Collective biological systems can exhibit surprisingly rich macroscopic adaptive behaviors from their relatively simple components. These behaviors often resemble sensing, cognition, and ecological interaction, as in higher-order organisms. Unfortunately, a normative model explaining cognitive behaviors from the interactions of simple elements is not yet available. We hypothesize here that minimal self-knowledge can be observed from local interactions between simple components if two conditions are given: (i) self-generated, globally shared information and (ii) higher-order feedback loops emerging from the actions of simple elements. To evaluate the hypothesis, a discrete-time, discrete-space computational simulation is developed, modeling simple particles discharging a chemical-like substance (pheromone). The results indicate that unstructured variations introduced through random movements of particles across the arena enabled the formation of clusters with complex, emergent behaviors. Under the hypothesized conditions, these results indicate that a minimal form of self-knowledge emerges, representing a primitive self-definition.

Walter Veit’s A Philosophy for the Science of Animal Consciousness proposes a naturalistic and evolutionarily grounded account of consciousness that positions subjectivity as a biological adaptation to ecological complexity. In this commentary, I highlight the book’s potential relevance for animal welfare science, where understanding species-specific affective experiences is central. Veit’s concept of pathological complexity, the adaptive challenge of managing life-history trade-offs, provides a compelling functional rationale for the evolution of consciousness. This framework has important implications for both comparative cognition and welfare science, encouraging us to consider consciousness as a gradual and multidimensional phenomenon shaped by ecological demands. While the book’s theoretical depth may require careful reading for those outside philosophy or evolutionary biology, Veit’s openness to critique, and his use of rich biological examples make the work an important contribution. By emphasising evaluative richness as a core dimension of consciousness, Veit lays the groundwork for an empirical bridge between consciousness studies and animal welfare research. I argue that integrating his framework into welfare science could help refine our methods for assessing animal affect and agency. If consciousness evolved to guide living well, then investigating how animals thrive is not only ethically meaningful but also conceptually essential to understanding consciousness itself.

Facing stress and producing stress responses are crucial aspects of an organism’s life and the evolution of both its species and of the other species in its environment, which are co-evolving with it. Philosophers and biologists emphasize the importance of environmental complexity and how organisms deal with it in evolution of cognitive processes. This article adds to these discussions by highlighting the importance of stress physiology in processes connected to plant cognition. While this article supports the thesis that life means cognizing (i.e., sensing the environment, arranging internal processes according to that perception, and affecting the environment with its actions), it also emphasizes that there are various kinds of organisms. In this regard, plant cognition is not animal cognition. However, given both the variety and continuity in evolutionary processes and the similarities even between the distantly related organisms in the tree of life, I argue that it is usually useful to consider and compare physiological and molecular mechanisms in plants and animals as well as the concepts and research processes in animal and plant science. Although the “pathological complexity” thesis that Veit (2023) presents is fruitful in considering the evolution of consciousness and cognition, I argue that, when thinking of biological processes in relation to cognition, stress can be a helpful concept (maybe even as suitable as pathological complexity) in thinking of organisms’ responses to environmental complexity and their adaptation and acclimation processes.

In his book “ A Philosophy for the Science of Animal Consciousness ,” Walter Veit attempts to provide a philosophical foundation for the science of animal consciousness, especially focusing on hedonic evaluation. While he highlights an important aspect of phenomenal consciousness, several conceptual issues warrant further examination. The first one is the role of evaluation. Veit argues that hedonic evaluation explains the origin of consciousness, but it seems implausible for conscious animals to have the evaluative aspect alone. Instead, the origin(s) of consciousness may have linked to the capacity for making prediction that combine both perceptive and evaluative aspects. Second, it is necessary to distinguish between creature and state consciousness for discussing subjective unity. Autonomous movements of octopus arms do not necessarily imply multiple conscious subjects within one body. Alternatively, an octopus may experiences “switching” within a single phenomenal field between conflicting perceptions as in binocular rivalry. Lastly, the adaptivity of consciousness requires scrutiny. Just as different animal phyla show various body plans, the neuroarchitecture of consciousness may be implemented in different ways among animals. Specific mentalities may be fine-tuned to specific lifestyles, similarly to specific adaptation of animal bodies. It appears that the “adaptivity” of ground plans (e.g., vertebrate body and vertebrate consciousness) makes no sense or must be considered differently from the adaptivity of specific character states (e.g., the body shape and food preferences of a bat). The current analysis could help improve Veit’s proposal, linking it to previous accounts and establishing a solid philosophical foundation for this research field.

