Basic emotions and adaptation. A computational and evolutionary model

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

A computational model of the evolution of antipredator behavior in situations with temporal variation of danger using simulated robots

The threat-sensitive predator avoidance hypothesis states that preys are able to assess the level of danger of the environment by using direct and in-direct predator cues. The existence of a neural system which determines this ability has been studied in many animal species like minnows, mosquitoes and wood frogs. What is still under debate is the role of evolution and learning for the emergence of this assessment system. We propose a bio-inspired computing model of how risk management can arise as a result of both factors and prove its impact on fitness in simulated robotic agents equipped with recurrent neural networks and evolved with genetic algorithm. The agents are trained and tested in environments with different level of danger and their performances are ana-lyzed and compared

Behavioral Restriction Determines Left Attentional Bias: Preliminary Evidences From COVID-19 Lockdown

During the COVID-19 lockdown, individuals were forced to remain at home, hence severely limiting the interaction within environmental stimuli, reducing the cognitive load placed on spatial competences. The effects of the behavioral restriction on cognition have been little examined. The present study is aimed at analyzing the effects of lockdown on executive function prominently involved in adapting behavior to new environmental demands. We analyze non-verbal fluency abilities, as indirectly providing a measure of cognitive flexibility to react to spatial changes. Sixteen students (mean age 20.75; SD 1.34), evaluated before the start of the lockdown (T1) in a battery of psychological tasks exploring different cognitive domains, have been reassessed during lockdown (T2). The assessment included the modified Five-Point Test (m-FPT) to analyze non-verbal fluency abilities. At T2, the students were also administered the Toronto Alexithymia Scale (TAS-20). The restriction of behaviors following a lockdown determines increased non-verbal fluency, evidenced by the significant increase of the number of new drawings. We found worsened verbal span, while phonemic verbal fluency remained unchanged. Interestingly, we observed a significant tendency to use the left part of each box in the m-FPT correlated with TAS-20 and with the subscales that assess difficulty in describing and identifying feelings. Although our data were collected from a small sample, they evidence that the restriction of behaviors determines a leftward bias, suggesting a greater activation of the right hemisphere, intrinsically connected with the processing of non-verbal information and with the need to manage an emotional situation

Designing for human–agent collectives: display considerations

The adoption of unmanned systems is growing at a steady rate, with the promise of improved task effectiveness and decreased costs associated with an increasing multitude of operations. The added flexibility that could potentially enable a single operator to control multiple unmanned platforms is thus viewed as a potential game-changer in terms of both cost and effectiveness. The use of advanced technologies that facilitate the control of multiple systems must lie within control frameworks that allow the delegation of authority between the human and the machine(s). Agent-based systems have been used across different domains in order to offer support to human operators, either as a form of decision support offered to the human or to directly carry out behaviours that lead to the achievement of a defined goal. This paper discusses the need for adopting a human–agent interaction paradigm in order to facilitate an effective human–agent partnership. An example of this is discussed, in which a single human operator may supervise and control multiple unmanned platforms within an emergency response scenario

DOMENICO PARISI, IL CERCHIO E ALTRE FIGURE GEOMETRICHE

In his office at the Institute of Cognitive Sciences and Technologies, Domenico Parisi sat in front of a densely written whiteboard. It was crowded with different geometric figures, mainly circles. In the visual dictionary of Domenico, circles represent divers things: agents, neurons, environments and more. With a pinch of imagination, it was not difficult to see an entire world behind those figures. A simulated (micro) world designed and developed to explain interesting phenomena of its real counterpart. In this paper, I try to describe how Domenico’s ideas influenced and still influence, through his precious scientific heritage, my research work devoted to building up (micro) worlds to better understand psychological phenomena

Un modello simulato dell'Onisco d Tolman.

Bellaria-Rimini

La coccinella nel castello. Una versione videogioco del labirinto radiale per lo studio delle abilità spaziali

Spatial exploration requires a synergy of different cognitive abilities. The assessment of declarative and procedural competencies allows to design and schedule effective intervention strategies addressing the recovery of specific impaired cognitive domains. In recent years, digital technologies have been widely used to implement psychological assessment tools. Those tools provide an effective way of collecting data and, when implemented as serious games, engaging playgrounds. In this paper, we present a pilot videogame version of the Radial Arm Maze, a well known visuospatial task. Developed for children, this videogame allows to extract the classic RAM parameters (i.e. errors, time, type of angle) and it can be also used as a training system to promote spatial learning processe