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Adaptive cognitive biases, such as “optimism,” may have evolved as heuristic rules for computationally efficient decision-making, or as error-management tools when error payoff is asymmetrical. Ecologists typically use the term “optimism” to describe unrealistically positive expectations from the future that are driven by positively biased initial belief. Cognitive psychologists on the other hand, focus on valence-dependent optimism bias, an asymmetric learning process where information about undesirable outcomes is discounted (sometimes also termed “positivity biased learning”). These two perspectives are not mutually exclusive, and both may lead to similar emerging space-use patterns, such as increased exploration. The distinction between these two biases may becomes important, however, when considering the adaptive value of balancing the exploitation of known resources with the exploration of an ever-changing environment. Deepening our theoretical understanding of the adaptive value of valence-dependent learning, as well as its emerging space-use and foraging patterns, may be crucial for understanding whether, when and where might species withstand rapid environmental change. We present the results of an optimal-foraging model implemented as an individual-based simulation in continuous time and discrete space. Our forager, equipped with partial knowledge of average patch quality and inter-patch travel time, iteratively decides whether to stay in the current patch, return to previously exploited patches, or explore new ones. Every time the forager explores a new patch, it updates its prior belief using a simple single-parameter model of valence-dependent learning. We find that valence-dependent optimism results in the maintenance of positively biased expectations (prior-based optimism), which, depending on the spatiotemporal variability of the environment, often leads to greater fitness gains. These results provide insights into the potential ecological and evolutionary significance of valence-dependent optimism and its interplay with prior-based optimism.

Biased Learning as a Simple Adaptive Foraging Mechanism