The self-composing brain: towards a glial-neuronal brain theory

A brain model is proposed which describes its structural organization and the related functions as compartments organized in time and space. On a molecular level the negative feedback loops of clock-controlled genes are interpreted as compartments. This spatio-temporal operational principle may also work on the cellular level as glial-neuronal interactions, wherein glia have a spatio-temporal boundary setting function. The synchronization of the multi-compartmental operations of the brain is compared to the harmonization in a symphony and appears as an integrated behavior of the whole organism, defined as modes of behavior. For explanation of the principle of harmonization, an example from Schubert's Symphony No. 8 has been chosen. While harmonization refers to the synchronization of diverse systems, it seems appropriate to select the brain of a composer and the structure of musical composition as a paradigm towards a glial-neuronal brain theory. Finally, some limitations of experimental brain research are discussed and robotics are proposed as a promising alternative.     

From cybernetics to brain theory,and more: A memoir

While structured as an autobiography, this memoir exemplifies ways in which classic contributions to cybernetics (e.g., by Wiener, McCulloch & Pitts, and von Neumann) have fed into a diversity of current research areas, including the mathematical theory of systems and computation, artificial intelligence and robotics, computational neuroscience, linguistics, and cognitive science. The challenges of brain theory receive special emphasis. Action-oriented perception and schema theory complement neural network modeling in analyzing cerebral cortex, cerebellum, hippocampus, and basal ganglia. Comparative studies of frog, rat, monkey, ape and human not only deepen insights into the human brain but also ground an EvoDevoSocio view of “how the brain got language.” The rapprochement between neuroscience and architecture provides a recent challenge. The essay also assesses some of the social and theological implications of this broad perspective.     

A real‐world rational agent: unifying old and new AI

Explanations of cognitive processes provided by traditional artificial intelligence were based on the notion of the knowledge level. This perspective has been challenged by new AI that proposes an approach based on embodied systems that interact with the real‐world. We demonstrate that these two views can be unified. Our argument is based on the assumption that knowledge level explanations can be defined in the context of Bayesian theory while the goals of new AI are captured by using a well established robot based model of learning and problem solving, called Distributed Adaptive Control (DAC). In our analysis we consider random foraging and we prove that minor modifications of the DAC architecture renders a model that is equivalent to a Bayesian analysis of this task. Subsequently, we compare this enhanced, “rational,” model to its “non‐rational” predecessor and a further control condition using both simulated and real robots, in a variety of environments. Our results show that the changes made to the DAC architecture, in order to unify the perspectives of old and new AI, also lead to a significant improvement in random foraging.     

Towards a Vygotskyan cognitive robotics: The role of language as a cognitive tool

Cognitive Robotics can be defined as the study of cognitive phenomena by their modeling in physical artifacts such as robots. This is a very lively and fascinating field which has already given fundamental contributions to our understanding of natural cognition. Nonetheless, robotics has to date addressed mainly very basic, low-level cognitive phenomena like sensory-motor coordination, perception, and navigation, and it is not clear how the current approach might scale up to explain high-level human cognition. In this paper we argue that a promising way to do that is to merge current ideas and methods of ‘embodied cognition’ with the Russian tradition of theoretical psychology which views language not only as a communication system but also as a cognitive tool, that is by developing a Vygotskyan cognitive robotics. We substantiate this idea by discussing several domains in which language can improve basic cognitive abilities and permit the development of high-level cognition: learning, categorization, abstraction, memory, voluntary control, and mental life.     

Re-framing the characteristics of concepts and their relation to learning and cognition in artificial agents

