Cognitive systems anticipate, assimilate, and adapt. They develop and learn, predict, explain, and imagine. In this chapter, we look briefly at the two principal paradigms of cognition, cognitivism and emergence, to determine what embodied form each entails, if any. We highlight one specific emergent approach to embodied cognition enaction and discuss the challenges it poses for the advancement of both robotics and cognition.

The growth of cognitive capabilities in biological systems and particularly in humans is assisted by the extensive and safe interaction of their bodies with their living environment. This ability to interact safely with the environment is mainly due to a set of inherent behaviours/properties that allow the biological systems to intelligently react to the interaction at the body/actuation level with minimum active control. One fundamental intrinsic characteristic of the bio logical systems is the compliance. Looking at the diverse range of actuation techniques, the natural world appears to have developed a much more ubiquitous actuation design, with the organic muscle that provides power to animals ranging from the largest whales to microbes with adaptation to cope with environmental extremes. The potential benefits, which can be gained in any mechanism with the incorporation of biologically inspired compliant actuation concepts, are well known. However, the majority of today's robots lack these characteristics. This chapter reviews some of the solution adopted to implement the compliant behaviour in robotic systems and introduces a case study of a new compact soft actuation unit intended to be used in multi-degree of freedom and small-scale robotic systems such as the cognitive child humanoid robot 'iCub'.

The concept of object affordances describes the possible ways whereby an agent (either biological or artificial) can act upon an object. By observing the effects of actions on objects with certain properties, the agent can acquire an internal representation of the way the world functions with respect to its own motor and perceptual skills. Thus, affordances encode knowledge about the relationships between action and effects lying at the core of high-level cognitive skills such as planning, recognition, prediction and imitation. Humans learn and exploit object affordances through their entire lifespan, by either autonomous exploration of the world or social interaction. Building on a biological motivation and aiming at the development of adaptive robotic systems, we propose a computational model capable of encoding object affordances during exploratory learning trials. We represent this knowledge as a Bayesian network and rely on statistical learning and inference methods to generate and explore the network, efficiently dealing with uncertainty, redundancy and irrelevant information. The affordance model serves as base for an imitation learning framework, which exploits the recognition and planning capabilities to learn new tasks from demonstrations. We show the application of our model in a real-world task in which a humanoid robot interacts with objects, uses the acquired knowledge and learns from demonstrations. Results illustrate the success of our approach in learning object affordances and generating complex cognitive behavior.

The Prisoner's Dilemma (PD) game is a two-person game that is widely used as a model of social interactions. The structure of the PD game represents a dilemma between the individual and the collective rationality. The interest in studying PD game arises from the idea that many social situations and problems such as overpopulation, pollution, energy savings, etc. have such a dilemma structure. PD games are specifically used as a tool for studying cooperative behavior.

The coupling between a body (in an extended sense that encompasses both neural and non-neural dynamics) and its environment is here conceived as a critical substrate for cognition. We propose and discuss the plan for a neurocomputational cognitive architecture for robotic agents, so far implemented in its minimal form for supporting the behavior of a simple simulated robotic agent. A non neural internal bodily mechanism (crucially characterized by a timescale much slower than the normal sensory-motor interactions of the robot with its environment) extends the cognitive potential of a system composed of purely reactive parts with a dynamic action selection mechanism and the capacity to integrate information over time. The same non-neural mechanism is the foundation for a novel, minimalist anticipatory architecture, implementing our bodily anticipation hypothesis and capable of swift readaptation to related yet novel tasks.

