The importance of embodiment for effective robot performance has been postulated for a long time. Despite this, only relatively recently concrete quantitative models were put forward to characterize the advantages provided by a well-chosen embodiment. We here use one of these models, based on the concept of relevant information, to identify in a minimalistic scenario how and when embodiment affects the decision density. Concretely, we study how embodiment affects information costs when, instead of atomic actions, scripts are introduced, that is, predefined action sequences. Their inclusion can be treated as a straightforward extension of the basic action space. We will demonstrate the effect on informational decision cost of utilizing scripts vs. basic actions using a simple navigation task. Importantly, we will also employ a world with “mislabeled” actions, which we will call a “twisted” world. This is a model which had been used in an earlier study of the influence of embodiment on decision costs. It will turn out that twisted scenarios, as opposed to well-labeled (“embodied”) ones, are significantly more costly in terms of relevant information. This cost is further worsened when the agent is forced to lower the decision density by employing scripts (once a script is triggered, no decisions are taken until the script has run to its end). This adds to our understanding why well-embodied (interpreted in our model as well-labeled) agents should be preferable, in a quantifiable, objective sense.

Animals and humans encounter many tasks which permit ritualized behaviours, essentially fixed action sequences or "scripts", similar to options known from Reinforcement Learning, but proceeding without intermediate decisions.While running a script, they proceed in an open-loop fashion. However even when these are already known, an agent needs to decide whether to perform a basic action or to trigger a script regarding the particular task. Here we study if including such scripts (i.e. behaviour rituals) is advantageous from the point of view of the relevant information required to take the decision to start such a script depending on the tasks. To achieve this, we modify the relevant information framework including sequences of basic actions to the possible actions.

Animals and humans encounter many tasks which permit ritualized behaviours, essentially fixed action sequences or "scripts", similar to options known from Reinforcement Learning, but proceeding without intermediate decisions. While running a script, they proceed in an open-loop fashion. However even when these are already known, an agent needs to decide whether to perform a basic action or to trigger a script regarding the particular task. Here we study if including such scripts (i.e. behaviour rituals) is advantageous from the point of view of the relevant information required to take the decision to start such a script depending on the tasks. To achieve this, we modify the relevant information framework including sequences of basic actions to the possible actions.

Many models of organism navigation concern themselves in essence just with the sequence of locations visited and how to manage it. However, larger and bulkier organisms have also to deal with managing momentum. We expect that this affects the cognitive management of movement. Here we propose a simple model for the information processing complexity of navigation when velocity and acceleration are considered, moving away from a kinematic perspective to a partially dynamic model, to separate the effects of location and momentum management.The work is discussed in the context of recent neurobiological research suggesting that biological agents plan around acceleration and deceleration phases, showing high neural activity during their body's velocity changes.