Simulating reaction time for Eureka effect in visual object recognition using artificial neural network

TL;DR

This paper presents an artificial neural network model that simulates the Eureka effect in visual object recognition, capturing the neural coincidence processes underlying human recognition of degraded images.

Contribution

It introduces a novel neural network model that mimics the neural coincidence mechanism associated with the Eureka recognition in humans.

Findings

The model successfully simulates Eureka recognition behavior.

It provides insights into neural processes underlying object recognition.

The approach bridges psychological theory and neural network modeling.

Abstract

The human brain can recognize objects hidden in even severely degraded images after observing them for a while, which is known as a type of Eureka effect, possibly associated with human creativity. A previous psychological study suggests that the basis of this "Eureka recognition" is neural processes of coincidence of multiple stochastic activities. Here we constructed an artificial-neural-network-based model that simulated the characteristics of the human Eureka recognition.