A comparative study of multilayered and single layered neural network based predictive controllers

The authors compare the performance of two types of controllers one based on the multilayered network and the other based on the single layered CMAC network (cerebellar model articulator controller). The neurons (information processing units) in the multi-layered network use Gaussian activation functions. The control scheme which is considered is a predictive control algorithm, along the lines used by Willis et al. (1991), Kambhampati and Warwick (1991). The process selected as a test bed is a continuous stirred tank reactor. The reaction taking place is an irreversible exothermic reaction in a constant volume reactor cooled by a single coolant stream. This reactor is a simplified version of the first tank in the two tank system given by Henson and Seborg (1989).

ERP correlates of shared control mechanisms involved in saccade preparation and in covert attention

We investigated whether attention shifts and eye movement preparation are mediated by shared control mechanisms, as claimed by the premotor theory of attention. ERPs were recorded in three tasks where directional cues presented at the beginning of each trial instructed participants to direct their attention to the cued side without eye movements (Covert task), to prepare an eye movement in the cued direction without attention shifts (Saccade task) or both (Combined task). A peripheral visual Go/Nogo stimulus that was presented 800 ms after cue onset signalled whether responses had to be executed or withheld. Lateralised ERP components triggered during the cue–target interval, which are assumed to reflect preparatory control mechanisms that mediate attentional orienting, were very similar across tasks. They were also present in the Saccade task, which was designed to discourage any concomitant covert attention shifts. These results support the hypothesis that saccade preparation and attentional orienting are implemented by common control structures. There were however systematic differences in the impact of eye movement programming and covert attention on ERPs triggered in response to visual stimuli at cued versus uncued locations. It is concluded that, although the preparatory processes underlying saccade programming and covert attentional orienting may be based on common mechanisms, they nevertheless differ in their spatially specific effects on visual information processing.

An experimental investigation of peer influences on hostile attributions in adolescents

Aggression in young people has been associated with a bias towards attributing hostile intent to others; however, little is known about the origin of biased social information processing. The current study explored the potential role of peer contagion in the emergence of hostile attribution in adolescents. 134 adolescents were assigned to one of two manipulated ‘chat-room’ conditions, where they believed they were communicating with online peers (e-confederates) who endorsed either hostile or benign intent attributions. Adolescents showed increased hostile attributions following exposure to hostile e-confederates and reduced hostility in the benign condition. Further analyses demonstrated that social anxiety was associated with a reduced tendency to take on hostile peer attitudes. Neither gender nor levels of aggression influenced individual susceptibility to peer influence, but aggressive adolescents reported greater affinity with hostile e-confederates.

Bistability through triadic closure

We propose and analyse a class of evolving network models suitable for describing a dynamic topological structure. Applications include telecommunication, on-line social behaviour and information processing in neuroscience. We model the evolving network as a discrete time Markov chain, and study a very general framework where, conditioned on the current state, edges appear or disappear independently at the next timestep. We show how to exploit symmetries in the microscopic, localized rules in order to obtain conjugate classes of random graphs that simplify analysis and calibration of a model. Further, we develop a mean field theory for describing network evolution. For a simple but realistic scenario incorporating the triadic closure effect that has been empirically observed by social scientists (friends of friends tend to become friends), the mean field theory predicts bistable dynamics, and computational results confirm this prediction. We also discuss the calibration issue for a set of real cell phone data, and find support for a stratified model, where individuals are assigned to one of two distinct groups having different within-group and across-group dynamics.

Can sentiment surveys pre-empt real estate market activities?

We evaluate a number of real estate sentiment indices to ascertain current and forward-looking information content that may be useful for forecasting the demand and supply activities. Our focus lies on sector-specific surveys targeting the players from the supply-side of both residential and non-residential real estate markets. Analyzing the dynamic relationships within a Vector Auto-Regression (VAR) framework, we test the efficacy of these indices by comparing them with other coincident indicators in predicting real estate returns. Overall, our analysis suggests that sentiment indicators convey important information which should be embedded in the modeling exercise to predict real estate market returns. Generally, sentiment indices show better information content than broad economic indicators. The goodness of fit of our models is higher for the residential market than for the non-residential real estate sector. The impulse responses, in general, conform to our theoretical expectations. Variance decompositions and out-of-sample predictions generally show desired contribution and reasonable improvement respectively, thus upholding our hypothesis. Quite remarkably, consistent with the theory, the predictability swings when we look through different phases of the cycle. This perhaps suggests that, e.g. during recessions, market players’ expectations may be more accurate predictor of the future performances, conceivably indicating a ‘negative’ information processing bias and thus conforming to the precautionary motive of consumer behaviour.

Attention through self-synchronisation in the spiking neuron stochastic diffusion network

The paper discusses ensemble behaviour in the Spiking Neuron Stochastic Diffusion Network, SNSDN, a novel network exploring biologically plausible information processing based on higher order temporal coding. SNSDN was proposed as an alternative solution to the binding problem [1]. SNSDN operation resembles Stochastic Diffusin on Search, SDS, a non-deterministic search algorithm able to rapidly locate the best instantiation of a target pattern within a noisy search space ([3], [5]). In SNSDN, relevant information is encoded in the length of interspike intervals. Although every neuron operates in its own time, ‘attention’ to a pattern in the search space results in self-synchronised activity of a large population of neurons. When multiple patterns are present in the search space, ‘switching of at- tention’ results in a change of the synchronous activity. The qualitative effect of attention on the synchronicity of spiking behaviour in both time and frequency domain will be discussed.

