TY - JOUR
T1 - A visual sense of number emerges from the dynamics of a recurrent on-center off-surround neural network
AU - Sengupta, Rakesh
AU - Surampudi, Bapi Raju
AU - Melcher, David
N1 - Funding Information:
This work was supported by a European Research Council Starting Grant (agreement n. 313658) to D.M. and by the India–Trento Programme for Advanced Research (ITPAR) support to RSG .
Publisher Copyright:
© 2014 Elsevier B.V.
PY - 2014/9/25
Y1 - 2014/9/25
N2 - It has been proposed that the ability of humans to quickly perceive numerosity involves a visual sense of number. Different paradigms of enumeration and numerosity comparison have produced a gamut of behavioral and neuroimaging data, but there has been no unified conceptual framework that can explain results across the entire range of numerosity. The current work tries to address the ongoing debate concerning whether the same mechanism operates for enumeration of small and large numbers, through a computational approach. We describe the workings of a single-layered, fully connected network characterized by self-excitation and recurrent inhibition that operates at both subitizing and estimation ranges. We show that such a network can account for classic numerical cognition effects (the distance effect, Fechner's law, Weber fraction for numerosity comparison) through the network steady state activation response across different recurrent inhibition values. The model also accounts for fMRI data previously reported for different enumeration related tasks. The model also allows us to generate an estimate of the pattern of reaction times in enumeration tasks. Overall, these findings suggest that a single network architecture can account for both small and large number processing.
AB - It has been proposed that the ability of humans to quickly perceive numerosity involves a visual sense of number. Different paradigms of enumeration and numerosity comparison have produced a gamut of behavioral and neuroimaging data, but there has been no unified conceptual framework that can explain results across the entire range of numerosity. The current work tries to address the ongoing debate concerning whether the same mechanism operates for enumeration of small and large numbers, through a computational approach. We describe the workings of a single-layered, fully connected network characterized by self-excitation and recurrent inhibition that operates at both subitizing and estimation ranges. We show that such a network can account for classic numerical cognition effects (the distance effect, Fechner's law, Weber fraction for numerosity comparison) through the network steady state activation response across different recurrent inhibition values. The model also accounts for fMRI data previously reported for different enumeration related tasks. The model also allows us to generate an estimate of the pattern of reaction times in enumeration tasks. Overall, these findings suggest that a single network architecture can account for both small and large number processing.
KW - Computational model On-center off-surround
KW - Enumeration
KW - Neural network
KW - Numerical cognition
KW - Spatial attention Individuation
KW - Visual sense of numbers
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U2 - 10.1016/j.brainres.2014.03.014
DO - 10.1016/j.brainres.2014.03.014
M3 - Article
C2 - 25108042
AN - SCOPUS:84904570381
SN - 0006-8993
VL - 1582
SP - 114
EP - 124
JO - Brain Research
JF - Brain Research
ER -