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Abstract Protein sequence evolution in the presence of epistasis makes many previously acceptable amino acid residues at a site unfavorable over time. This phenomenon of entrenchment has also been observed with neutral substitutions using Potts Hamiltonian models. Here, we show that simulations using these models often evolve non-neutral proteins. We introduce a Neutral-with-Epistasis (N×E) model that incorporates purifying selection to conserve fitness, a requirement of neutral evolution. N×E protein evolution revealed a surprising lack of entrenchment, with site-specific amino-acid preferences remaining remarkably conserved, in biologically realistic time frames despite extensive residue coupling. Moreover, we found that the overdispersion of the molecular clock is caused by rate variation across sites introduced by epistasis in individual lineages, rather than by historical contingency. Therefore, substitutional entrenchment and rate contingency may indicate that adaptive and other non-neutral evolutionary processes were at play during protein evolution. 

When interacting with a robot, humans form con-ceptual models (of varying quality) which capture how the robot behaves. These conceptual models form just from watching or in-teracting with the robot, with or without conscious thought. Some methods select and present robot behaviors to improve human conceptual model formation; nonetheless, these methods and HRI more broadly have not yet consulted cognitive theories of human concept learning. These validated theories offer concrete design guidance to support humans in developing conceptual models more quickly, accurately, and flexibly. Specifically, Analogical Transfer Theory and the Variation Theory of Learning have been successfully deployed in other fields, and offer new insights for the HRI community about the selection and presentation of robot behaviors. Using these theories, we review and contextualize 35 prior works in human-robot teaching and learning, and we assess how these works incorporate or omit the design implications of these theories. From this review, we identify new opportunities for algorithms and interfaces to help humans more easily learn conceptual models of robot behaviors, which in turn can help humans become more effective robot teachers and collaborators.