Emergence of behavioral phenomena and adaptation effects in human numerosity decoder using recurrent neural networks.

Humans possess an innate ability to visually perceive numerosities, which refers to the cardinality of a set. Numerous studies indicate that the lateral intraparietal cortex (LIP) and other intraparietal sulcus (IPS) regions (region) of the brain contain the neurological substrates responsible for number processing. Existing computational models of number perception often focus on a limited range of numbers and fail to account for important behavioral characteristics like adaptation effects, despite simulating fundamental aspects such as size and distance effects. To address these limitations, our study develops (introduces) a novel computational model of number perception utilizing a network of neurons with self-excitatory and mutual inhibitory properties. Our approach assumes that the mean activation of the network at steady state can encode numerosity by exhibiting a monotonically increasing relationship with the input variable set size. By optimizing the total number of inhibition strengths required, we achieve coverage of the full range of numbers through three distinct intervals: 1 to 4, 5 to 17, and 21 to 50. Remarkably, this division aligns closely with the breakpoints in numerosity perception identified in behavioral studies. Furthermore, our study develops a method for decoding the mean activation into a continuous scale of numbers spanning from 1 to 50. Additionally, we propose a mechanism for dynamically selecting the inhibition strength based on current inputs, enabling the network to operate effectively across an extended (entire) range of numerosities. Our model not only sheds new light on the generation of diverse behavioral phenomena in the brain but also elucidates how continuous visual attributes and adaptation effects influence perceived numerosity.