Sensing, communication, navigation, decision-making, memory and learning are key components in a standard cognitive tool-kit that enhance an animal\'s ability to successfully survive and reproduce. However, these tools are not only useful for, or accessible to, animals-they evolved long ago in simpler organisms using mechanisms which may be either unique or widely conserved across diverse taxa. In this article, I review the recent research that demonstrates these key cognitive abilities in the plasmodial slime mould Physarum polycephalum, which has emerged as a model for non-animal cognition. I discuss the benefits and limitations of comparisons drawn between neural and non-neural systems, and the implications of common mechanisms across wide taxonomic divisions. I conclude by discussing future avenues of research that will draw the most benefit from a closer integration of Physarum and animal cognition research.

\"Morphological computation\" is an increasingly important concept in robotics, artificial intelligence, and philosophy of the mind. It is used to understand how the body contributes to cognition and control of behavior. Its understanding in terms of \"offloading\" computation from the brain to the body has been criticized as misleading, and it has been suggested that the use of the concept conflates three classes of distinct processes. In fact, these criticisms implicitly hang on accepting a semantic definition of what constitutes computation. Here, I argue that an alternative, mechanistic view on computation offers a significantly different understanding of what morphological computation is. These theoretical considerations are then used to analyze the existing research program in developmental biology, which understands morphogenesis, the process of development of shape in biological systems, as a computational process. This important line of research shows that cognition and intelligence can be found across all scales of life, as the proponents of the basal cognition research program propose. Hence, clarifying the connection between morphological computation and morphogenesis allows for strengthening the role of the former concept in this emerging research field.

We present a description of an ASM-network, a new habit-based robot controller model consisting of a network of adaptive sensorimotor maps. This model draws upon recent theoretical developments in enactive cognition concerning habit and agency at the sensorimotor level. It aims to provide a platform for experimental investigation into the relationship between networked organizations of habits and cognitive behavior. It does this by combining (1) a basic mechanism of generating continuous motor activity as a function of historical sensorimotor trajectories with (2) an evaluative mechanism which reinforces or weakens those historical trajectories as a function of their support of a higher-order structure of higher-order sensorimotor coordinations. After describing the model, we then present the results of applying this model in the context of a well-known minimal cognition task involving object discrimination. In our version of this experiment, an individual robot is able to learn the task through a combination of exploration through random movements and repetition of historic trajectories which support the structure of a pre-given network of sensorimotor coordinations. The experimental results illustrate how, utilizing enactive principles, a robot can display recognizable learning behavior without explicit representational mechanisms or extraneous fitness variables. Instead, our model\'s behavior adapts according to the internal requirements of the action-generating mechanism itself.

Although machines may be good at mimicking, they are not currently able, as organisms are, to act creatively. We offer an understanding of the emergent qualities of biological sign processing in terms of generalization, association, and encryption. We use slime mold as a model of minimal cognition and compare it to deep-learning video game bots, which some claim have evolved beyond their merely quantitative algorithms. We find that these discrete Turing machine bots are not able to make productive, yet unanticipated, \"errors\"-necessary for biological learning-which, based on the physicality of signs, their relatively similar shapes, and relative physical positions spatially and temporally, lead to emergent effects and make learning and evolution possible. In organisms, stochastic resonance at the local level can be leveraged for self-organization at the global level. We contrast all this to the symbolic processing of today\'s machine learning, whereby each logic node and memory state is discrete. Computer codes are produced by external operators, whereas biological symbols are evolved through an internal encryption process.

Bioengineered neural tissues help advance our understanding of neurodevelopment, regeneration, and neural disease; however, it remains unclear whether they can replicate higher-order functions including cognition. Building upon technical achievements in the fields of biomaterials, tissue engineering, and cell biology, investigators have generated an assortment of artificial brain structures and cocultured circuits. Though they have displayed basic electrochemical signaling, their capacities to generate minimal patterns of information processing suggestive of high-order cognitive analogues have not yet been explored. Here, we review the current state of neural tissue engineering and consider the possibility of a study of cognition in vitro. We adopt a practical definition of minimal cognition, anticipate problems of measurement, and discuss solutions toward a study of cognition in a dish.

In this comment to Ocklenburg et al.\'s paper I stressed the contribution that computational ethology can provide to the accurate tracking of lateralized behaviour in a variety of species; I also discussed how current interest in so-called «minimal cognition» may help to disentangle shared and species-specific mechanisms of brain and behavioural asymmetries.

The aim of this article is to investigate the relevance and implications of synthetic models for the study of the interactive dimension of minimal life and cognition, by taking into consideration how the use of artificial systems may contribute to an understanding of the way in which interactions may affect or even contribute to shape biological identities. To do so, this article analyzes experimental work in synthetic biology on different types of interactions between artificial and natural systems, more specifically: between protocells and between biological living cells and protocells. It discusses how concepts such as control, cognition, communication can be used to characterize these interactions from a theoretical point of view, which criteria of relevance and evaluation of synthetic models can be applied to these cases, and what are their limits.

We look at a recent expansion of Physarum research from inspiring biomimetic algorithms to serving as a model organism in the evolutionary study of perception, memory, learning, and decision making.

In some evolutionary robotics experiments, evolved robots are transferred from simulation to reality, while sensor/motor data flows back from reality to improve the next transferral. We envision a generalization of this approach: a simulation-to-reality pipeline. In this pipeline, increasingly embodied agents flow up through a sequence of increasingly physically realistic simulators, while data flows back down to improve the next transferral between neighboring simulators; physical reality is the last link in this chain. As a first proof of concept, we introduce a two-link chain: A fast yet low-fidelity ( lo-fi) simulator hosts minimally embodied agents, which gradually evolve controllers and morphologies to colonize a slow yet high-fidelity ( hi-fi) simulator. The agents are thus physically scaffolded. We show here that, given the same computational budget, these physically scaffolded robots reach higher performance in the hi-fi simulator than do robots that only evolve in the hi-fi simulator, but only for a sufficiently difficult task. These results suggest that a simulation-to-reality pipeline may strike a good balance between accelerating evolution in simulation while anchoring the results in reality, free the investigator from having to prespecify the robot\'s morphology, and pave the way to scalable, automated, robot-generating systems.

The study of evolutionary patterns of cognitive convergence would be greatly helped by a clear demarcation of cognition. Cognition is often used as an equivalent of mind, making it difficult to pin down empirically or to apply it confidently beyond the human condition. Recent developments in embodied cognition and philosophy of biology now suggest an interpretation that dissociates cognition from this mental context. Instead, it anchors cognition in a broad range of biological cases of intelligence, provisionally marked by a basic cognitive toolkit. This conception of cognition as an empirically based phenomenon provides a suitable and greatly expanded domain for studies of evolutionary convergence. This paper first introduces this wide, biologically embodied interpretation of cognition. Second, it discusses examples drawn from studies on bacteria, plants and fungi that all provide cases fulfilling the criteria for this wide interpretation. Third, the field of early nervous system evolution is used to illustrate how biologically embodied cognition raises new fundamental questions for research on animal cognition. Finally, an outline is given of the implications for the evolutionary convergence of cognition.