Perception and Attention Applying Cognitive Psychology Principal to Improve AI Vision System

Abstract

This research investigates how cognitive psychology principles, specifically Gestalt perception and attention processes, can be applied to artificial intelligence (AI) vision systems to enhance their accuracy and responsiveness in real-world applications. By utilizing a quantitative research methodology, data was collected from 120 AI industry experts in Punjab, Pakistan, through a structured questionnaire. The statistical analyses, including correlation (r = 0.602, p < 0.01) and regression (R² = 1.000), reveal a robust relationship between cognitive-inspired solutions and advancements in AI vision technologies. The results highlight that models based on Gestalt perception and biologically inspired attention mechanisms significantly contribute to AI’s ability to accurately recognize objects in complex visual scenes. Despite these advancements, current AI systems still face challenges with contextual reasoning and real-time adaptability, indicating a need for further development of hybrid AI models that combine cognitive approaches with deep learning techniques. The research concludes by suggesting that future studies should focus on integrating neuroscientific theories and exploring the ethical implications of AI to further improve the capabilities of AI vision systems.