The Representation Of Numerosity In The Human Brain And Machines

The capacity to estimate the number of objects (numerosity) in the environment is ontogenetically precocious and phylogenetically ancient. In animals, this ability holds significant adaptive advantages, directly influencing survival and reproductive success. In humans, it may serve an additional purpose by providing a start-up kit for the acquisition of symbolic numbers, thus making it a potential focus for mathematics education and intervention strategies. Behavioral, neurophysiological, and neuroimaging findings suggest that numerosity information is directly extracted from the environment. However, numerosity is inherently linked with other visual characteristics of sets (such as larger sets often occupy more space or are more densely spaced), making it challenging to determine the extent to which the observed response to numerosity is distinct from the response to other visual attributes. In my PhD research I provide experimental evidence through neuroimaging and computational modeling techniques elucidating where, when, and how numerical information is encoded in the human brain. This work therefore provides a threefold contribution. First, I show that numerosity is represented over and above nonnumeric visual features in a widespread network of areas starting from early visual areas and further amplified in associative areas along the dorsal but also notably the ventral stream, and that the neural representational geometries of regions across the two steams are substantially identical. Second, I showed that numerosity is represented at an early stage and seemingly in parallel across of a set of regions including early visual, parietal, and temporal, preceding the emergence of non-numeric features that could indirectly contribute to numerosity computation. Finally, by comparing the fMRI data with a convolutional neural network (CNN) to explore similarities and differences between the model and human brain data, I discovered that although the CNN can perform approximate numerosity comparisons and the structure of their representation in their hidden layers captures well numerosity representation in early visual areas of humans, it falls short of fully simulating the way in which associative brain regions represent numerosity. Taken together, the findings of this thesis provide experimental evidence supporting the notion that number is a primary visual feature, encoded independent from other visual features quickly and widely across the human brain. Furthermore, they emphasize the need for additional investigation to unravel the computational mechanisms underlying numerosity in the human brain.