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.

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.

Cognitive models in biomimetic pattern cognition

We describe a new model which is based on the concept of cognizing theory. The method identifies subsets of the data which are embedded in arbitrary oriented lower dimensional space. We definite k-mean covering, and study its property. Covering subsets of points are repeatedly sampled to construct trial geometry space of various dimensions. The sampling corresponding to the feature space having the best cognition ability between a mode near zero and the rest is selected and the data points are partitioned on the basis of the best cognition ability. The repeated sampling then continues recursively on each block of the data. We propose this algorithm based on cognition models. The experimental results for face recognition demonstrate that the correct rejection rate of the test samples excluded in the classes of training samples is very high and effective.

Effects of Naotan Pill on repair of neural cells and cognitive disorders in juvenile rats following hypoxia and ischemia

BACKGROUND: Hypoxia and ischemia induce neuronal damage, decreased neuronal numbers and synaptophysin levels, and deficits in learning and memory functions. Previous studies have shown that lycium barbarum polysaccharide, the most effective component of barbary wolfberry fruit, has protective effects on neural cells in hypoxia-ischemia. OBJECTIVE: To investigate the effects of Naotan Pill on glutamate-treated neural cells and on cognitive function in juvenile rats following hypoxia-ischemia. DESIGN, TIME AND SETTING: The randomized, controlled, in vivo study was performed at the Cell Laboratory of Lanzhou University, Lanzhou Institute of Modern Physics of Chinese Academy of Sciences, and Department of Traditional Chinese Medicine of Gansu Provincial Rehabilitation Center Hospital, China from December 2005 to August 2006. The cellular neurobiology, in vitro experiment was conducted at the Institute of Human Anatomy, Histology, Embryology and Neuroscience, School of Basic Medical Sciences, Lanzhou University, and Department of Traditional Chinese Medicine of Gansu Provincial Rehabilitation Center Hospital, China from March 2007 to January 2008. MATERIALS: Naotan Pill, composed of barbary wolfberry fruit, danshen root, grassleaf sweetflag rhizome, and glossy privet fruit, was prepared by Gansu Provincial Rehabilitation Center, China. Rabbit anti-synaptophysin, choline acetyl transferase polyclonal antibody, streptavidin-biotin complex kit and diaminobenzidine kit (Boster, Wuhan, China), as well as glutamate (Hualian, Shanghai, China) were used in this study. METHODS: Cortical neural cells were isolated from neonatal Wistar rats. Neural cell damage models were induced using glutamate, and administered Naotan Pill prior to and following damage. A total of 54 juvenile Wistar rats were equally and randomly assigned into model, Naotan Pill, and sham operation groups. The left common carotid artery was ligated, and then rat models of hypoxic-ischemic injury were assigned to the model and Naotan Pill groups. At 2 days following model induction, rats in the Naotan Pill group were administered Naotan Pill suspension for 21 days. In the model and sham operation groups, rats received an equal volume of saline. MAIN OUTCOME MEASURES: Neural cell morphology was observed using an inverted phase contrast microscope. Survival rate of neural cells was measured by MTT assay. Synaptophysin and choline acetyl transferase expression was observed in the hippocampal CA1 region of juvenile rats using immunohistochemistry. Cognitive function was tested by the Morris water maze. RESULTS: Pathological changes were detected in glutamate-treated neural cells. Neural cell morphology remained normal after Naotan Pill intervention. Absorbance and survival rate of neural cells were significantly greater following Naotan Pill intervention, compared to glutamate-treated neural cells (P < 0.05). Synaptophysin and choline acetyl transferase expression was lowest in the hippocampal CA1 region in the model group and highest in the sham operation group. Significant differences among groups were observed (P < 0.05). Escape latency and swimming distance were significantly longer in the model group compared to the Naotan Pill group (P < 0.05). CONCLUSION: Naotan Pill exhibited protective and repair effects on glutamate-treated neural cells. Naotan Pill upregulated synaptophysin and choline acetyl transferase expression in the hippocampus and improved cognitive function in rats following hypoxia-ischemia.

An Obstacle Cluster Based Data Fusion Method for Mobile Robot

首先给出了一种通过融合多个超声波传感器和一台激光全局定位系统的数据建立机器人环境地图的方法 ,并在此基础上 ,首次提出了机器人在非结构环境下识别障碍物的一种新方法 ,即基于障碍物群的方法 .该方法的最大特点在于它可以更加简洁、有效地提取和描述机器人的环境特征 ,这对于较好地实现机器人的导航、避障 ,提高系统的自主性和实时性是至关重要的 .大量的实验结果表明了该方法的有效性 .

