As intelligent systems become increasingly integrated into our daily lives, understanding human cognition, motivation, and behavior is essential to designing effective and trustworthy algorithmic systems. Although empirical research on human-algorithm interaction (HAI) is growing rapidly, we nonetheless observe that theoretical frameworks often lack the integration of foundational principles from cognitive and engineering psychology. The STEP-HAI workshop (Strengthening Theoretical and Empirical Principles in Human-AI Interaction) addresses this challenge by bringing together researchers and practitioners to discuss psychological frameworks, experimental methodologies, and measurement approaches in HAI. Key topics include information processing, cognitive and behavioral factors, and the influence of user perceptions, such as trust or situational awareness, in algorithm-assisted decision-making. By fostering cross-disciplinary dialogue between researchers and practitioners, STEP-HAI aims to define research priorities and bridge the gap between theoretical models and real-world AI applications, promoting more robust, psychologically grounded approaches to AI development.