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Abstract We offer three advances to the perceptual crossing simulation studies, which are aimed at challenging methodological individualism in the analysis of social cognition. First, we evolve and systematically test agents in rigorous conditions, identifying a set of 26 “robust circuits” with consistently high and generalizing performance. Next, we transform the sensor from discrete to continuous, facilitating a bifurcation analysis of the dynamics that shows that nonequilibrium dynamics are key to the mutual maintenance of interaction. Finally, we examine agents’ performance with partners whose neural controllers are different from their own and with decoy objects of fixed frequency and amplitude. Nonclonal performance varies and is not predicted by genotypic distance. Frequency-amplitude values that fool the focal agent do not include the agent’s own values. Altogether, our findings accentuate the importance of dynamical and nonclonal analyses for simulated sociality, emphasize the role of dialogue between artificial and human studies, and highlight the contributions of simulation studies to understanding social interactions. 

An analysis of the language we use in scientific practice is critical to developing more rigorous and sound methodologies. This article argues that how certain methods of description are commonly employed in cognitive science risks obscuring important features of an agent’s cognition. We propose to make explicit a method of description whereby the concept of cognitive distinctions is the core principle. A model of referential communication is developed and analyzed as a platform to compare methods of description. We demonstrate that cognitive distinctions, realized in a graph theoretic formalism, better describe the behavior and perspective of a simple model agent than other, less systematic or natural language–dependent methods. We then consider how different descriptions relate to one another in the broader methodological framework of minimally cognitive behavior. Finally, we explore the consequences of, and challenges for, cognitive distinctions as a useful concept and method in the tool kit of cognitive scientists. 

This paper investigates the capability of embodied agents to perform a sequential counting task. Drawing inspiration from honeybee studies, we present a minimal numerical cognition task wherein an agent navigates a 1D world marked with landmarks to locate a previously encountered food source. We evolved embodied artificial agents controlled by dynamical recurrent neural networks to be capable of associating a food reward with encountering a number of landmarks sequentially. To eliminate the possibility of the evolved agents relying on distance to locate the target landmark, we varied the positions of the landmarks across trials. Our experiments demonstrate that embodied agents equipped with relatively small neural networks can accurately enumerate and remember up to five landmarks when encountered sequentially. Counter to the intuitive notion that numerical cognition is a complex, higher cortical function, our findings support the idea that numerical discrimination can be achieved in relatively compact neural circuits.