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.

Predictive coding and its generalization to active inference offer a unified theory of brain function. The underlying predictive processing paradigmhas gained significant attention in artificial intelligence research for its representation learning and predictive capacity. Here, we suggest that it is possible to integrate human and artificial generative models with a predictive coding network that processes sensations simultaneously with the signature of predictive coding found in human neuroimaging data. We propose a recurrent hierarchical predictive coding model that predicts low-dimensional representations of stimuli, electroencephalogram and physiological signals with variational inference. We suggest that in a shared environment, such hybrid predictive coding networks learn to incorporate the human predictive model in order to reduce prediction error. We evaluate the model on a publicly available EEG dataset of subjects watching one-minute long video excerpts. Our initial results indicate that the model can be trained to predict visual properties such as the amount, distance and motion of human subjects in videos.

The free energy principle describes cognitive functions such as perception, action, learning and attention in terms of surprisal minimisation. Under simplifying assumptions, agents are depicted as systems minimising a weighted sum of prediction errors encoding the mismatch between incoming sensations and an agent’s predictions about such sensations. The “dark room” is defined as a state that an agent would occupy should it only look to minimise this sum of prediction errors. This (paradoxical) state emerges as the contrast between the attempts to describe the richness of human and animal behaviour in terms of surprisal minimisation and the trivial solution of a dark room, where the complete lack of sensory stimuli would provide the easiest way to minimise prediction errors, i.e., to be in a perfectly predictable state of darkness with no incoming stimuli. Using a process theory derived from the free energy principle, active inference, we investigate with an agent-based model the meaning of the dark room problem and discuss some of its implications for natural and artificial systems. In this set up, we propose that the presence of this paradox is primarily due to the long-standing belief that agents should encode accurate world models, typical of traditional (computational) theories of cognition.

Insect-Inspired models of visual navigation, that operate by scanning for familiar views of the world, have been shown to be capable of robust route navigation in simulation. These familiarity-based navigation algorithms operate by training an artificial neural network (ANN) with views from a training route, so that it can then output a familiarity score for any new view. In this paper we show that such an algorithm – with all computation performed on a small low-power robot – is capable of delivering reliable direction information along real-world outdoor routes, even when scenes contain few local landmarks and have high-levels of noise (from variable lighting and terrain). Indeed, routes can be precisely recapitulated and we show that the required computation and storage does not increase with the number of training views. Thus the ANN provides a compact representation of the knowledge needed to traverse a route. In fact, rather than losing information, there are instances where the use of an ANN ameliorates the problems of sub optimal paths caused by tortuous training routes. Our results suggest the feasibility of familiarity-based navigation for long-range autonomous visual homing.

Artificial life simulations are an important tool in the study of ecological phenomena that can be difficult to examine directly in natural environments. Recent work has established the soundscape as an ecologically important resource and it has been proposed that the differentiation of animal vocalizations within a soundscape is driven by the imperative of intraspecies communication. The experiments in this paper test that hypothesis in a simulated soundscape in order to verify the feasibility of intraspecies communication as a driver of acoustic niche differentiation. The impact of intraspecies communication is found to be a significant factor in the division of a soundscape’s frequency spectrum when compared to simulations where the need to identify signals from conspecifics does not drive the evolution of signalling. The method of simulating the effects of interspecies interactions on the soundscape is positioned as a tool for developing artificial life agents that can inhabit and interact with physical ecosystems and soundscapes.

Affordances are directly perceived environmental possibilities for action. Born within ecological psychology, they have been proposed to be one of the main building blocks to explain cognition from and embodied and situated perspective. Despite the interest, a formal definition of affordances in information theory terms that would allow to exploit their full potential in models of cognitive systems is still missing. We explore the challenge of quantifying affordances by using information-theoretical measures. Specifically, we propose that empowerment (i.e., information quantifying how much influence and control an agent has over the environment it can perceive) can be used to formally capture information about the possibilities for action (the range of possible behaviors of the agent in a given environment), which in some cases can constitute affordances. We test this idea in a minimal model reproducing some aspects of a classical example of body-scaled affordances: an agent passing through an aperture. We use empowerment measures to characterize the affordance of passing through the aperture. We find out that empowerment measures yield a similar transition to the one found in experimental data in humans in the specialized literature on ecological psychology. The exercise points to some limitations for formalizing affordances and allows us to pose questions regarding how affordances can be differentiated from more generic possibilities for action.

We describe the design, implementation and on going evaluation processes of “NukaBot”, a system built to realize Human to Microbe Interaction, aimed to assist production of fermented food. Our system senses, records and analyzes in real time the fermentation process inside a “ nukadoko ”, a traditional method to produce vegetable pickles in Japan that involves a highly complex network of microbes. The flora of a nukadoko mainly consists of lactic acid bacteria, yeasts and gram-negative bacteria. The novelty of our system lies in providing an intuitive user interface that lets its user chat with a virtual persona attributed to the nukadoko. The NukaBot thus enables non-specialists to discern the complex dynamics of the microbial communities within a nukadoko in daily situations.