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This study investigates four distinct socio-cognitive models of social information use in a patch foraging task using agent-based simulations and an augmented collective drift-diffusion model. The research quantifies collective dynamics and evaluates model selection and parameter identifiability via Bayesian inference and Wasserstein distance minimization on group accuracy and departure time distributions. Key findings reveal that group accuracy temporal dynamics offer informative signatures, Bayesian inference on group accuracy distributions is most reliable, and model selection relies on oscillations while parameter identifiability depends on global accuracy, with both being condition-dependent.
Distinguishing between cognitive mechanisms and parameter recovery in social foraging reveals that oscillations signal model structure, while overall accuracy estimates parameters, highlighting a critical dissociation for understanding collective decision-making.
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.