Modelling mental rotation in cognitive robots

Mental rotation concerns the cognitive processes that allow an agent mentally to rotate the image of an object in order to solve a given task, for example to say if two objects with different orientations are the same or different. Here we present a system-level bio-constrained model, developed within a neurorobotics framework, that provides an embodied account of mental rotation processes relying on neural mechanisms involving motor affordance encoding, motor simulation and the anticipation of the sensory consequences of actions (both visual and proprioceptive). This model and methodology are in agreement with the most recent theoretical and empirical research on mental rotation. The model was validated through experiments with a simulated humanoid robot (iCub) engaged in solving a classical mental rotation test. The results of the test show that the robot is able to solve the task and, in agreement with data from psychology experiments, exhibits response times linearly dependent on the angular disparity between the objects. This model represents a novel detailed operational account of the embodied brain mechanisms that may underlie mental rotation. © The Author(s) 2013

Toward the next generation of research into small area effects on health : a synthesis of multilevel investigations published since July 1998.

To map out area effects on health research, this study had the following aims: (1) to inventory multilevel investigations of area effects on self rated health, cardiovascular diseases and risk factors, and mortality among adults; (2) to describe and critically discuss methodological approaches employed and results observed; and (3) to formulate selected recommendations for advancing the study of area effects on health. Overall, 86 studies were inventoried. Although several innovative methodological approaches and analytical designs were found, small areas are most often operationalised using administrative and statistical spatial units. Most studies used indicators of area socioeconomic status derived from censuses, and few provided information on the validity and reliability of measures of exposures. A consistent finding was that a significant portion of the variation in health is associated with area context independently of individual characteristics. Area effects on health, although significant in most studies, often depend on the health outcome studied, the measure of area exposure used, and the spatial scale at which associations are examined

How affordances associated with a distractor object affect compatibility effects: a study with the computational model TRoPICALS.

Seeing an object activates both visual and action codes in the brain. Crucial evidence supporting this view is the observation of object to response compatibility effects: perception of an object can facilitate or interfere with the execution of an action (e.g., grasping) even when the viewer has no intention of interacting with the object. TRoPICALS is a computational model that proposes some general principles about the brain mechanisms underlying compatibility effects, in particular the idea that top-down bias from prefrontal cortex, and whether it conflicts or not with the actions afforded by an object, plays a key role in such phenomena. Experiments on compatibility effects using a target and a distractor object show the usual positive compatibility effect of the target, but also an interesting negative compatibility effect of the distractor: responding with a grip compatible with the distractor size produces slower reaction times than the incompatible case. Here, we present an enhanced version of TRoPICALS that reproduces and explains these new results. This explanation is based on the idea that the prefrontal cortex plays a double role in its top-down guidance of action selection producing: (a) a positive bias in favour of the action requested by the experimental task; (b) a negative bias directed to inhibiting the action evoked by the distractor. The model also provides testable predictions on the possible consequences of damage to volitional circuits such as in Parkinsonian patients

Integration of Action and Language Knowledge: A Roadmap for Developmental Robotics

“This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.”This position paper proposes that the study of embodied cognitive agents, such as humanoid robots, can advance our understanding of the cognitive development of complex sensorimotor, linguistic, and social learning skills. This in turn will benefit the design of cognitive robots capable of learning to handle and manipulate objects and tools autonomously, to cooperate and communicate with other robots and humans, and to adapt their abilities to changing internal, environmental, and social conditions. Four key areas of research challenges are discussed, specifically for the issues related to the understanding of: 1) how agents learn and represent compositional actions; 2) how agents learn and represent compositional lexica; 3) the dynamics of social interaction and learning; and 4) how compositional action and language representations are integrated to bootstrap the cognitive system. The review of specific issues and progress in these areas is then translated into a practical roadmap based on a series of milestones. These milestones provide a possible set of cognitive robotics goals and test scenarios, thus acting as a research roadmap for future work on cognitive developmental robotics.Peer reviewe

