Modelling cognitive impairment to improve universal access

The purpose of this study is to develop a model of cognitive impairment to help designers consider the range of issues which affect the lives of people living with such impairment. A series of interviews with experts of cognitive impairment was conducted to describe and assess the links between specific medical conditions, including learning disability, specific learning difficulties, autistic spectrum disorders, traumatic brain injury and schizophrenia, and the types of cognitive impairment associated with them. The results reveal some of the most prevalent and serious types of impairment, which - when transformed into design guidance - will help designers make mainstream products more inclusive also for people with cognitive impairment. © 2011 Springer-Verlag.

A framework for studying cognitive impairment to inform inclusive design

Chapter 12 A Framework for Studying Cognitive Impairment to Inform Inclusive Design E. Jokisuu, PM Langdon and PJ Clarkson 12.1 Introduction Cognitive impairment is an exceptionally complicated phenomenon, for which no simple solutions ...

Modulation of pain processing in hyperalgesia by cognitive demand.

The relationship between pain and cognitive function is of theoretical and clinical interest, exemplified by observations that attention-demanding activities reduce pain in chronically afflicted patients. Previous studies have concentrated on phasic pain, which bears little correspondence to clinical pain conditions. Indeed, phasic pain is often associated with differential or opposing effects to tonic pain in behavioral, lesion, and pharmacological studies. To address how cognitive engagement interacts with tonic pain, we assessed the influence of an attention-demanding cognitive task on pain-evoked neural responses in an experimental model of chronic pain, the capsaicin-induced heat hyperalgesia model. Using functional magnetic resonance imaging (fMRI), we show that activity in the orbitofrontal and medial prefrontal cortices, insula, and cerebellum correlates with the intensity of tonic pain. This pain-related activity in medial prefrontal cortex and cerebellum was modulated by the demand level of the cognitive task. Our findings highlight a role for these structures in the integration of motivational and cognitive functions associated with a physiological state of injury. Within the limitations of an experimental model of pain, we suggest that the findings are relevant to understanding both the neurobiology and pathophysiology of chronic pain and its amelioration by cognitive strategies.

Robotics and neuroscience: A rhythmic interaction

At the crossing between motor control neuroscience and robotics system theory, the paper presents a rhythmic experiment that is amenable both to handy laboratory implementation and simple mathematical modeling. The experiment is based on an impact juggling task, requiring the coordination of two upper-limb effectors and some phase-locking with the trajectories of one or several juggled objects. We describe the experiment, its implementation and the mathematical model used for the analysis. Our underlying research focuses on the role of sensory feedback in rhythmic tasks. In a robotic implementation of our experiment, we study the minimum feedback that is required to achieve robust control. A limited source of feedback, measuring only the impact times, is shown to give promising results. A second field of investigation concerns the human behavior in the same impact juggling task. We study how a variation of the tempo induces a transition between two distinct control strategies with different sensory feedback requirements. Analogies and differences between the robotic and human behaviors are obviously of high relevance in such a flexible setup. © 2008 Elsevier Ltd. All rights reserved.

Cognitive Tomography Reveals Complex, Task-Independent Mental Representations

Humans develop rich mental representations that guide their behavior in a variety of everyday tasks. However, it is unknown whether these representations, often formalized as priors in Bayesian inference, are specific for each task or subserve multiple tasks. Current approaches cannot distinguish between these two possibilities because they cannot extract comparable representations across different tasks [1-10]. Here, we develop a novel method, termed cognitive tomography, that can extract complex, multidimensional priors across tasks. We apply this method to human judgments in two qualitatively different tasks, "familiarity" and "odd one out," involving an ecologically relevant set of stimuli, human faces. We show that priors over faces are structurally complex and vary dramatically across subjects, but are invariant across the tasks within each subject. The priors we extract from each task allow us to predict with high precision the behavior of subjects for novel stimuli both in the same task as well as in the other task. Our results provide the first evidence for a single high-dimensional structured representation of a naturalistic stimulus set that guides behavior in multiple tasks. Moreover, the representations estimated by cognitive tomography can provide independent, behavior-based regressors for elucidating the neural correlates of complex naturalistic priors. © 2013 The Authors.

Self-organization, embodiment, and biologically inspired robotics.

