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Most people tend to bisect horizontal lines slightly to the left of their true center (pseudoneglect), and start visual search from left-sided items. This physiological leftward spatial bias may depend on hemispheric asymmetries in the organization of attentional networks, but the precise mechanisms are unknown. Here we modeled relevant aspects of the ventral and dorsal attentional networks (VAN and DAN) of the human brain. First, we demonstrated pseudoneglect in visual search in 101 right-handed psychology students. Participants consistently tended to start the task from a left-sided item, thus showing pseudoneglect. Second, we trained populations of simulated neurorobots to perform a similar task, by using a genetic algorithm. The neurorobots’ behavior was controlled by artificial neural networks, which simulated the human VAN and DAN in the two brain hemispheres. Neurorobots differed in the connectional constraints that were applied to the anatomy and function of the attention networks. Results indicated that (1) neurorobots provided with a biologically plausible hemispheric asymmetry of the VAN-DAN connections, as well as with inter-hemispheric inhibition, displayed the best match with human data; however, (2) anatomical asymmetry per se was not sufficient to generate pseudoneglect; in addition, the VAN must have an excitatory influence on the ipsilateral DAN; (3) neurorobots provided with bilateral competence in the VAN but without inter-hemispheric inhibition failed to display pseudoneglect. These findings provide a proof of concept of the causal link between connectional asymmetries and pseudoneglect, and specify important biological constraints that result in physiological asymmetries of human behavior.Significance statementMost of us start our exploration of the environment from the left side. Here we demonstrated this tendency in undergraduate students, and trained artificial agents (neurorobots) to perform a similar visual search task. The neurorobots’ behavior was controlled by artificial neural networks, inspired by the human fronto-parietal attentional system. In seven distinct populations of neurorobots, different constraints were applied on the network connections within and between the brain hemispheres. Only one of the artificial populations behaved in a similar way to the human participants. The connectional constraints applied to this population included known characteristics of the human fronto-parietal networks, but had also additional properties not previously described. Thus, our findings specify biological constraints that induce physiological asymmetries of human behavior.

The field of collective robotics has been raising increasing interest in the last few years. In the vast majority of works devoted to collective robotics all interacting robots play always the same function, while less attention has been paid to groups of collaborating robots in which different robots play different roles. In this paper we evolve a population of homogeneous robots for dynamically allocating roles through communicative interactions. In particular, we focus on the development of a team of robots in which one and only one individual (the 'leader') must differentiate its communicative behaviour from that of all the others ('non-leaders'). Evolved solutions prove to be very robust with respect to changes in the size of the group. Furthermore, both behavioural analyses and a comparison with a control condition in which robots are not allowed to move demonstrate the importance of co-adapting communicative and non-communicative behaviours, and, in particular, of being allowed to dynamically change the topology of communicative interactions. Finally, we show how the same method can be used for solving other kinds of role-allocation tasks. The general idea proposed in this paper might be used in the future for evolving general, robust, and scalable role differentiation mechanisms which can be exploited to develop non-communicative collaborative behaviours that require specialisation of roles within groups of homogeneous individuals.

Educational robotics has been extensively used to teach hard skills such as computer science, computational thinking and coding because traditional robotics is the outcome of analysis, design and programming. Other approaches to robotics, namely evolutionary robotics, open the way to reflection on emergence, self-organization, dynamical systems. As these issues are relevant in present days society, we propose a robotic laboratory where children are trained to rule complex systems. In particular, the integrated hardware/software system BrainFarm, that allows to evolve and train virtual robots and then test them in physical environments, is employed to train these skills and a successful experience in informal context is described.

Purpose – This paper aims to describe an integrated hardware/software system based on evolutionary robotics and its application in an edutainment context. Design/methodology/approach – The system is based on a wide variety of artificial life techniques (artificial neural networks, genetic algorithms, user-guided evolutionary design and evolutionary robotics). A user without any computer programming skill can determine the robot's behavior in two different ways: artificial breeding or artificial evolution. Breedbot has been used as a didactic tool in teaching evolutionary biology and as a “futuristic” toy by several science centers. The digital side of Breedbot can be downloaded on the web site: www.isl.unina.it/breedbot Findings – The results in this pilot study suggest that using Breedbot in an educational context can be useful to improve learning in biology. Research limitations/implications – As this is a pilot study, one limitation is the small sample considered. The issue will be investigated further with a wider population and subject-matter, which will also improve the Breedbot system. Practical implications – These results suggest that tools like Breedbot could be introduced into biology curricula at schools. Originality/value – The paper describes an original application in digital content and shows the importance of using such a tool in an Edutainment context. It is therefore interesting for teachers, vocational trainers and anyone involved in educational activities.

Mainstream approaches to modelling cognitive processes have typically focused on (1) reproducing their neural underpinning, without regard to sensory-motor systems and (2) producing a single, ideal computational model. Evolutionary robotics is an alternative possibility to bridge the gap between neural substrate and behavior by means of a sensory-motor apparatus, and a powerful tool to build a population of individuals rather than a single model. We trained 4 populations of neurorobots, equipped with a pan/tilt/zoom camera, and provided with different types of motor control in order to perform a cancellation task, often used to tap spatial cognition. Neurorobots’ eye movements were controlled by (a) position, (b) velocity, (c) simulated muscles and (d) simulated muscles with fixed level of zoom. Neurorobots provided with muscle and velocity control showed better performances than those controlled in position. This is an interesting result since muscle control can be considered a particular type of position control. Finally, neurorobots provided with muscle control and zoom outperformed those without zooming ability.