External representations ( ERs) are objects or performances in the world whose proper function is to communicate about other things in the world. Why and how did we make them, and what do they give us? We outline a simple framework for thinking about ERs grounded in modern machine learning. We propose a minimal set of neural mechanisms needed for open-ended ER production. Our constructivist enactive view contrasts with nativist views which propose specialist neural modules requiring symbolic internal representations. We propose a plausible set of (biological and cultural) evolutionary steps to full Gricean symbolic communication via a set of increasingly complex enactive algorithms for ER production. A pragmatic space of games is defined, which includes not only fully cooperative language games but also science, art, and evolved signal manipulation games. This space is defined by the complexity of learning needed by sender and receiver. We propose that one important step towards open-ended ER use was selection for bush reading, which like mind-reading is an inferential process requiring complex contextual and syntactic understanding of cues about events displaced in space and time. Bush reading pre-adapted receivers to be receptive, competent, and perspicacious interpreters of later intentionally produced signals about hidden topics such as felt mental states. This paved the way for minimally Gricean communication, which subsequently could be bootstrapped into explicit theories of mind, folk psychology narratives, and symbolic language in general. Recent findings in cognitive archaeology are integrated within the framework, and new experiments in machine learning suggested.

Many theoretical studies defend the existence of ongoing phase transitions in the brain dynamics that could explain its enormous plasticity to cope with the environment. However, tackling the ever-changing landscapes of brain dynamics seems a hopeless task with complex models. This paper uses a simple Haken-Kelso-Bunz (HKB) model to illustrate how phase transitions that change the number of attractors in the landscape for the relative phase between two neural assemblies can occur, helping to explain a qualitative agreement with empirical decision-making measures. Additionally, the paper discusses the possibility of interpreting this agreement with the aid of Irruption Theory (IT). Being the effect of symmetry breakings and the emergence of non-linearities in the fundamental equations, the order parameter governing phase transitions may not have a complete microscopic determination. Hence, many requirements of IT, particularly the Participation Criterion, could be fulfilled by the HKB model and its extensions. Briefly stated, triggering phase transitions in the brain activity could thus be conceived of as a consequence of actual motivations or free will participating in decision-making processes.

Large Language Models (LLMs) are capable of displaying a wide range of abilities that are not directly connected with the task for which they are trained: predicting the next words of human-written texts. In this article, I review recent research investigating the cognitive abilities developed by LLMs and their relation to human cognition. I discuss the nature of the indirect process that leads to the acquisition of these cognitive abilities, their relation to other indirect processes, and the implications for the acquisition of integrated abilities. Moreover, I propose the factors that enable the development of abilities that are related only very indirectly to the proximal objective of the training task. Finally, I discuss whether the full set of capabilities that LLMs could possibly develop is predictable.

While organisms are continually experiencing and interacting with their environments, the role and extent of experiences in behavioral development has been controversial. Some argue that adaptive behaviors are acquired through experiences, while others claim they are the result of innate programs that don’t require environmental input. Such controversies have historically occurred within animal behavior and psychology, but similar debates are emerging in the field of artificial intelligence. Here, the debate is centered on those who design experience-dependent systems that are trained to learn the statistical properties of “environmental” inputs, and those advocating the use of pre-packaged artificially “innate” responses tailored to prespecified inputs. Those favoring artificial innateness draw analogies with animal behavior to argue that innateness is necessary for the emergence of complex adaptive behavior. But does behavioral development in animals reflect the unfolding of innate programs? Here we highlight the widespread role of specifically causal experiences in the ontogeny of species-typical behaviors. All behaviors are an outcome of a chain of organism-environment transactions—called ontogenetic niches—that begin in the earliest periods of life. This challenges the notion that organisms come prepared with innate programs for behavior. We suggest that an artificial intelligence that matches the complexity of animal behavior should be based on principles of behavioral development, where experiences are necessary and specifically causal factors in the emergence of behavioral abilities.

Any odor cue can be traced to find its release source. So-called “source localization” has been observed in animals in many important tasks including finding food or mates. In particular, the scientific community for a long time focused on unraveling the complex behavior of moths while in pursuit of sex pheromones emitted by their distant female counterpart. These studies have provided many insights including details of the flight paths, sensory organs, and pheromone processing. In turn, this knowledge has provided inspiration to engineers and researchers to devise source-seeking algorithms, whereas sensory organs/-mechanisms led to insect-machine hybrid systems. Therefore, this review revolves around these last two approaches specifically (1) the implementation of moth-inspired algorithms in robotic platforms and the (2) use of biosensors such as antennae or insect-machine hybrid systems.