Abstract

This narrative review integrates evidence from cognitive science and AI research to challenge commonly accepted dichotomies between human and artificial cognition, such as the assumed divide between genuine human understanding and mere machine pattern matching. Instead, we propose a view that recognises similarities in their cognitive architectures and processes. Human and artificial cognition seem to operate through comparable mechanisms, as both rely on statistical processing, associative pattern recognition and approximation rather than perfect logic. Through a systematic comparison of core cognitive domains across 363 articles, we highlight parallels in capabilities and limitations, including shared vulnerabilities to biases, memory distortions and decision‐making opacity. We critically examine popular narratives such as the stochastic parrot argument and the myth of human rationality. These positions often rely on idealised views of human cognition that are contradicted by cognitive and neuroscientific evidence. This review calibrates expectations of both human and artificial systems by moving beyond both AI alarmism and human exceptionalism towards a more empirically grounded perspective on cognition. Our comparative review acknowledges both the shared statistical foundations of intelligence and differences in embodiment, intentionality and phenomenological aspects of cognition. This perspective has implications for human–AI collaboration, cognitive performance benchmarking and research on AI transparency.