14,834 research outputs found
Desire thinking and craving across the continuum of problem drinking
Desire thinking has been conceptualized as a conscious and voluntary cognitive process prefiguring images, information and memories about positive target-related experience. In the last few years, desire thinking has been found to be closely involved in addictive behaviours (substance and behavioural addictions).
Research in this field has investigated the role of desire thinking in increasing craving experience and leading to problematic behaviours (such as binge drinking and gambling). So far, studies on desire thinking have focused especially on drinking behaviour. Preliminary evidence is also emerging in the field of behavioural addictions. The first aim of this thesis was to investigate desire thinking across addictive behaviours, through a systematic review of existing studies (first study of the present thesis). The ten included studies highlighted a significant relationship between desire thinking and addictive behaviour in all conditions (alcohol use, nicotine use, gambling, problematic internet use), even though the nature of studies were mostly cross-sectional. The second and the third studies of my thesis aimed to explore longitudinally, in clinical and non- clinical populations, the involvement of desire thinking in increasing craving experience (supporting previous data) and assessing its impact (over and above craving) in leading to binge drinking and alcohol abuse/relapse (adding new findings in the field of alcohol problems and therapies). Findings showed that desire thinking predicted craving and binge drinking in both samples and predict relapse at follow ups in people with severe alcohol use disorder. Furthermore, the components of desire thinking were found to be differently implicated in alcohol problems (imaginal prefiguration predicts craving levels at follow-up and verbal perseveration were found to be the predictor of binge drinking frequency at follow-up.
As a whole, the results of the studies reported in this thesis will provide support for the central role of desire thinking in increasing craving experience and leading to alcohol use (over and above the level of craving). In other words, engaging in desire thinking gradually leads to an escalation of craving increasing the salience of using alcohol as a means of attaining control. According with this view, therapies should aim at helping patients reducing their desire thinking and mental activities related to imagining how to reach and use their desired target
Effective Sample Size for Importance Sampling based on discrepancy measures
The Effective Sample Size (ESS) is an important measure of efficiency of
Monte Carlo methods such as Markov Chain Monte Carlo (MCMC) and Importance
Sampling (IS) techniques. In the IS context, an approximation
of the theoretical ESS definition is widely applied, involving the inverse of
the sum of the squares of the normalized importance weights. This formula,
, has become an essential piece within Sequential Monte Carlo
(SMC) methods, to assess the convenience of a resampling step. From another
perspective, the expression is related to the Euclidean
distance between the probability mass described by the normalized weights and
the discrete uniform probability mass function (pmf). In this work, we derive
other possible ESS functions based on different discrepancy measures between
these two pmfs. Several examples are provided involving, for instance, the
geometric mean of the weights, the discrete entropy (including theperplexity
measure, already proposed in literature) and the Gini coefficient among others.
We list five theoretical requirements which a generic ESS function should
satisfy, allowing us to classify different ESS measures. We also compare the
most promising ones by means of numerical simulations
On the strategy frequency problem in batch Minority Games
Ergodic stationary states of Minority Games with S strategies per agent can
be characterised in terms of the asymptotic probabilities with which
an agent uses of his strategies. We propose here a simple and general
method to calculate these quantities in batch canonical and grand-canonical
models. Known analytic theories are easily recovered as limiting cases and, as
a further application, the strategy frequency problem for the batch
grand-canonical Minority Game with S=2 is solved. The generalization of these
ideas to multi-asset models is also presented. Though similarly based on
response function techniques, our approach is alternative to the one recently
employed by Shayeghi and Coolen for canonical batch Minority Games with
arbitrary number of strategies.Comment: 17 page
Theory of controlled quantum dynamics
We introduce a general formalism, based on the stochastic formulation of
quantum mechanics, to obtain localized quasi-classical wave packets as
dynamically controlled systems, for arbitrary anharmonic potentials. The
control is in general linear, and it amounts to introduce additional quadratic
and linear time-dependent terms to the given potential. In this way one can
construct for general systems either coherent packets moving with constant
dispersion, or dynamically squeezed packets whose spreading remains bounded for
all times. In the standard operatorial framework our scheme corresponds to a
suitable generalization of the displacement and scaling operators that generate
the coherent and squeezed states of the harmonic oscillator.Comment: LaTeX, A4wide, 28 pages, no figures. To appear in J. Phys. A: Math.
Gen., April 199
Dymanics of Generalized Coherent States
We show that generalized coherent states follow Schr\"{o}dinger dynamics in
time-dependent potentials. The normalized wave-packets follow a classical
evolution without spreading; in turn, the Schr\"{o}dinger potential depends on
the state through the classical trajectory. This feedback mechanism with
continuous dynamical re-adjustement allows the packets to remain coherent
indefinetely.Comment: 8 pages, plain latex, no figure
Parallel Metropolis chains with cooperative adaptation
Monte Carlo methods, such as Markov chain Monte Carlo (MCMC) algorithms, have
become very popular in signal processing over the last years. In this work, we
introduce a novel MCMC scheme where parallel MCMC chains interact, adapting
cooperatively the parameters of their proposal functions. Furthermore, the
novel algorithm distributes the computational effort adaptively, rewarding the
chains which are providing better performance and, possibly even stopping other
ones. These extinct chains can be reactivated if the algorithm considers
necessary. Numerical simulations shows the benefits of the novel scheme
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