In this work, the problems of knowledge acquisition and information processing are explored in relation to the definitions of concepts and conceptual processing, and their implications for artificial agents.The discussion focuses on views of cognition as a dynamic property in which the world is actively represented in grounded mental states which only have meaning in the action context. Reasoning is understood as an emerging property consequence of actions-environment couplings achieved through experience, and concepts as situated and dynamic phenomena enabling behaviours.Re-framing the characteristics of concepts is considered crucial to overcoming settled beliefs and reinterpreting new understandings in artificial systems.The first part presents a review of concepts from cognitive sciences. Support is found for views on grounded and embodied cognition, describing concepts as dynamic, flexible, context-dependent, and distributedly coded.That is argued to contrast with many technical implementations assuming concepts as categories, whilst explains limitations when grounding amodal symbols, or in unifying learning, perception and reasoning.The characteristics of concepts are linked to methods of active inference, self-organization, and deep learning to address challenges posed and to reinterpret emerging techniques.In a second part, an architecture based on deep generative models is presented to illustrate arguments elaborated. It is evaluated in a navigation task, showing that sufficient representations are created regarding situated behaviours with no semantics imposed on data. Moreover, adequate behaviours are achieved through a dynamic integration of perception and action in a single representational domain and process.     

Value systems for developmental cognitive robotics: A survey

This paper surveys value systems for developmental cognitive robotics. A value system permits a biological brain to increase the likelihood of neural responses to selected external phenomena. Many machine learning algorithms capture the essence of this learning process. However, computational value systems aim not only to support learning, but also autonomous attention focus to direct learning. This combination of unsupervised attention focus and learning aims to address the grand challenge of autonomous mental development for machines. This survey examines existing value systems for developmental cognitive robotics in this context. We examine the definitions of value used—including recent pioneering work in intrinsic motivation as value—as well as initialisation strategies for innate values, update strategies for acquired value and the data structures used for storing value. We examine the extent to which existing value systems support attention focus, learning and prediction in an unsupervised setting. The types of robots and applications in which these value systems are used are also examined, as well as the ways that these applications are evaluated. Finally, we study the strengths and limitations of current value systems for developmental cognitive robots and conclude with a set of research challenges for this field.     

Robot studies on saccade-triggered visual prediction

Three robot studies on visual prediction are presented. In all of them, a visual forward model is used, which predicts the visual consequences of saccade-like camera movements. This forward model works by remapping visual information between the pre- and postsaccadic retinal images; at an abstract modeling level, this process is closely related to neurons whose visual receptive fields shift in anticipation of saccades. In the robot studies, predictive remapping is used (1) in the context of saccade adaptation, to reidentify target objects after saccades are carried out; (2) for a model of grasping, in which both fixated and non-fixated target objects are processed by the same foveal mechanism; and (3) in a computational architecture for mental imagery, which generates “gripper appearances” internally without real sensory inflow. The robotic experiments and their underlying computational models are discussed with regard to predictive remapping in the brain, transsaccadic memory, and attention. The results confirm that visual prediction is a mechanism that has to be considered in the design of artificial cognitive agents and the modeling of information processing in the human visual system.     

Grasping of extrafoveal targets: A robotic model

We present a computational model of grasping of non-fixated (extrafoveal) target objects which is implemented on a robot setup, consisting of a robot arm with cameras and gripper. This model is based on the premotor theory of attention (Rizzolatti et al., 1994) which states that spatial attention is a consequence of the preparation of goal-directed, spatially coded movements (especially saccadic eye movements). In our model, we add the hypothesis that saccade planning is accompanied by the prediction of the retinal images after the saccade. The foveal region of these predicted images can be used to determine the orientation and shape of objects at the target location of the attention shift. This information is necessary for precise grasping. Our model consists of a saccade controller for target fixation, a visual forward model for the prediction of retinal images, and an arm controller which generates arm postures for grasping. We compare the precision of the robotic model in different task conditions, among them grasping (1) towards fixated target objects using the actual retinal images, (2) towards non-fixated target objects using visual prediction, and (3) towards non-fixated target objects without visual prediction. The first and second setting result in good grasping performance, while the third setting causes considerable errors of the gripper orientation, demonstrating that visual prediction might be an important component of eye–hand coordination. Finally, based on the present study we argue that the use of robots is a valuable research methodology within psychology.     

The dual role of ‘emergence’ in the philosophy of mind and in cognitive science

The concept of emergence is widely used in both the philosophy of mind and in cognitive science. In the philosophy of mind it serves to refer to seemingly irreducible phenomena, in cognitive science it is often used to refer to phenomena not explicitly programmed. There is no unique concept of emergence available that serves both purposes.