Schizotypal personality and vulnerability to involuntary autobiographical memories

Background and objectives: Individuals who score high on positive schizotypy personality traits are vulnerable to more frequent trauma-related intrusive memories after a stressful event. This vulnerability may be the product of a low level of contextual integration of non-stressful material combined with a heightened sensitivity to a further reduction in contextual integration during a stressful event. The current study assessed whether high scoring schizotypes are vulnerable to frequent involuntary autobiographical memories (IAMs) of non-stressful material. Methods: A free-association word task was used. Participants completed three recorded trials which were then replayed to allow the identification of any associations where an involuntary autobiographical memory had come to mind. Self-report measures of schizotypy and anxiety were completed. Results: All participants retrieved at least one IAM from the three free-association word trials, with 70% experiencing two or more IAMs. Individuals scoring high in schizotypy reported more IAMs than those who scored low. Over 75% of the memories retrieved were neutral or positive in content. Limitations: The current study is an improvement on previous methodologies used to assess IAMs. However, bias due to retrospective recall remains a possibility. Conclusions: Individuals scoring high in schizotypy are vulnerable to an increased level of neutral intrusive memories which may be associated with a ‘baseline’ level of information-processing which is low in contextual integration.

P-Prism: a computationally efficient approach to scaling up classification rule induction

Top Down Induction of Decision Trees (TDIDT) is the most commonly used method of constructing a model from a dataset in the form of classification rules to classify previously unseen data. Alternative algorithms have been developed such as the Prism algorithm. Prism constructs modular rules which produce qualitatively better rules than rules induced by TDIDT. However, along with the increasing size of databases, many existing rule learning algorithms have proved to be computational expensive on large datasets. To tackle the problem of scalability, parallel classification rule induction algorithms have been introduced. As TDIDT is the most popular classifier, even though there are strongly competitive alternative algorithms, most parallel approaches to inducing classification rules are based on TDIDT. In this paper we describe work on a distributed classifier that induces classification rules in a parallel manner based on Prism.

Neuromantic : from semi-manual to semi-automatic reconstruction of neuron morphology

The ability to create accurate geometric models of neuronal morphology is important for understanding the role of shape in information processing. Despite a significant amount of research on automating neuron reconstructions from image stacks obtained via microscopy, in practice most data are still collected manually. This paper describes Neuromantic, an open source system for three dimensional digital tracing of neurites. Neuromantic reconstructions are comparable in quality to those of existing commercial and freeware systems while balancing speed and accuracy of manual reconstruction. The combination of semi-automatic tracing, intuitive editing, and ability of visualizing large image stacks on standard computing platforms provides a versatile tool that can help address the reconstructions availability bottleneck. Practical considerations for reducing the computational time and space requirements of the extended algorithm are also discussed.

Emotional arousal amplifies the effects of biased competition in the brain

The arousal-biased competition model predicts that arousal increases the gain on neural competition between stimuli representations. Thus, the model predicts that arousal simultaneously enhances processing of salient stimuli and impairs processing of relatively less-salient stimuli. We tested this model with a simple dot-probe task. On each trial, participants were simultaneously exposed to one face image as a salient cue stimulus and one place image as a non-salient stimulus. A border around the face cue location further increased its bottom-up saliency. Before these visual stimuli were shown, one of two tones played: one that predicted a shock (increasing arousal) or one that did not. An arousal-by-saliency interaction in category-specific brain regions (fusiform face area for salient faces and parahippocampal place area for non-salient places) indicated that brain activation associated with processing the salient stimulus was enhanced under arousal whereas activation associated with processing the non-salient stimulus was suppressed under arousal. This is the first functional magnetic resonance imaging study to demonstrate that arousal can enhance information processing for prioritized stimuli while simultaneously impairing processing of non-prioritized stimuli. Thus, it goes beyond previous research to show that arousal does not uniformly enhance perceptual processing, but instead does so selectively in ways that optimizes attention to highly salient stimuli.

Zombie mouse in a Chinese Room

John Searle’s Chinese Room Argument (CRA) purports to demonstrate that syntax is not sufficient for semantics, and, hence, because computation cannot yield understanding, the computational theory of mind, which equates the mind to an information processing system based on formal computations, fails. In this paper, we use the CRA, and the debate that emerged from it, to develop a philosophical critique of recent advances in robotics and neuroscience. We describe results from a body of work that contributes to blurring the divide between biological and artificial systems; so-called animats, autonomous robots that are controlled by biological neural tissue and what may be described as remote-controlled rodents, living animals endowed with augmented abilities provided by external controllers. We argue that, even though at first sight, these chimeric systems may seem to escape the CRA, on closer analysis, they do not. We conclude by discussing the role of the body–brain dynamics in the processes that give rise to genuine understanding of the world, in line with recent proposals from enactive cognitive science.

KPIs and their properties defined with the EXTREME method

Key Performance Indicators (KPIs) are the main instruments of Business Performance Management. KPIs are the measures that are translated to both the strategy and the business process. These measures are often designed for an industry sector with the assumptions about business processes in organizations. However, the assumptions can be too incomplete to guarantee the required properties of KPIs. This raises the need to validate the properties of KPIs prior to their application to performance measurement. This paper applies the method called EXecutable Requirements Engineering Management and Evolution (EXTREME) for validation of the KPI definitions. EXTREME semantically relates the goal modeling, conceptual modeling and protocol modeling techniques into one methodology. The synchronous composition built into protocol modeling enables raceability of goals in protocol models and constructive definitions of a KPI. The application of the method clarifies the meaning of KPI properties and procedures of their assessment and validation.