Affiliations: [a] Center for Creative Cognition, SR University, Warangal, India | [b] Thapar School of Liberal Arts and Sciences, Thapar Institute of Engineering and Technology, Patiala, Punja, India | [c] Department of ECE, SR University, Warangal, India | [d] Indian Institute of Science Education and Research (IISER), Pune, India | [e] Center for Creative Cognition, SR University, Warangal, India
Correspondence:
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Corresponding author: Rakesh Sengupta, Center for Creative Cognition, SR University, Warangal, India. E-mail: [email protected].
Abstract: Analyzing visual scenes and computing ensemble statistics, known as perceptual averaging, is crucial for the stable sensory experience of a cognitive agent. Despite the apparent simplicity of applying filters to scenes, the challenge arises from our brain’s seamless transition between summarization and individuation across various reference frames (retinotopic, spatiotopic, and hemispheric). In this study, we explore the capability of a neural network to dynamically switch between individuation and summarization. Our chosen computational model, a fully connected on-center off-surround recurrent neural network previously employed for enumeration/individuation, demonstrates the potential to extract both summary statistics and achieve high individuation accuracy. Notably, our results show that the individuation accuracy can reach close to perfection within a presentation duration of 100 ms, but not so for summarization. We have also shown a spatially varying excitation version of the network that can explain quite a few interesting spatio-temporal patterns of perception. These findings not only highlight the feasibility of such a neural network but also provide insights into the temporal dynamics of ensemble perception.
Keywords: Perceptual averaging, neural network model, human visual system, temporal dynamics, human behavior, individuation, spatially varying excitation
