Henrik Svensson

Evolving cognitive scaffolding and environment adaptation: a new research direction for evolutionary robotics

Many researchers in embodied cognitive science and artificial intelligence, and evolutionary robotics in particular, emphasize the interaction of brain, body and environment as crucial to the emergence of intelligent, adaptive behaviour. Accordingly, the interaction between agent and environment, as well as the co-adaptation of artificial brains and bodies, has been the focus of much research in evolutionary robotics. Hence, there are plenty of studies of robotic agents/species adapting to a given environment. Many animals, on the other hand, in particular humans, to some extent can choose to adapt the environment to their own needs instead of adapting (only) themselves. That alternative has been studied relatively little in robot experiments. This paper, therefore, presents some simple initial simulation experiments, in a delayed response task setting, that illustrate how the evolution of environment adaptation can serve to provide cognitive scaffolding that reduces the requirements for individual agents. Furthermore, theoretical implications, open questions and future research directions for evolutionary robotics are discussed.

Dreaming of electric sheep? Exploring the functions of dream-like mechanisms in the development of mental imagery simulations

According to the simulation hypothesis, mental imagery can be explained in terms of predictive chains of simulated perceptions and actions, i.e., perceptions and actions are reactivated internally by our nervous system to be used in mental imagery and other cognitive phenomena. Our previous research shows that it is possible but not trivial to develop simulations in robots based on the simulation hypothesis. While there are several previous approaches to modelling mental imagery and related cognitive abilities, the origin of such internal simulations has hardly been addressed. The inception of simulation (InSim) hypothesis suggests that dreaming has a function in the development of simulations by forming associations between experienced, non-experienced but realistic, and even unrealistic perceptions. Here, we therefore develop an experimental set-up based on a simple simulated robot to test whether such dream-like mechanisms can be used to instruct research into the development of simulations and mental imagery-like abilities. Specifically, the hypothesis is that ‘dreams’ informing the construction of simulations lead to faster development of good simulations during waking behaviour. The paper presents initial results in favour of the hypothesis.

Modeling the Development of Goal-Specificity in Mirror Neurons

Neurophysiological studies have shown that parietal mirror neurons encode not only actions but also the goal of these actions. Although some mirror neurons will fire whenever a certain action is perceived (goal-independently), most will only fire if the motion is perceived as part of an action with a specific goal. This result is important for the action-understanding hypothesis as it provides a potential neurological basis for such a cognitive ability. It is also relevant for the design of artificial cognitive systems, in particular robotic systems that rely on computational models of the mirror system in their interaction with other agents. Yet, to date, no computational model has explicitly addressed the mechanisms that give rise to both goal-specific and goal-independent parietal mirror neurons. In the present paper, we present a computational model based on a self-organizing map, which receives artificial inputs representing information about both the observed or executed actions and the context in which they were executed. We show that the map develops a biologically plausible organization in which goal-specific mirror neurons emerge. We further show that the fundamental cause for both the appearance and the number of goal-specific neurons can be found in geometric relationships between the different inputs to the map. The results are important to the action-understanding hypothesis as they provide a mechanism for the emergence of goal-specific parietal mirror neurons and lead to a number of predictions: (1) Learning of new goals may mostly reassign existing goal-specific neurons rather than recruit new ones; (2) input differences between executed and observed actions can explain observed corresponding differences in the number of goal-specific neurons; and (3) the percentage of goal-specific neurons may differ between motion primitives.

Beyond bodily anticipation

There is a long history of implementing internal simulation mechanisms in robotics, typically for the purpose of predicting the outcomes of motor commands before executing them. In the literature on human cognition, however, the relevance of such mechanisms goes beyond that of prediction: they also provide foundational aspects of social cognition and interaction.In this paper, we present a review of internal simulation mechanisms from this perspective. We contrast the roles they play in human cognition, in particular in the context of social interaction, with robotic implementations. We further discuss work in social robotics, emphasising in particular that a substantial effort currently goes into evaluating social robot systems, but that social robots to date are still limited in their abilities. We further discuss episodic simulations, which are functionally distinct from the type of internal simulations we consider here, and note their role in prospective thought in particular. Overall, we conclude that one of the necessary next steps on the road to social robots may be to develop social abilities from the bottom up using internal simulations. By reviewing how these aspects all tie together in human cognition, we hope to clarify how this may be achieved.