Robotics researchers increasingly agree that ideas from biology and self-organization can strongly benefit the design of autonomous robots. Biological organisms have evolved to perform and survive in a world characterized by rapid changes, high uncertainty, indefinite richness, and limited availability of information. Industrial robots, in contrast, operate in highly controlled environments with no or very little uncertainty. Although many challenges remain, concepts from biologically inspired (bio-inspired) robotics will eventually enable researchers to engineer machines for the real world that possess at least some of the desirable properties of biological organisms, such as adaptivity, robustness, versatility, and agility.

Cognition from the bottom up: on biological inspiration, body morphology, and soft materials.

Traditionally, in cognitive science the emphasis is on studying cognition from a computational point of view. Studies in biologically inspired robotics and embodied intelligence, however, provide strong evidence that cognition cannot be analyzed and understood by looking at computational processes alone, but that physical system-environment interaction needs to be taken into account. In this opinion article, we review recent progress in cognitive developmental science and robotics, and expand the notion of embodiment to include soft materials and body morphology in the big picture. We argue that we need to build our understanding of cognition from the bottom up; that is, all the way from how our body is physically constructed.

Robotics education: A case study in soft-bodied locomotion

Locomotion has been one of the frequently used case studies in hands-on curricula in robotics education. Students are usually instructed to construct their own wheeled or legged robots from modular robot kits. In the development process of a robot students tend to emphasize on the programming part and consequently, neglect the design of the robot's body. However, the morphology of a robot (i.e. its body shape and material properties) plays an important role especially in dynamic tasks such as locomotion. In this paper we introduce a case study of a tutorial on soft-robotics where students were encouraged to focus solely on the morphology of a robot to achieve stable and fast locomotion. The students should experience the influence material properties exert on the performance of a robot and consequently, extract design principles. This tutorial was held in the context of the 2012 Summer School on Soft Robotics at ETH Zurich, which was one of the world's first courses specialized in the emerging field. We describe the tutorial set-up, the used hardware and software, the students assessment criteria as well as the results. Based on the high creativity and diversity of the robots built by the students, we conclude that the concept of this tutorial has great potentials for both education and research. © 2013 IEEE.

Soft robotics: The next generation of intelligent machines

There has been an increasing interest in applying biological principles to the design and control of robots. Unlike industrial robots that are programmed to execute a rather limited number of tasks, the new generation of bio-inspired robots is expected to display a wide range of behaviours in unpredictable environments, as well as to interact safely and smoothly with human co-workers. In this article, we put forward some of the properties that will characterize these new robots: soft materials, flexible and stretchable sensors, modular and efficient actuators, self-organization and distributed control. We introduce a number of design principles; in particular, we try to comprehend the novel design space that now includes soft materials and requires a completely different way of thinking about control. We also introduce a recent case study of developing a complex humanoid robot, discuss the lessons learned and speculate about future challenges and perspectives.

Soft robotics: Challenges and perspectives

There has been an increasing interest in the use of unconventional materials and morphologies in robotic systems because the underlying mechanical properties (such as body shapes, elasticity, viscosity, softness, density and stickiness) are crucial research topics for our in-depth understanding of embodied intelligence. The detailed investigations of physical system-environment interactions are particularly important for systematic development of technologies and theories of emergent adaptive behaviors. Based on the presentations and discussion in the Future Emerging Technology (fet11) conference, this article introduces the recent technological development in the field of soft robotics, and speculates about the implications and challenges in the robotics and embodied intelligence research. © Selection and peer-review under responsibility of FET11 conference organizers and published by Elsevier B.V.

New robotics: design principles for intelligent systems.

New robotics is an approach to robotics that, in contrast to traditional robotics, employs ideas and principles from biology. While in the traditional approach there are generally accepted methods (e. g., from control theory), designing agents in the new robotics approach is still largely considered an art. In recent years, we have been developing a set of heuristics, or design principles, that on the one hand capture theoretical insights about intelligent (adaptive) behavior, and on the other provide guidance in actually designing and building systems. In this article we provide an overview of all the principles but focus on the principles of ecological balance, which concerns the relation between environment, morphology, materials, and control, and sensory-motor coordination, which concerns self-generated sensory stimulation as the agent interacts with the environment and which is a key to the development of high-level intelligence. As we argue, artificial evolution together with morphogenesis is not only "nice to have" but is in fact a necessary tool for designing embodied agents.