Humans exhibit a striking tendency to persist with chosen goals. This strong attachment to goals can often appear irrational – a perspective captured by terms such as perseverance or sunk-cost biases. In this review, we explore how goal commitment could stem from several adaptive mechanisms, including those that optimise cognitive resources, shield decisions from interference, and scaffold motivation in the absence of accessible reward signals. We propose that these computational considerations have important implications for algorithmic architectures supporting decision making, including separate algorithms for goal selection and implementation, and for monitoring ongoing goals versus alternative sources of reward. Finally, we discuss how a variety of mechanisms supporting goal commitment and abandonment could relate to dimensions affected in mental health.

Reinforcement learning agents learn from rewards, but humans can uniquely assign value to novel, abstract outcomes in a goal-dependent manner. However, this flexibility is cognitively costly, making l...

Extracting time-varying latent variables from computational cognitive models plays a key role in uncovering the dynamic cognitive processes that drive behaviors. However, existing methods are limited ...

Many natural behaviors require the flexible coordination of sampling sensory information from the external environment and the use of information from memory. Kumle et al. show that long-term memory s...