Support Vector Machines: Modeling The Dual Cognitive Processes of an SVM

Abstract

Can machines think fast and logical like us? In this study, we explore whether support vector machines (SVMs) - the workhorses of AI (Artificial Intelligence) - exhibit human-like heuristic judgment alongside mathematical optimization. Our experiments reveal that nonlinear SVMs can act as cognitive mimics, making surprisingly "intuitive" shortcuts reminiscent of Kahneman and Tversky's dual process theory. Yet SVMs avoid our irrational biases by combining heuristics with optimal statistical learning. These cognitive cousins both leverage the power of mental shortcuts, but only humans trip up. Our multidisciplinary results illuminate the psychology behind AI's decisions, with profound implications. We glimpse mind-like heuristics emerging from rigid math, suggesting new directions for human-aligned AI. But mysteries remain on whether SVMs' heuristic gambles are features or flaws. Do their information-savvy shortcuts point towards the essence of intuition? We discuss implications for interpreting modern AI through cognitive psychology lenses while identifying key differences. This multidisciplinary work aims to provide novel empirical insights on the interplay between heuristic and optimal practices in an important class of machine learning algorithms. The results shed light on developing human-aligned classifiers that balance the strengths of both heuristic and logical thinking. This paper takes a step towards unravelling the inner workings of one of the most used artificial intelligence models, Support Vector Machines.