indexfromarray code from Trust your gut: using physiological states as a source of information is almost as effective as optimal Bayesian learning

Approaches to understanding adaptive behaviour often assume that animals have perfect information about environmental conditions or are capable of sophisticated learning. If such learning abilities are costly, however, natural selection will favour simpler mechanisms for controlling behaviour when faced with uncertain conditions. Here, we show that, in a foraging context, a strategy based only on current energy reserves often performs almost as well as a Bayesian learning strategy that integrates all previous experiences to form an optimal estimate of environmental conditions. We find that Bayesian learning gives a strong advantage only if fluctuations in the food supply are very strong and reasonably frequent. The performance of both the Bayesian and the reserve-based strategy are more robust to inaccurate knowledge of the temporal pattern of environmental conditions than a strategy that has perfect knowledge about current conditions. Studies assuming Bayesian learning are often accused of being unrealistic; our results suggest that animals can achieve a similar level of performance to Bayesians using much simpler mechanisms based on their physiological state. More broadly, our work suggests that the ability to use internal states as a source of information about recent environmental conditions will have weakened selection for sophisticated learning and decision-making systems.

现有探究适应性行为的研究范式通常假定，动物对环境条件拥有完备信息，或具备复杂的学习能力。然而倘若此类学习能力存在演化代价，那么当面临不确定环境时，自然选择将更青睐调控行为的更简易机制。本研究表明，在觅食场景中，仅基于当前能量储备的策略，其表现往往与整合所有过往经验以最优估算环境条件的贝叶斯学习（Bayesian learning）策略几乎不相上下。我们发现，仅当食物供应波动幅度极大且发生频率适中时，贝叶斯学习策略才会具备显著优势。相较于对当前环境条件拥有完备认知的策略，贝叶斯策略与基于储备的策略的表现，对环境条件时间模式的不准确认知具备更强的鲁棒性。以往假定贝叶斯学习的研究常被指责不切实际；本研究结果显示，动物可借助基于生理状态的极简机制，达成与贝叶斯学习者相近的表现水平。从更广泛的层面来看，本研究表明，将内部状态作为近期环境条件信息来源的能力，会削弱针对复杂学习与决策系统的选择压力。