Building and Understanding the Minimal Self

Within the methodologically diverse interdisciplinary research on the minimal self, we identify two movements with seemingly disparate research agendas – cognitive science and cognitive (developmental) robotics. Cognitive science, on the one hand, devises rather abstract models which can predict and explain human experimental data related to the minimal self. Incorporating the established models of cognitive science and ideas from artificial intelligence, cognitive robotics, on the other hand, aims to build embodied learning machines capable of developing a self “from scratch” similar to human infants. The epistemic promise of the latter approach is that, at some point, robotic models can serve as a testbed for directly investigating the mechanisms that lead to the emergence of the minimal self. While both approaches can be productive for creating causal mechanistic models of the minimal self, we argue that building a minimal self is different from understanding the human minimal self. Thus, one should be cautious when drawing conclusions about the human minimal self based on robotic model implementations and vice versa. We further point out that incorporating constraints arising from different levels of analysis will be crucial for creating models that can predict, generate, and causally explain behavior in the real world.

The Radically Embodied Conscious Cybernetic Bayesian Brain: From Free Energy to Free Will and Back Again

Drawing from both enactivist and cognitivist perspectives on mind, I propose that explaining teleological phenomena may require reappraising both “Cartesian theaters” and mental homunculi in terms of embodied self-models (ESMs), understood as body maps with agentic properties, functioning as predictive-memory systems and cybernetic controllers. Quasi-homuncular ESMs are suggested to constitute a major organizing principle for neural architectures due to their initial and ongoing significance for solutions to inference problems in cognitive (and affective) development. Embodied experiences provide foundational lessons in learning curriculums in which agents explore increasingly challenging problem spaces, so answering an unresolved question in Bayesian cognitive science: what are biologically plausible mechanisms for equipping learners with sufficiently powerful inductive biases to adequately constrain inference spaces? Drawing on models from neurophysiology, psychology, and developmental robotics, I describe how embodiment provides fundamental sources of empirical priors (as reliably learnable posterior expectations). If ESMs play this kind of foundational role in cognitive development, then bidirectional linkages will be found between all sensory modalities and frontal-parietal control hierarchies, so infusing all senses with somatic-motoric properties, thereby structuring all perception by relevant affordances, so solving frame problems for embodied agents. Drawing upon the Free Energy Principle and Active Inference framework, I describe a particular mechanism for intentional action selection via consciously imagined (and explicitly represented) goal realization, where contrasts between desired and present states influence ongoing policy selection via predictive coding mechanisms and backward-chained imaginings (as self-realizing predictions). This embodied developmental legacy suggests a mechanism by which imaginings can be intentionally shaped by (internalized) partially-expressed motor acts, so providing means of agentic control for attention, working memory, imagination, and behavior. I further describe the nature(s) of mental causation and self-control, and also provide an account of readiness potentials in Libet paradigms wherein conscious intentions shape causal streams leading to enaction. Finally, I provide neurophenomenological handlings of prototypical qualia including pleasure, pain, and desire in terms of self-annihilating free energy gradients via quasi-synesthetic interoceptive active inference. In brief, this manuscript is intended to illustrate how radically embodied minds may create foundations for intelligence (as capacity for learning and inference), consciousness (as somatically-grounded self-world modeling), and will (as deployment of predictive models for enacting valued goals).

Epi: An open humanoid platform for developmental robotics

Epi is a humanoid robot developed by Lund University Cognitive Science Robotics Group. It was designed to be used in experiments in developmental robotics and has proportions to give a childlike impression while still being decidedly robotic. The robot head has two degrees of freedom in the neck and each eye can independently move laterally. There is a camera in each eye to make stereovision possible. The arms are designed to resemble those of a human. Each arm has five degrees of freedom, three in the shoulder, one in the elbow and one in the wrist. The hands have four movable fingers and a stationary thumb. A force distribution mechanism inside the hand connect a single servo to the movable fingers and makes sure the hand closes around an object regardless of its shape. The rigid parts of the hands are 3D printed in PLA and HIPS while the flexible parts, including the joints and the tendons, are made from polyurethane rubber. The control system for Epi is based on neurophysiological data and is implemented using the Ikaros system. Most of the sensory and motor processing is done at 40 Hz to allow smooth movements. The irises of the eyes can change colour and the pupils can dilate and contract. There is also a grid of LEDs that resembles a mouth that can be animated by changing colour and intensity.

The radically embodied conscious cybernetic Bayesian brain: Towards explaining the emergence of agency

This manuscript attempts to characterize a broad range of intentional phenomena in terms of embodied self-models (ESMs), understood as body maps with agentic properties, functioning as predictive-memory systems and cybernetic controllers. ESMs may constitute a dominant organizing principle for neural architectures due to their initial and ongoing significance for the processes by which inference problems are solved in cognitive (and affective) development. Specifically, embodied experiences may provide a source of foundational lessons in learning curriculums in which agents explore increasingly challenging inference spaces along zones of proximal development, so helping to solve an unresolved problem in Bayesian cognitive science: what are biologically plausible mechanisms for equipping learners with sufficiently constraining/empowering inductive biases? Drawing on models from neurophysiology, psychology, and developmental robotics, I suggest a potentially surprising answer to how this problem might be solved: body maps are the primary source of (empirical) priors, or very reliably learnable posterior expectations. If ESMs play this kind of foundational role in bootstrapping cognitive development, then we ought to expect bidirectional linkages between all sensory modalities and frontal-parietal control hierarchies, so infusing all senses with somatic-motoric properties, thereby structuring all perception by relevant affordances, so solving frame problems for embodied learners/agents.

Detection of sensorimotor contingencies in infants before the age of one year: a comprehensive review

In order to benefit from the exploration of their body and their physical and social environment, infants need to detect sensorimotor contingencies linking their actions to sensory feedback. This ability, which seems to be present in babies from birth and even in utero, has been widely used by researchers in their study of early development. However, a careful look at the literature, particularly recent literature, suggests that babies may not be uniformly sensitive to all sensorimotor contingencies. This literature review examines in detail the mechanism of sensorimotor contingency detection in infants before the age of one year. Four aspects of sensorimotor contingency detection are considered: characteristics of action and feedback, contingency parameters, exposure conditions, and inter-individual differences. For each topic we highlight what favours and what hinders the detection of sensorimotor contingencies in infants. Our review also demonstrates the limitations of our knowledge about sensorimotor contingency detection. We advocate the importance of making progress in this field at a time when sensorimotor contingency detection is of major interest in developmental robotics and artificial intelligence.