Cognitive biases and addiction: an evolution in theory and method

An evolution in theoretical models and methodological paradigms for investigating cognitive biases in the addictions is discussed. Anomalies in traditional cognitive perspectives, and problems with the self-report methods which underpin them, are highlighted. An emergent body of cognitive research, contextualized within the principles and paradigms of cognitive neuropsychology rather than social learning theory, is presented which, it is argued, addresses these anomalies and problems. Evidence is presented that biases in the processing of addiction-related stimuli, and in the network of propositions which motivate addictive behaviours, occur at automatic, implicit and pre-conscious levels of awareness. It is suggested that methods which assess such implicit cognitive biases (e.g. Stroop, memory, priming and reaction-time paradigms) yield findings which have better predictive utility for ongoing behaviour than those biases determined by self-report methods of introspection. The potential utility of these findings for understanding "loss of control" phenomena, and the desynchrony between reported beliefs and intentions and ongoing addictive behaviours, is discussed. Applications to the practice of cognitive therapy are considered.

Is Use of Optional Attributes and Associations in Conceptual Modeling Always Problematic? Theory and Empirical Tests

Prior research has argued that use of optional properties in conceptual models results in loss of information about the semantics of the domains represented by the models. Empirical research undertaken to date supports this argument. Nevertheless, no systematic analysis has been done of whether use of optional properties is always problematic. Furthermore, prior empirical research might have deliberately or unwittingly employed models where use of optionality always causes problems. Accordingly, we examine analytically whether use of optional properties is always problematic. We employ our analytical results to inform the design of an experiment where we systematically examined the impact of optionality on users’ ability to understand domains represented by different types of conceptual models. We found evidence that use of optionality undermines users’ ability to understand the domain represented by a model but that this effect weakens when use of mandatory properties to replace optional properties leads to more-complex models.

A Statistical Model for Shadowed Body Centric Communications Channels: Theory and Validation

This paper presents a new statistical signal reception model for shadowed body-centric communications channels. In this model, the potential clustering of multipath components is considered alongside the presence of elective dominant signal components. As typically occurs in body-centric communications channels, the dominant or line-of-sight (LOS) components are shadowed by body matter situated in the path trajectory. This situation may be further exacerbated due to physiological and biomechanical movements of the body. In the proposed model, the resultant dominant component which is formed by the phasor addition of these leading contributions is assumed to follow a lognormal distribution. A wide range of measured and simulated shadowed body-centric channels considering on-body, off-body and body-to-body communications are used to validate the model. During the course of the validation experiments, it was found that, even for environments devoid of multipath or specular reflections generated by the local surroundings, a noticeable resultant dominant component can still exist in body-centric channels where the user's body shadows the direct LOS signal path between the transmitter and the receiver.

Optimization through quantum annealing: theory and some applications

Quantum annealing is a promising tool for solving optimization problems, similar in some ways to the traditional ( classical) simulated annealing of Kirkpatrick et al. Simulated annealing takes advantage of thermal fluctuations in order to explore the optimization landscape of the problem at hand, whereas quantum annealing employs quantum fluctuations. Intriguingly, quantum annealing has been proved to be more effective than its classical counterpart in many applications. We illustrate the theory and the practical implementation of both classical and quantum annealing - highlighting the crucial differences between these two methods - by means of results recently obtained in experiments, in simple toy-models, and more challenging combinatorial optimization problems ( namely, Random Ising model and Travelling Salesman Problem). The techniques used to implement quantum and classical annealing are either deterministic evolutions, for the simplest models, or Monte Carlo approaches, for harder optimization tasks. We discuss the pro and cons of these approaches and their possible connections to the landscape of the problem addressed.

Disturbance, neutral theory and patterns of beta diversity in soil communities

Beta diversity describes how local communities within an area or region differ in species composition/abundance. There have been attempts to use changes in beta diversity as a biotic indicator of disturbance, but lack of theory and methodological caveats have hampered progress. We here propose that the neutral theory of biodiversity plus the definition of beta diversity as the total variance of a community matrix provide a suitable, novel, starting point for ecological applications. Observed levels of beta diversity (BD) can be compared to neutral predictions with three possible outcomes: Observed BD equals neutral prediction or is larger (divergence) or smaller (convergence) than the neutral prediction. Disturbance might lead to either divergence or convergence, depending on type and strength. We here apply these ideas to datasets collected on oribatid mites (a key, very diverse soil taxon) under several regimes of disturbances. When disturbance is expected to increase the heterogeneity of soil spatial properties or the sampling strategy encompassed a range of diverging environmental conditions, we observed diverging assemblages. On the contrary, we observed patterns consistent with neutrality when disturbance could determine homogenization of soil properties in space or the sampling strategy encompassed fairly homogeneous areas. With our method, spatial and temporal changes in beta diversity can be directly and easily monitored to detect significant changes in community dynamics, although the method itself cannot inform on underlying mechanisms. However, human-driven disturbances and the spatial scales at which they operate are usually known. In this case, our approach allows the formulation of testable predictions in terms of expected changes in beta diversity, thereby offering a promising monitoring tool.