Critical intelligence: Computing defensive behaviour

Characterising the mechanisms underlying naturalistic defensive behavior remains a significant challenge. While substantial progress has been made in unravelling the neural basis of tightly constrained behaviors, a critical gap persists in our comprehension of the circuits that implement algorithms capable of generating the diverse defensive responses observed outside experimental restrictions. Recent advancements in neuroscience technology now allow for an unprecedented examination of naturalistic behaviour. To help provide a theoretical grounding for this nascent experimental programme, we summarise the main computational and statistical challenges of defensive decision making, encapsulated in the concept of critical intelligence. Next, drawing from an extensive literature in biology, machine learning, and decision theory, we explore a range of candidate solutions to these challenges. While the proposed solutions offer insights into potential adaptive strategies, they also present inherent trade-offs and limitations in their applicability across different biological contexts. Ultimately, we propose series of experiments designed to differentiate between these candidate solutions, providing a roadmap for future investigations into the fundamental defensive algorithms utilized by biological agents and their neural implementation. Thus, our work aims to provide a roadmap towards broader understanding of how complex defensive behaviors are orchestrated in the brain, with implications for both neuroscience research and the development of more sophisticated artificial intelligence systems.