The embodied character of cognitive motor systems that has greatly influenced the understanding of their constitution and function is reflected in many recent models. Embodiment conditions that system behaviours must take appropriate account of energy expenditure and metabolic costs that are unavoidable in a physically realised medium. We here consider that optimisation, presumed to result from both phylogenetic and ontogenetic processes, can be used to con strain the space of potential degrees of freedom of a system, ensuring that the resulting action is efficient and smooth. To understand the emerging adaptations, it is necessary to factor in the properties of the physical and physiological substrate that anchor the system's goal-oriented performance. However, the embodied nature of speech production has been disregarded by most phonological research to date. This leads to a failure in providing a coherent phonological grounding of a wide range of phenomena extensively documented by experimental phoneticians, in particular those associated with the relative timing of gestures (also called gestural phasing) in connected speech and its variability as found in different manners of speech. Existing phonological theories of sequencing rely on essentially external system-wide rules and principles or explicit dynamical constraints governing phasing to account for various suprasegmental properties and prosodic parameters of an utterance. We introduce here a new and highly abstract modelling platform developed to investigate the embodied character of speech. The physically instantiated, second-order dynamic nature of the system allows us to define and exactly evaluate various cost functions, which we hypothesise to play a role in efficient gestural sequencing. We investigate the general dynamical properties of the system, and identify a set of its high-level, intentional parameters linked to the cost functions associated with its goal-oriented performance. We show that the phenomena accompanying gestural sequencing, coarticulation, fluency and prosodic modulation emerge as consequences of a non-trivially formulated efficiency constraint, thus providing a principled phonological account of phonetic reality.

Behavioral finance is a subdiscipline of finance that uses insights from cognitive and social psychology to enrich our knowledge of how investors make their financial decisions. Agent-based artificial financial markets are bottom-up models of financial markets that start from the micro level of individual investor behavior and map it into the macro level of aggregate market phenomena. It has been recognized in the literature, yet not fully explored, that such agent-based models are very suitable tool to generate or test various behavioral hypotheses. To pursue this research idea, first we develop a conceptual model of individual investor that consists of a cognitive model of the investor and a description of the investment environment. In the modeling tradition of cognitive science and intelligent systems, the investor is seen as learning, adapting, and evolving entity that perceives the environment, processes information, acts upon it, and updates its internal states. This conceptual model can be used to build stylized representations of (classes of) individual investors, and further studied within the paradigm of agent-based artificial financial markets.

The cognitive dimensions framework provides a mechanism by which we may better understand the factors which influence human interaction with notational systems. Primarily used to inform notational design, or to evaluate programming languages, interaction environments and specification notations, the focus of the framework is in highlighting the things that make using notations for a given task 'hard' or 'easy'. We believe that the dimensions, which have their roots in cognitive psychology, may offer the foundation for a cognitive model of interaction with notations. This work examines the existing body of literature that seeks to understand the cognitive features of human interaction with notations, noting the parallels with the cognitive dimensions framework. We suggest that we may derive a cognitive model of interaction with notations through examination of the dimensions using cognition as a lens. The work provides a contribution to the cognitive dimensions framework itself, highlighting the distinction between cognitive effort and constraints arising at the notational level. It also suggests that interaction with symbols and notations may be seen as a type of embodiment, and we believe that development of a cognitive model of interaction with notations may be of use in better understanding than embodiment.

This final chapter looks back and forward. It looks back over the other chapters, to trace a 'thread' that runs through the work as a whole, which can suggest new and interesting avenues for future work. A very obvious theme argued in several chapters is that cognition is developed by interaction with the environment, especially of an embodied kind. Rather taken for granted, but no less important, is the diversity of these (embodied) interactions between the agent and its domain of application. It is diversity as such which this chapter traces as the thread that runs through the entire work, to point a way forward. Diversity may be understood by reference to Dooyeweerd's philosophical exploration of distinct spheres of meaning and law which, as aspects, enable us to differentiate types of interaction and embodiment. Plotting the chapters by these aspects reveals interesting patterns, which allow a number of suggestions to be made for future research, including focussing on under-represented aspects, providing agents with good quality meta-rules that express the distinct rationality of each aspect, and contributing to meeting a number of challenges. It concludes by suggesting that, in addition to contributions this work makes to the field of cognitive systems and the paradigm of embodied interaction, it can also contribute an understanding of diversity itself.