发表机构
University of Konstanz; Max Planck Institute of Animal Behavior（康斯坦茨大学; 马克斯·普朗克动物行为研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过补丁觅食实验中的集体决策模型，比较了不同社会信息表示与整合机制，发现模型选择依赖动态振荡，参数辨识依赖全局准确性，且受实验条件影响。
AI 中文摘要
实验室中的集体补丁觅食实验提供了一个受控环境，在该环境中可以量化社会信息的使用。在此，我们考虑一个去/不去任务，其中群体在两个食物奖励概率不同的补丁之间进行选择。我们使用基于智能体的模拟，并辅以增强的集体漂移-扩散模型，来研究表示和整合社会信息的替代机制。我们考虑两种表示方式，即连续（计数表示）和离散（脉冲表示），以及两种整合机制，即偏置决策阈值（阈值调制）和修改累积信念（信念调制），从而产生四种不同的社会模型，外加一个非交互模型。我们首先表征这些模型在认知参数和实验条件下产生的集体动态。群体准确性的时间动态为潜在机制提供了信息丰富的特征。然后，我们使用贝叶斯推断和Wasserstein距离最小化，应用于群体准确性和离开时间分布，来评估模型选择和参数可辨识性。使用群体准确性分布的贝叶斯推断在所考虑的条件中提供了最可靠的识别。重要的是，模型选择和参数可辨识性与集体行为的不同方面相关联：模型选择与时间动态中振荡的存在密切相关，而参数可辨识性则与全局准确性值更密切相关。因此，用于区分潜在认知机制的信息不一定与恢复其参数所需的信息相同。我们的结果进一步表明，识别不仅取决于模型结构，还取决于实验条件。
英文摘要
Collective patch-foraging experiments in the laboratory provide a controlled setting in which social information use can be quantified. Here, we consider a go/no-go task in which groups choose between two patches differing in food reward probability. We use agent-based simulations, underpinned by an augmented collective drift-diffusion model, to investigate alternative mechanisms for representing and integrating social information. We consider two representations, continuous (counting representation) and discrete (pulsatile representation), and two integration mechanisms, biasing the decision threshold (threshold modulation) and modifying the accumulated belief (belief modulation), yielding four distinct social models, in addition to a non-interacting model. We first characterize the collective dynamics generated by these models across cognitive parameters and experimental conditions. The temporal dynamics of group accuracy provide informative signatures of the underlying mechanisms. We then assess model selection and parameter identifiability using Bayesian inference and Wasserstein distance minimization, applied to group-accuracy and departure-time distributions. Bayesian inference using distributions of group accuracy provides the most reliable identification across the conditions considered. Importantly, model selection and parameter identifiability are associated with different aspects of collective behavior: model selection is closely linked to the presence of oscillations in the temporal dynamics, whereas parameter identifiability is more closely related to the global accuracy value. Thus, the information available for distinguishing the underlying cognitive mechanisms is not necessarily the same as that required to recover their parameters. Our results further show that identification depends not only on model structure but also on the experimental conditions.
