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.

Applying computational models of reinforcement learning can help to improve psychotherapeutic interventions by helping to identify the therapeutic mechanisms of change and identifying for whom therapi...

NIH Funding Opportunities and Notices in the NIH Guide for Grants and Contracts: Notice of Early Expiration of RFA-EB-22-002,