E-TAN, a technology-enhanced platform with tangible objects for the assessment of visual neglect: A multiple single-case study.

Visual neglect is a frequent and disabling consequence of right brain damage. Traditional paper-and pencil tests of neglect have limited sensitivity and ecological validity. The Baking Tray Task (BTT), instead, approaches real-life situations, because it requires participants to place 16 physical objects on a board. The number of objects placed on the left and right portions of the board provides a clinical index of visual neglect. Here we present E-TAN, a technology-enhanced platform for BTT (E-BTT). E-BTT automatically determines the object locations on the board, and also records the sequence and timing of their placement. We used E-BTT to test 9 patients with right hemisphere damage and compared their performance with that obtained by 115 healthy participants. To this end, we developed a new method of analysis of participants' performance, based on the use of the convex hull described by the objects on the board. This measure provides an estimate of the portion of space processed by each participant and can effectively discriminate neglect patients from patients without neglect. E-TAN allows clinicians to assess visuospatial performance by using a convenient, fast, and relatively automatized procedure, that patients can even perform at home to follow-up the effects of rehabilitation.

Neuromodelling based on evolutionary robotics: on the importance of motor control for spatial attention.

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.

Further to the Left: Stress-Induced Increase of Spatial Pseudoneglect During the COVID-19 Lockdown.

BACKGROUND: The measures taken to contain the coronavirus disease 2019 (COVID-19) pandemic, such as the lockdown in Italy, do impact psychological health; yet, less is known about their effect on cognitive functioning. The transactional theory of stress predicts reciprocal influences between perceived stress and cognitive performance. However, the effects of a period of stress due to social isolation on spatial cognition and exploration have been little examined. The aim of the present study was to investigate the possible effects and impact of the COVID-19 pandemic on spatial cognition tasks, particularly those concerning spatial exploration, and the physiological leftward bias known as pseudoneglect. A right-hemisphere asymmetry for spatial attention processes crucially contributes to pseudoneglect. Other evidence indicates a predominantly right-hemisphere activity in stressful situations. We also analyzed the effects of lockdown on coping strategies, which typically show an opposite pattern of hemispheric asymmetry, favoring the left hemisphere. If so, then pseudoneglect should increase during the lockdown and be negatively correlated with the efficacy of coping strategies. METHODS: One week before the start of the lockdown due to COVID-19 in Italy (T1), we had collected data from a battery of behavioral tests including tasks of peri-personal spatial cognition. During the quarantine period, from late April to early May 2020 (T2), we repeated the testing sessions with a subgroup of the same participants (47 right-handed students, mean age = 20, SD = 1.33). At both testing sessions, participants performed digitized neuropsychological tests, including a Cancellation task, Radial Arm Maze task, and Raven's Advanced Progressive Matrices. Participants also completed a newly developed COVID-19 Student Stress Scale, based on transactional models of stress, and the Coping Orientation to Problems Experienced-New Italian Version (COPE-NIV) to assess coping orientation. RESULTS: The tendency to start cancelation from a left-sided item, to explore first a left-sided arm of the maze, and to choose erroneous response items on the left side of the page on Raven's matrices increased from T1 to T2. The degree of pseudoneglect increment positively correlated with perceived stress and negatively correlated with Positive Attitude and Problem-Solving COPE-NIV subscales. CONCLUSION: Lockdown-related stress may have contributed to increase leftward bias during quarantine through a greater activation of the right hemisphere. On the other hand, pseudoneglect was decreased for better coping participants, perhaps as a consequence of a more balanced hemispheric activity in these individuals.

Encoding geometric and non-geometric information: a study with evolved agents.

Vertebrate species use geometric information and non-geometric or featural cues to orient. Under some circumstances, when both geometric and non-geometric information are available, the geometric information overwhelms non-geometric cues (geometric primacy). In other cases, we observe the inverse tendency or the successful integration of both cues. In past years, modular explanations have been proposed for the geometric primacy: geometric and non-geometric information are processed separately, with the geometry module playing a dominant role. The modularity issue is related to the recent debate on the encoding of geometric information: is it innate or does it depend on environmental experience? In order to get insight into the mechanisms that cause the wide variety of behaviors observed in nature, we used Artificial Life experiments. We demonstrated that agents trained mainly with a single class of information oriented efficiently when they were exposed to one class of information (geometric or non-geometric). When they were tested in environments that contained both classes of information, they displayed a primacy for the information that they had experienced more during their training phase. Encoding and processing geometric and non-geometric information was run in a single cognitive neuro-representation. These findings represent a theoretical proof that the exposure frequency to different spatial information during a learning/adaptive history could produce agents with no modular neuro-cognitive systems that are able to process different types of spatial information and display various orientation behaviors (geometric primacy, non-geometric primacy, no primacy at all).

Educational Robotics to Foster and Assess Social Relations in Students' Groups.

