Fear and anxiety are adaptive emotions that serve important defensive functions, yet in excess, they can be debilitating and lead to poor mental health. Computational modelling of behaviour provides a mechanistic framework for understanding the cognitive and neurobiological bases of fear and anxiety, and has seen increasing interest in the field. In this brief review, we discuss recent developments in the computational modelling of human fear and anxiety. Firstly, we describe various reinforcement learning strategies that humans employ when learning to predict or avoid threat, and how these relate to symptoms of fear and anxiety. Secondly, we discuss initial efforts to explore, through a computational lens, approach-avoidance conflict paradigms that are popular in animal research to measure fear- and anxiety-relevant behaviours. Finally, we discuss negative biases in decision-making in the face of uncertainty in anxiety.