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Human description of the surrounding world may spontaneously rely on others' perspective, which is a crucial component of social cognition. In five studies, participants were asked to describe the spatial relations between objects in visual scenes including, or not, other agents. In Experiment 1, a substantial proportion of participants used an other-centered perspective in the presence of another agent, replicating classical findings. To our own surprise, we also observed that an even greater number of participants used an other-centered perspective when the human agent was replaced by an armchair. In order to explore this phenomenon, Experiments 2 to 5 compared the respective strength of chair-centered and agent-centered perspectives and/or set them into conflict. A significant proportion of participants spontaneously took the seat's perspective even when it may not be sat on (Experiments 3 and 4) and even when the seat was not referred to (Experiments 4 and 5). Altogether, these findings suggest that perspective taking may spontaneously apply to inanimate objects. These results question whether such tendencies originate from social cognitive skills—as classically assumed—or reveal a nonsocial phenomenon. Future works should specifically test the widely assumed social nature of spontaneous perspective-taking. [ABSTRACT FROM AUTHOR]

In this paper, we propose a general framework for local path-planning and steering that can be easily extended to perform high-level behaviors. Our framework is based on the concept of affordances: the possible ways an agent can interact with its environment. Each agent perceives the environment through a set of vector and scalar fields that are represented in the agent's local space. This egocentric property allows us to efficiently compute a local space-time plan and has better parallel scalability than a global fields approach. We then use these perception fields to compute a fitness measure for every possible action, defined as an affordance field. The action that has the optimal value in the affordance field is the agent's steering decision. We propose an extension to a linear space-time prediction model for dynamic collision avoidance and present our parallelization results on multicore systems. We analyze and evaluate our framework using a comprehensive suite of test cases provided in SteerBench and demonstrate autonomous virtual pedestrians that perform steering and path planning in unknown environments along with the emergence of high-level responses to never seen before situations. [ABSTRACT FROM AUTHOR]