Tense, Aspect and the Cognitive Representation of Time

This paper explores the relationships between a computation theory of temporal representation (as developed by James Allen) and a formal linguistic theory of tense (as developed by Norbert Hornstein) and aspect. It aims to provide explicit answers to four fundamental questions: (1) what is the computational justification for the primitive of a linguistic theory; (2) what is the computational explanation of the formal grammatical constraints; (3) what are the processing constraints imposed on the learnability and markedness of these theoretical constructs; and (4) what are the constraints that a linguistic theory imposes on representations. We show that one can effectively exploit the interface between the language faculty and the cognitive faculties by using linguistic constraints to determine restrictions on the cognitive representation and vice versa. Three main results are obtained: (1) We derive an explanation of an observed grammatical constraint on tense?? Linear Order Constraint??m the information monotonicity property of the constraint propagation algorithm of Allen's temporal system: (2) We formulate a principle of markedness for the basic tense structures based on the computational efficiency of the temporal representations; and (3) We show Allen's interval-based temporal system is not arbitrary, but it can be used to explain independently motivated linguistic constraints on tense and aspect interpretations. We also claim that the methodology of research developed in this study??oss-level" investigation of independently motivated formal grammatical theory and computational models??a powerful paradigm with which to attack representational problems in basic cognitive domains, e.g., space, time, causality, etc.

Yield drag in a two-dimensional foam flow around a circular obstacle: Effect of liquid fraction

Raufaste, C., Dollet, B., Cox, S., Jiang, Y. and Graner, F. (2007). Yield drag in a two-dimensional foam flow around a circular obstacle: Effect of liquid fraction. European Physical Journal E, 23 (2), 217?228 Sponsorship: Y.J. is supported by US DOE under contract No. DE-AC52-06NA25396. S.C. is supported by EPSRC (EP/D071127/1)

A cognitive approach to identifying measurable milestones for programming skill acquisition

Mead, J., Gray, S., Hamer, J., James, R., Sorva, J., Clair, C. S., and Thomas, L. 2006. A cognitive approach to identifying measurable milestones for programming skill acquisition. SIGCSE Bull. 38, 4 (Dec. 2006), 182-194.

Teaching English tense and aspect with the help of cognitive grammar: An empirical study

Form-focused instruction is usually based on traditional practical/pedagogical grammar descriptions of grammatical features. The comparison of such traditional accounts with cognitive grammar (CG) descriptions seems to favor CG as a basis of pedagogical rules. This is due to the insistence of CG on the meaningfulness of grammar and its detailed analyses of the meanings of particular grammatical features. The differences between traditional and CG rules/descriptions are exemplified by juxtaposing the two kinds of principles concerning the use of the present simple and present progressive to refer to situations happening or existing at speech time. The descriptions provided the bases for the instructional treatment in a quasi-experimental study exploring the effectiveness of using CG descriptions of the two tenses, and of their interplay with stative (imperfective) and dynamic (perfective) verbs, and comparing this effectiveness with the value of grammar teaching relying on traditional accounts found in standard pedagogical grammars. The study involved 50 participants divided into three groups, with one of them constituting the control group and the other two being experimental ones. One of the latter received treatment based on CG descriptions and the other on traditional accounts. CG-based instruction was found to be at least moderately effective in terms of fostering mostly explicit grammatical knowledge and its effectiveness turned out be comparable to that of teaching based on traditional descriptions.

Neural Dynamics of Autistic Behaviors: Cognitive, Emotional, and Timing Substrates

What brain mechanisms underlie autism and how do they give rise to autistic behavioral symptoms? This article describes a neural model, called the iSTART model, which proposes how cognitive, emotional, timing, and motor processes may interact together to create and perpetuate autistic symptoms. These model processes were originally developed to explain data concerning how the brain controls normal behaviors. The iSTART model shows how autistic behavioral symptoms may arise from prescribed breakdowns in these brain processes.

Navite: A Neural Network System For Sensory-Based Robot Navigation

A neural network system, NAVITE, for incremental trajectory generation and obstacle avoidance is presented. Unlike other approaches, the system is effective in unstructured environments. Multimodal inforrnation from visual and range data is used for obstacle detection and to eliminate uncertainty in the measurements. Optimal paths are computed without explicitly optimizing cost functions, therefore reducing computational expenses. Simulations of a planar mobile robot (including the dynamic characteristics of the plant) in obstacle-free and object avoidance trajectories are presented. The system can be extended to incorporate global map information into the local decision-making process.

Laminar Cortical Dynamics of Cognitive and Motor Working Memory, Sequence Learning and Performance: Toward a Unified Theory of How the Cerebral Cortex Works

How do the layered circuits of prefrontal and motor cortex carry out working memory storage, sequence learning, and voluntary sequential item selection and performance? A neural model called LIST PARSE is presented to explain and quantitatively simulate cognitive data about both immediate serial recall and free recall, including bowing of the serial position performance curves, error-type distributions, temporal limitations upon recall, and list length effects. The model also qualitatively explains cognitive effects related to attentional modulation, temporal grouping, variable presentation rates, phonemic similarity, presentation of non-words, word frequency/item familiarity and list strength, distracters and modality effects. In addition, the model quantitatively simulates neurophysiological data from the macaque prefrontal cortex obtained during sequential sensory-motor imitation and planned performance. The article further develops a theory concerning how the cerebral cortex works by showing how variations of the laminar circuits that have previously clarified how the visual cortex sees can also support cognitive processing of sequentially organized behaviors.