A firm cognitive subject or “individual” is presupposed for the emergence of mind. However, with the development of recent information technology, the “individual” has become more dispersed in society and the cognitive subject has become increasingly unstable and adaptive, necessitating an update in our understanding of the individual”. Autopoiesis serves as a model of the cognitive subject, which is unstable and requires effort to maintain itself to adapt to the environment. In this study, we evaluated adaptivity for a highly extensible multi-particle system model Particle Lenia through the response perturbation. As a result, we found that Particle Lenia has a particle configuration that is both temporally unstable and has multiple stable states. This result suggests that Particle Lenia can express adaptive characteristics and is expected to be used as a computational model toward building an autopoietic cognitive agent.

Chemotaxis is a phenomenon whereby organisms like ameba direct their movements responding to their environmental gradients, often called gradient climbing. It is considered to be the origin of self-movement that characterizes life forms. In this work, we have simulated the gradient climbing behaviour on Neural Cellular Automata (NCA) that has recently been proposed as a model to simulate morphogenesis. NCA is a cellular automata model using deep networks for its learnable update rule and it generates a target cell pattern from a single cell through local interactions among cells. Our model, Gradient Climbing Neural Cellular Automata (GCNCA), has an additional feature that enables itself to move a generated pattern by responding to a gradient injected into its cell states.

To investigate how embodied sensorimotor interactions shape subjective visual experience, we developed a novel naturalistic Virtual Reality setting combined with motion tracking that allow object interactions with a high degree of freedom, which we implemented within an adapted breaking continuous flash suppression (bCFS) paradigm. This setup allowed us to manipulate the sensorimotor contingencies governing interactions with virtual objects, while characterising the effects on subjective visual experience by measuring breakthrough time to awareness of the virtual objects. We found that breakthrough times were faster for live compared to replayed sensorimotor interactions, demonstrating that visual awareness for unfamiliar 3D virtual objects is influenced by the contingency of the dynamic causal coupling between a person’s actions and their visual consequences, in line with theories of perception that emphasise the influence of sensorimotor contingencies on visual experience.

Simulating phenomenological aspects of altered states of consciousness provides an important experimental tool for consciousness science and psychiatry. Here we describe the Hallucination Machine, which comprises a novel combination of two powerful technologies: deep convolutional neural networks (DCNNs) and panoramic videos of natural scenes, viewed immersively through a head-mounted display. The Hallucination Machine enables the simulation of visual hallucinatory experiences in a biologically plausible and ecologically valid way. We show that the system induces visual phenomenology qualitatively similar to classical psychedelics. The Hallucination Machine offers a valuable new technique for simulating altered phenomenology without directly altering the underlying neurophysiology.