The dynamics of threat, fear and intentionality in the conduct disorders: longitudinal findings in the children of women with post-natal depression

This paper considers how environmental threat may contribute to the child's use of avoidant strategies to regulate negative emotions, and how this may interact with high emotional reactivity to create vulnerability to conduct disorder symptoms. We report a study based on the hypothesis that interpreting others' behaviours in terms of their motives and emotions - using the intentional stance - promotes effective social action, but may lead to fear in threatful situations, and that inhibiting the intentional stance may reduce fear but promote conduct disorder symptoms. We assessed 5-year-olds' use of the intentional stance with an intentionality scale, contrasting high and low threat doll play scenarios. In a sample of 47 children of mothers with post-natal depression ( PND) and 35 controls, children rated as securely attached with their mothers at the age of 18 months were better able to preserve the intentional stance than insecure children in high threat scenarios, but not in low threat scenarios. Girls had higher intentionality scores than boys across all scenarios. Only intentionality in the high threat scenario was associated with teacher-rated conduct disorder symptoms, and only in the children of women with PND. Intentionality mediated the associations between attachment security and gender and conduct disorder symptoms in the PND group.

Threat interpretation in anxious children and their mothers: comparison with nonclinical children and the effects of treatment

Interpretation biases towards threat play a prominent role in cognitive theories of anxiety, and have been identified amongst highly anxious adults and children. Little is known, however, about the development of these cognitive biases although family processes have been implicated. The current study investigated the nature of threat interpretation of anxious children and their mothers through (i) comparison of a clinic and non-clinic population, (ii) analysis of individual differences; and (ill) pre- and post-treatment comparisons. Participants were 27 children with a primary anxiety disorder and 33 children from a non-clinic population and their mothers. Children and mothers completed self-report measures of anxiety and indicated their most likely interpretation of ambiguous scenarios. Clinic and non-clinical groups differed significantly on measures of threat interpretation. Furthermore, mothers' and children's threat interpretation correlated significantly. Following treatment for child anxiety, both children and their mothers reported a reduction in threat interpretation. (c) 2005 Elsevier Ltd. All rights reserved.

Inverse problems in dynamic cognitive modeling

Inverse problems for dynamical system models of cognitive processes comprise the determination of synaptic weight matrices or kernel functions for neural networks or neural/dynamic field models, respectively. We introduce dynamic cognitive modeling as a three tier top-down approach where cognitive processes are first described as algorithms that operate on complex symbolic data structures. Second, symbolic expressions and operations are represented by states and transformations in abstract vector spaces. Third, prescribed trajectories through representation space are implemented in neurodynamical systems. We discuss the Amari equation for a neural/dynamic field theory as a special case and show that the kernel construction problem is particularly ill-posed. We suggest a Tikhonov-Hebbian learning method as regularization technique and demonstrate its validity and robustness for basic examples of cognitive computations.

The SAFEE on-board threat detection system

Under the framework of the European Union Funded SAFEE project(1), this paper gives an overview of a novel monitoring and scene analysis system developed for use onboard aircraft in spatially constrained environments. The techniques discussed herein aim to warn on-board crew about pre-determined indicators of threat intent (such as running or shouting in the cabin), as elicited from industry and security experts. The subject matter experts believe that activities such as these are strong indicators of the beginnings of undesirable chains of events or scenarios, which should not be allowed to develop aboard aircraft. This project aimes to detect these scenarios and provide advice to the crew. These events may involve unruly passengers or be indicative of the precursors to terrorist threats. With a state of the art tracking system using homography intersections of motion images, and probability based Petri nets for scene understanding, the SAFEE behavioural analysis system automatically assesses the output from multiple intelligent sensors, and creates. recommendations that are presented to the crew using an integrated airborn user interface. Evaluation of the system is conducted within a full size aircraft mockup, and experimental results are presented, showing that the SAFEE system is well suited to monitoring people in confined environments, and that meaningful and instructive output regarding human actions can be derived from the sensor network within the cabin.

Dynamic virtual environments using really simple syndication

This paper investigates the use of really simple syndication (RSS) to dynamically change virtual environments. The case study presented here uses meteorological data downloaded from the Internet in the form of an RSS feed, this data is used to simulate current weather patterns in a virtual environment. The downloaded data is aggregated and interpreted in conjunction with a configuration file, used to associate relevant weather information to the rendering engine. The engine is able to animate a wide range of basic weather patterns. Virtual reality is a way of immersing a user into a different environment, the amount of immersion the user experiences is important. Collaborative virtual reality will benefit from this work by gaining a simple way to incorporate up-to-date RSS feed data into any environment scenario. Instead of simulating weather conditions in training scenarios, actual weather conditions can be incorporated, improving the scenario and immersion.

Dynamic automata for mobile robot learning

Researchers at the University of Reading have developed over many years some simple mobile robots that explore an environment they perceive through simple ultrasonic sensors. Information from these sensors has allowed the robots to learn the simple task of moving around while avoiding dynamic obstacles using a static set of fuzzy automata, the choice of which has been criticised, due to its arbitrary nature. This paper considers how a dynamic set of automata can overcome this criticism. In addition, a new reinforcement learning function is outlined which is both scalable to different numbers and types of sensors. The innovations compare successfully with earlier work.

Predictive control using feedback linearization based on dynamic neural models

This paper presents a hybrid control strategy integrating dynamic neural networks and feedback linearization into a predictive control scheme. Feedback linearization is an important nonlinear control technique which transforms a nonlinear system into a linear system using nonlinear transformations and a model of the plant. In this work, empirical models based on dynamic neural networks have been employed. Dynamic neural networks are mathematical structures described by differential equations, which can be trained to approximate general nonlinear systems. A case study based on a mixing process is presented.

Performance of asynchronous channel assignment scheme in non-uniform and dynamic topology WLANs

Dense deployments of wireless local area networks (WLANs) are fast becoming a permanent feature of all developed cities around the world. While this increases capacity and coverage, the problem of increased interference, which is exacerbated by the limited number of channels available, can severely degrade the performance of WLANs if an effective channel assignment scheme is not employed. In an earlier work, an asynchronous, distributed and dynamic channel assignment scheme has been proposed that (1) is simple to implement, (2) does not require any knowledge of the throughput function, and (3) allows asynchronous channel switching by each access point (AP). In this paper, we present extensive performance evaluation of this scheme when it is deployed in the more practical non-uniform and dynamic topology scenarios. Specifically, we investigate its effectiveness (1) when APs are deployed in a nonuniform fashion resulting in some APs suffering from higher levels of interference than others and (2) when APs are effectively switched `on/off' due to the availability/lack of traffic at different times, which creates a dynamically changing network topology. Simulation results based on actual WLAN topologies show that robust performance gains over other channel assignment schemes can still be achieved even in these realistic scenarios.