) IES Digits obtained from the results of the single-digit Arabic numeral comparison test; 2) IES Calculation -obtained by averaging together the results from the multiplication and subtraction tests and computing the IES from the combined measure; 3) IES Mathobtained by averaging the IES Digits and IES Calculation as index of general math ability.…”