ANNABELL, a cognitive system able to learn different languages

© 2018 The authors and IOS Press. All rights reserved. ANNABELL is a cognitive system entirely based on a large-scale neural architecture capable of learning to communicate through natural language starting from a tabula rasa condition. In order to shed light on the level of cognitive development required for language acquisition, in this work the model is used to study the acquisition of a new language, namely Albanian, in addition to English. The aim is to evaluate in a completely different and more complex language the ability of the model to acquire new information through several examples introduced in the new language and to process the acquired information, answering questions that require the use of different language patterns. The results show that the system is capable of learning cumulatively in either language and to develop a broad range of language processing functionalities in both languages

Embodied language learning and cognitive bootstrapping: methods and design principles

Co-development of action, conceptualization and social interaction mutually scaffold and support each other within a virtuous feedback cycle in the development of human language in children. Within this framework, the purpose of this article is to bring together diverse but complementary accounts of research methods that jointly contribute to our understanding of cognitive development and in particular, language acquisition in robots. Thus, we include research pertaining to developmental robotics, cognitive science, psychology, linguistics and neuroscience, as well as practical computer science and engineering. The different studies are not at this stage all connected into a cohesive whole; rather, they are presented to illuminate the need for multiple different approaches that complement each other in the pursuit of understanding cognitive development in robots. Extensive experiments involving the humanoid robot iCub are reported, while human learning relevant to developmental robotics has also contributed useful results. Disparate approaches are brought together via common underlying design principles. Without claiming to model human language acquisition directly, we are nonetheless inspired by analogous development in humans and consequently, our investigations include the parallel co-development of action, conceptualization and social interaction. Though these different approaches need to ultimately be integrated into a coherent, unified body of knowledge, progress is currently also being made by pursuing individual methods

Abstract concept learning in cognitive robots

Purpose of Review Understanding and manipulating abstract concepts is a fundamental characteristic of human intelligence that is currently missing in artificial agents. Without it, the ability of these robots to interact socially with humans while performing their tasks would be hindered. However, what is needed to empower our robots with such a capability? In this article, we discuss some recent attempts on cognitive robot modeling of these concepts underpinned by some neurophysiological principles. Recent Findings For advanced learning of abstract concepts, an artificial agent needs a (robotic) body, because abstract and concrete concepts are considered a continuum, and abstract concepts can be learned by linking them to concrete embodied perceptions. Pioneering studies provided valuable information about the simulation of artificial learning and demonstrated the value of the cognitive robotics approach to study aspects of abstract cognition. Summary There are a few successful examples of cognitive models of abstract knowledge based on connectionist and probabilistic modeling techniques. However, the modeling of abstract concept learning in robots is currently limited at narrow tasks. To make further progress, we argue that closer collaboration among multiple disciplines is required to share expertise and co-design future studies. Particularly important is to create and share benchmark datasets of human learning behavior

Exosomal microRNAs from Longitudinal Liquid Biopsies for the Prediction of Response to Induction Chemotherapy in High-Risk Neuroblastoma Patients: A Proof of Concept SIOPEN Study

Despite intensive treatment, 50% of children with high-risk neuroblastoma (HR-NB) succumb to their disease. Progression through current trials evaluating the efficacy of new treatments for children with HR disease usually depends on an inadequate response to induction chemotherapy, assessed using imaging modalities. In this study, we sought to identify circulating biomarkers that might be detected in a simple blood sample to predict patient response to induction chemotherapy. Since exosomes released by tumor cells can drive tumor growth and chemoresistance, we tested the hypothesis that exosomal microRNA (exo-miRNAs) in blood might predict response to induction chemotherapy. The exo-miRNAs expression profile in plasma samples collected from children treated in HR-NBL-1/SIOPEN before and after induction chemotherapy was compared to identify a three exo-miRs signature that could discriminate between poor and good responders. Exo-miRNAs expression also provided a chemoresistance index predicting the good or poor prognosis of HR-NB patients