An expanded model for perceptual visual single object recognition system using expectation priming following neuroscientific evidence

Abstract

Under numerous circumstances, humans recognize visual objects in their environment with remarkable response times and accuracy. Existing artificial visual object recognition systems have not yet surpassed human vision, especially in its universality of application. We argue that modeling the recognition process in an exclusive feedforward manner hinders those systems’ performance. To bridge that performance gap between them and human vision, we present a brief review of neuroscientific data, which suggests that considering an agent’s internal influences (from cognitive systems that peripherally interact with visual-perceptual processes) recognition can be improved. Then, we propose a model for visual object recognition which uses these systems’ information, such as affection, for generating expectation to prime the object recognition system, thus reducing its execution times. Later, an implementation of the model is described. Finally, we present and discuss an experiment and its results.