Enacted Visual Perception: A Computational Model based on Piaget Equilibrium

TL;DR

This paper introduces a computational model of visual perception inspired by Piaget's equilibrium, integrating CNNs with adaptive filters controlled by a high-level signal representing the mind's state.

Contribution

It presents a novel perception model combining CNNs with a control mechanism based on Piaget's equilibrium, bridging psychology and neural network design.

Findings

Enhanced filter performance in CNNs through adaptive adjustment.

The model simulates perceptual equilibrium reflecting mental state.

Potential for improved visual perception systems.

Abstract

In Maurice Merleau-Ponty's phenomenology of perception, analysis of perception accounts for an element of intentionality, and in effect therefore, perception and action cannot be viewed as distinct procedures. In the same line of thinking, Alva No\"{e} considers perception as a thoughtful activity that relies on capacities for action and thought. Here, by looking into psychology as a source of inspiration, we propose a computational model for the action involved in visual perception based on the notion of equilibrium as defined by Jean Piaget. In such a model, Piaget's equilibrium reflects the mind's status, which is used to control the observation process. The proposed model is built around a modified version of convolutional neural networks (CNNs) with enhanced filter performance, where characteristics of filters are adaptively adjusted via a high-level control signal that accounts for the thoughtful activity in perception. While the CNN plays the role of the visual system, the control signal is assumed to be a product of mind.