Specific Sensitivity to Rare and Extreme Events: Quasi-Complete Black Swan Avoidance vs Partial Jackpot Seeking in Rat Decision-Making

Most behavioral studies in animals investigate risk using outcome probabilities larger than 10%. However, real-world Decision-Making often requires evaluating events that are both extremely unlikely and highly consequential. To address this gap, we developed an experimental and computational framework to quantify how rats detect and adapt to rare (probability 10 standard deviations) outcomes (REE). Using a four-armed bandit task, animals chose between options associated with probabilistic rewards (sugar pellets) or punishments (time-out delays). Depending on the animals choice, REE can occur or not, allowing us to probe how rats integrate information across both common and fat-tailed event distributions. Across subjects, behavior showed restricted diversification (typically two out of four options) and clear sensitivity to REE, expressed as a systematic avoidance of rare and extreme punishments (Black Swans) combined with partial exposure to rare and extreme gains (Jackpots). The dominant behavioral phenotype displayed a near-complete suppression of exposure to Black Swans, whereas exposure to Jackpots remained only moderate. This asymmetric sensitivity came at a cost: these animals accepted smaller frequent gains and larger frequent losses to avoid catastrophic outcomes. To account for these behaviors, we implemented an augmented reinforcement learning model in which REE are weighted separately from frequent events. Fitting this model to individual behavioral data captured subjects decision patterns far better than standard Q-learning, which systematically failed to reproduce the observed asymmetry. The best-fitting model suggests that the rat brain segregates information from the central outcome distribution and the distribution tails, assigning distinct value weights to REE during action selection. This work provides the first evidence that rodents spontaneously adapt their learning strategies to avoid extreme punishments while partially maintaining exposure to rare and extreme gains, suggesting that frequent outcomes are treated separately from rare and extreme ones through different weights in Decision-Making.