Socio-cognitive models in a patch foraging setting: a case study for model selection and parameter identifiability methods

arXiv:2609.36231 · q-bio.NC · Submitted 2026-09-28 · Read on arXiv

q-bio.NC

Submitted: 2026-09-28

Updated: 2026-09-28

Code: https://github.com/lblummoyse/t

Terminology

• Accuracy — 66× in this paper · explained in 1 episode
• Parameter Identifiability — 33× in this paper · explained in 2 episodes
• Bayesian Inference — 16× in this paper · explained in 5 episodes
• Diffusion Model — 6× in this paper · explained in 8 episodes
• Wasserstein Distance — 4× in this paper · explained in 4 episodes
• Computational Biology — 3× in this paper · explained in 2 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Degeneracy — 2× in this paper · explained in 3 episodes
• Abundance — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Adaptive Planning — 1× in this paper · explained in 1 episode
• Agent-Based Simulation — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Cooperation — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Diffusion Process — 1× in this paper · explained in 2 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Inverse Problem — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Stochastic Dynamics — 1× in this paper · explained in 2 episodes
• Temporal Correlations — 1× in this paper · explained in 2 episodes
• Uncertainty Quantification — 1× in this paper · explained in 8 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Convergent Learning: Do different neural networks learn the same representations?
• Divergent representations of ethological visual inputs emerge from supervised, unsupervised, and reinforcement learning
• Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis

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