Neural Rationality: Modeling Decision Logic in Deep Neural Architectures

Abstract: This paper introduces the concept of Neural Rationality, a framework that aims to model logical, interpretable decision-making within deep neural architectures. Traditional deep learning excels at pattern recognition but often lacks transparent decision logic. By integrating attention mechanisms, symbolic logic modules, and cognitive constraints, neural models can emulate rational decision-making observed in humans. Researcher conducted regression and predictive analyses using benchmark datasets (DecisionQA, LogicalNLI) to quantify logical consistency and interpretability. Results shown that rational neural architectures outperform conventional models in both decision traceability and accuracy. This work contributed to the growing body of explainable AI by fusing cognitive rationality with computational precision. Keywords: Neural rationality, decision logic, deep neural networks, cognitive modeling, interpretable AI, attention mechanisms, symbolic reasoning, statistical analysis, regression modeling, rational decision-making