The core principles of the evolutionary theories of emotions declare that affective states represent crucial drives for action selection in the environment and regulated the behavior and adaptation of natural agents in ancestrally recurrent situations. While many different studies used autonomous artificial agents to simulate emotional responses and the way these patterns can affect decision-making, few are the approaches that tried to analyze the evolutionary emergence of affective behaviors directly from the specific adaptive problems posed by the ancestral environment. A model of the evolution of affective behaviors is presented using simulated artificial agents equipped with neural networks and physically inspired on the architecture of the iCub humanoid robot. We use genetic algorithms to train populations of virtual robots across generations, and investigate the spontaneous emergence of basic emotional behaviors in different experimental conditions. In particular, we focus on studying the emotion of fear, therefore the environment explored by the artificial agents can contain stimuli that are safe or dangerous to pick. The simulated task is based on classical conditioning and the agents are asked to learn a strategy to recognize whether the environment is safe or represents a threat to their lives and select the correct action to perform in absence of any visual cues. The simulated agents have special input units in their neural structure whose activation keep track of their actual “sensations” based on the outcome of past behavior. We train five different neural network architectures and then test the best ranked individuals comparing their performances and analyzing the unit activations in each individual’s life cycle. We show that the agents, regardless of the presence of recurrent connections, spontaneously evolved the ability to cope with potentially dangerous environment by collecting information about the environment and then switching their behavior to a genetically selected pattern in order to maximize the possible reward. We also prove the determinant presence of an internal time perception unit for the robots to achieve the highest performance and survivability across all conditions.

We introduce FARSA, an open-source Framework for Autonomous Robotics Simulation and Analysis, that allows us to easily set up and carry on adaptive experiments involving complex robot/environmental models. Moreover, we show how a simulated iCub robot can be trained, through an evolutionary algorithm, to display reaching and integrated reaching and grasping behaviours. The results demonstrate how the use of an implicit selection criterion, estimating the extent to which the robot is able to produce the expected outcome without specifying the manner through which the action should be realized, is sufficient to develop the required capabilities despite the complexity of the robot and of the task. © The Author(s) 2014.

This article presents results from a multidisciplinary research project on the integration and transfer of language knowledge into robots as an empirical paradigm for the study of language development in both humans and humanoid robots. Within the framework of human linguistic and cognitive development, we focus on how three central types of learning interact and co-develop: individual learning about one's own embodiment and the environment, social learning (learning from others), and learning of linguistic capability. Our primary concern is how these capabilities can scaffold each other's development in a continuous feedback cycle as their interactions yield increasingly sophisticated competencies in the agent's capacity to interact with others and manipulate its world. Experimental results are summarized in relation to milestones in human linguistic and cognitive development and show that the mutual scaffolding of social learning, individual learning, and linguistic capabilities creates the context, conditions, and requisites for learning in each domain. Challenges and insights identified as a result of this research program are discussed with regard to possible and actual contributions to cognitive science and language ontogeny. In conclusion, directions for future work are suggested that continue to develop this approach toward an integrated framework for understanding these mutually scaffolding processes as a basis for language development in humans and robots.

We show how simulated robots evolved for the ability to display a context-dependent periodic behavior can spontaneously develop an internal model and rely on it to fulfill their task when sensory stimulation is temporarily unavailable. The analysis of some of the best evolved agents indicates that their internal model operates by anticipating sensory stimuli. More precisely, it anticipates functional properties of the next sensory state rather than the exact state that sensors will assume. The characteristics of the states that are anticipated and of the sensorimotor rules that determine how the agents react to the experienced states, however, ensure that they produce very similar behaviour during normal and blind phases in which sensory stimulation is available or is self-generated by the agent, respectively. Agents' internal models also ensure an effective transition during the phases in which agents' internal dynamics is decoupled and re-coupled with the sensorimotor flow. Our results suggest that internal models might have arisen for behavioral reasons and successively exapted for other cognitive functions. Moreover, the obtained results suggest that self-generated internal states should not necessarily match in detail the corresponding sensory states and might rather encode more abstract and motor-oriented information.

The field of collective robotics has been raising increasing interest in the last few years. In the vast majority of works devoted to collective robotics robots play all the same function, while less attention has been paid to groups of robots with different roles (teams). In this paper we evolve a population of homogeneous robots for dynamically allocating roles through bodily and communicative interactions. Evolved solutions are not only able to efficiently decide who is the leader, but are also robust to changes in team’s size, demonstrating the ability of Evolutionary Robotics to find efficient and robust solution to difficult design challenges by relying on self-organizing principles. Our evolved system might be used for improving robots’ performance in all the cases in which robots have to accomplish collective tasks for which the presence of a leader might be useful.

In the 1930s and 1940s, Edward Tolman developed a psychological theory of spatial orientation in rats and humans. He expressed his theory as an automaton (the ‘‘schematic sowbug’’) or what today we would call an ‘‘artificial organism.’’ With the technology of the day, he could not implement his model. Nonetheless, he used it to develop empirical predictions which tested with animals in the laboratory. This way of proceeding was in line with scientific practice dating back to Galileo. The way psychologists use artificial organisms in their work today breaks with this tradition. Modern ‘‘artificial organisms’’ are constructed a posteriori, working from experimental or ethological observations. As a result, researchers can use them to confirm a theoretical model or to simulate its operation. But they make no contribution to the actual building of models. In this paper, we try to return to Tolman’s original strategy: implementing his theory of ‘‘vicarious trial and error’’ in a simulated robot, forecasting the robot’s behavior and conducting experiments that verify or falsify these predictions.