A systematic review of human avoidance learning: Cognition, computation, and methods

Avoidance behaviour is fundamental for survival but can become maladaptive in clinical conditions. A large literature has accumulated on the dynamics of human avoidance learning, but current theories do not provide an exhaustive account of the evidence. In this systematic review, we identify N = 116 studies on human avoidance learning. We analyse them with the goal of establishing empirical phenomena to include in theory-building, and examine their diagnostic value in differentiating between competing theories. We find that the evidence consistently contradicts theories based on associative learning, and favours inference-based accounts. Existing theories within this framework, however, do not predict several of the identified phenomena, calling for further theoretical development. Methodologically, we observe that the problem setting in the most common experimental paradigms is structurally less complex than real-world settings, and unlikely to expose the limitations of inference-based mechanisms. In consequence, we argue that paradigms with larger computational demands and a wider range of action options are required to expose the mechanisms underlying avoidance learning. Collectively, these insights provide a foundation for theory development and methodological innovation, with significant implications for advancing clinical interventions aimed at maladaptive avoidance behaviour.