Model-based prioritization for acquiring protection

Protection, or the mitigation of harm, often involves the capacity to prospectively plan the actions needed to combat a threat. The computational architecture of decisions involving protection remains unclear, as well as whether these decisions differ from other positive prospective actions. Here we examine effects of valence and context by comparing protection to reward, which occurs in a different context but is also positively valenced, and punishment, which also occurs in an aversive context but differs in valence. We applied computational modeling across three independent studies (Total N=600) using five iterations of a 'two-step' behavioral task to examine model-based reinforcement learning for protection, reward, and punishment in humans. Decisions motivated by acquiring safety via protection evoked a higher degree of model-based control than acquiring reward and avoiding punishment, with no significant differences in learning rate. The context-valence asymmetry characteristic of protection increased deployment of flexible decision strategies, suggesting model-based control depends on the context in which outcomes are encountered as well as the valence of the outcome. ; License: CC0 1.0 Universal. Created: December 21, 2021; Last edited: March 16, 2022. DM and SMT are supported by the US National Institute of Mental Health grant no. 2P50MH094258 and Templeton Foundation grant TWCF0366. TW is supported by a Professor Anthony Mellows Fellowship. We thank Alexandra Hummel for her help with task development. Preregistration: The main hypotheses and methods were preregistered on the Open Science Framework (OSF), https://osf.io/4j3qz/registrations. Data availability: Task code and raw data are available through OSF, https://osf.io/4j3qz/. Author Contributions: SMT developed the study concept with input from DM. SMT designed the study with input from DM and TW. Data collection was performed by SMT. Data analysis and interpretation were performed by SMT and TW. SMT drafted the manuscript, with critical revisions ...