Robotics has gained, in recent years, a significant role in educational processes that take place in formal, non-formal, and informal contexts, mainly in the subjects related to STEM (science, technology, engineering, and mathematics). Indeed, educational robotics (ER) can be fruitfully applied also to soft skills, as it allows promoting social links between students, if it is proposed as a group activity. Working in a group to solve a problem or to accomplish a task in the robotics field allows fostering new relations and overcoming the constraints of the established links associated to the school context. Together with this aspect, ER offers an environment where it is possible to assess group dynamics by means of sociometric tools. In this paper, we will describe an example of how ER can be used to foster and assess social relations in students' group. In particular, we report a study that compares: (1) a laboratory with robots, (2) a laboratory with Scratch for coding, and (3) a control group. This study involved Italian students attending middle school. As the focus of this experiment was to study relations in students' group, we used the sociometric tools proposed by Moreno. Results show that involving students in a robotics lab can effectively foster relations between students and, jointly with sociometric tools, can be employed to portrait group dynamics in a synthetic and manageable way.

On the Edge Between Digital and Physical: Materials to Enhance Creativity in Children. An Application to Atypical Development.

The 4 P's creativity model (person, process, press, and product) underlines how creativity is strongly connected with the materials employed to conceive and realize a creative outcome. As a multiform construct, it invites a wide variety of approaches to the study of it. One of the most promising ways to address this issue is to connect it with cognitive development and related educational pathways, as creativity can be enhanced and stimulated in every child, leading to an improvement both at personal and societal level. Even if creativity is recognized and highly valued, there is still a lack of methods which can stimulate creativity in an effective way. Useful hints may come from the outstanding contributions of Piaget and Montessori who underlined that interaction with the physical world is a fundamental building block for cognitive development. In this paper, starting from these fixed points, we describe some creativity enhancing methods for children which give importance to the edge between digital and physical materials. Digital materials open new ways to the use and integration of physical materials with hybrid platforms which can be used in educational contexts. Together with this perspective we provide a description of the application of these methodologies to enhance creativity in children with Autistic Spectrum Disorder.

The Number Interval Position Effect (NIPE) in the mental bisection of numerical intervals might reflect the influence of the decimal-number system on the Gaussian representations of numerosities: A combined developmental and computational-modeling study.

Healthy adults show typical error biases when they mentally bisect number intervals without exact calculations. For a given number interval length, the bisection bias is in fact modulated by the position that the interval occupies within a ten. For intervals positioned at the beginning of tens the error bias is directed toward values that are higher than those of the true interval midpoint whereas for intervals at the end of tens the direction of the error bias is reversed toward values that are lower than that of the true midpoint (Doricchi et al., 2009; Rotondaro et al., 2015). This effect has been defined Number Interval Position Effect (NIPE). The NIPE recurs over consecutive tens and it is not found when intervals are bisected through exact calculations. For this reasons we have hypothesized that the NIPE reflects the influence that the habit of counting in tens has on the neural representations of numerosities that humans share with other species. Here, in a developmental study we demonstrate that children from preschool to fifth-grade display a NIPE that is comparable to that of healthy adults. Then, through a computational-modeling study we investigated whether the NIPE might reflect specific patterns in the Gaussian representations of numerosities that are found in the parietal and pre-frontal neuronal populations of macaque monkeys and that underlie approximate numerosity estimations also in humans. The findings of computational simulations suggest that the NIPE might reflect the influence that the learning and use of the decimal numerical system has on the phylogenetically and ontogenetically older representation of numerosities that humans share with other species. These changes in the representation of numerosities have an influence on approximate numerical estimations even when these, like in the case of the mental bisection of number intervals, are elicited by numerical symbols or words.

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.

Pseudoneglect in Visual Search: Behavioral Evidence and Connectional Constraints in Simulated Neural Circuitry.

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 interhemispheric 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; and (3) neurorobots provided with bilateral competence in the VAN but without interhemispheric 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.

Artificial organisms as tools for the development of psychological theory: Tolman's lesson.

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.

Genetic redundancy in evolving populations of simulated robots.

A number of authors have argued that redundancy in biological organisms contributes to their evolvability. We investigate this hypothesis via the experimental manipulation of genetic redundancy in evolving populations of simulated robots controlled by artificial neural networks. A genetic algorithm is used to simulate the evolution of robots with the ability to perform a previously studied task. Redundancy is measured using systematic lesioning. In our experiments, populations of robots with larger genotypes achieve systematically higher fitness than populations whose genotypes are smaller. It is shown that, in principle, robots with smaller genotypes have enough computational power to achieve optimal fitness. Populations with larger (redundant) genotypes appear, however, to be more evolvable and display significantly higher diversity. It is argued that this enhanced evolvability is a direct effect of genetic redundancy, which allows populations of redundant robots to explore neutral networks spanning large areas of genotype space. We conjecture that, where cost considerations allow, redundancy in functional or potentially functional components of the genome may make a valuable contribution to evolution in artificial and perhaps in biological systems. The methods described in the article provide a practical way of testing this hypothesis for the artificial case.