Constructing a short form of the Smoking Consequences Questionnaire with adolescents and young adults

The goal of the present studies was to construct and validate a short form of the 50-item Smoking Consequences Questionnaire (SCQ; T.H. Brandon & T. B. Baker, 1991), a measure of smoking outcome expectancies. In Study 1, a 21-item short form (S-SCQ) was derived from a sample of 107 young adults previously treated for substance abuse. In Study 2, the measure was cross-validated on 125 adolescents in treatment for substance abuse. Confirmatory factor analyses revealed good model fit and factorial invariance for the 4 S-SCQ subscales across both samples. Validation analyses on each sample found that subscale scores generally correlated significantly with smoking-related variables. The present studies provide initial evidence for the utility of the S-SCQ when used with young adults and adolescents.     

What the Liar Taught Achilles

Zeno's paradoxes of motion and the semantic paradoxes of the Liar have long been thought to have metaphorical affinities. There are, in fact, isomorphisms between variations of Zeno's paradoxes and variations of the Liar paradox in infinite-valued logic. Representing these paradoxes in dynamical systems theory reveals fractal images and provides other geometric ways of visualizing and conceptualizing the paradoxes.     

A computational and neuropsychological account of object-oriented behaviours in infancy

Infants under 7 months of age fail to reach behind an occluding screen to retrieve a desired toy even though they possess sufficient motor skills to do so. However, even by 3.5 months of age they show surprise if the solidity of the hidden toy is violated, suggesting that they know that the hidden toy still exists. We describe a connectionist model that learns to predict the position of objects and to initiate a response towards these objects. The model embodies the dual-route principle of object information processing characteristic of the cortex. One route develops a spatially invariant surface feature representation of the object whereas the other route develops a feature blind spatial–temporal representation of the object. The model provides an account of the developmental lag between infants’ knowledge of hidden objects and their ability to demonstrate that knowledge in an active retrieval task, in terms of the need to integrate information across multiple object representations using (associative) connectionist learning algorithms. Finally, the model predicts the presence of an early dissociation between infants’ ability to use surface features (e.g. colour) and spatial–temporal features (e.g. position) when reasoning about hidden objects. Evidence supporting this prediction has now been reported.     

One extreme or the other or perhaps the golden mean? Issues of spatial resolution in face processing

The findings of some of the key studies that have examined the contribution of different bands of spatial information, specified in terms of cycles per face, to face recognition, and identification are reviewed. Based on findings from studies of low-, high-, and band-pass filtering, it is concluded that neither low facial scales nor high facial scales are optimal. Instead, a center range of facial scales, approximately 8-16 cycles per face, appears to make the greatest contribution to the recognition process. A study of identification of band-pass filtered faces is reported that reinforces the view derived from the literature. This indicated that a band with a harmonic mean of 11.1 cycles per face provided the most efficient recognition, with speed and accuracy declining at lower and higher bands.     

QMPE: Estimating Lognormal, Wald, and Weibull RT distributions with a parameter-dependent lower bound

We describe and test quantile maximum probability estimator (QMPE), an open-source ANSI Fortran 90 program for response time distribution estimation.¹ QMPE enables users to estimate parameters for the ex-Gaussian and Gumbel (1958) distributions, along with three “shifted” distributions (i.e., distributions with a parameter-dependent lower bound): the Lognormal, Wald, and Weibull distributions. Estimation can be performed using either the standard continuous maximum likelihood (CML) method or quantile maximum probability (QMP; Heathcote & Brown, in press). We review the properties of each distribution and the theoretical evidence showing that CML estimates fail for some cases with shifted distributions, whereas QMP estimates do not. In cases in which CML does not fail, a Monte Carlo investigation showed that QMP estimates were usually as good, and in some cases better, than CML estimates. However, the Monte Carlo study also uncovered problems that can occur with both CML and QMP estimates, particularly when samples are small and skew is low, highlighting the difficulties of estimating distributions with parameter-dependent lower bounds.     

Divine Action,Resurrection, and the Transformation of the Universe

Abstract

The article examines the notion of self-organization and explores the reality of biological processes from an epistemological point of view. First, I briefly analyze what is currently regarded as one of the most important discoveries not only in physics, but also in biology—namely, complex systems and deterministic chaos; secondly, I offer some reflections on the new frontiers of contemporary biology— namely, functional genomics and systems biology. The central part of the article focuses on the epistemological transition from genetic determinism to the new conception of “meaning” as emergence.     

Experimental social innovation and client-centered job-seeking programs

The complexity of most social problems and a growing human services shortfall dictate that we reevaluate our approach to planned social change. Simplistic one-shot evaluations of social interventions are unlikely to yield the kind of information that will help us identify true social innovations, understand and refine their instrumental processes, and thereby optimize their effects. However, incremental progress in these areas is possible through the pursuit of ESI or other systematic and rigorous approaches to outcome research.     

Consequence Mining

The standard semantic definition of consequence with respect to a selected set X of symbols, in terms of truth preservation under replacement (Bolzano) or reinterpretation (Tarski) of symbols outside X, yields a function mapping X to a consequence relation \(\Rightarrow_X\). We investigate a function going in the other direction, thus extracting the constants of a given consequence relation, and we show that this function (a) retrieves the usual logical constants from the usual logical consequence relations, and (b) is an inverse to—more precisely, forms a Galois connection with—the Bolzano–Tarski function.     

TRACX: a recognition-based connectionist framework for sequence segmentation and chunk extraction

Individuals of all ages extract structure from the sequences of patterns they encounter in their environment, an ability that is at the very heart of cognition. Exactly what underlies this ability has been the subject of much debate over the years. A novel mechanism, implicit chunk recognition (ICR), is proposed for sequence segmentation and chunk extraction. The mechanism relies on the recognition of previously encountered subsequences (chunks) in the input rather than on the prediction of upcoming items in the input sequence. A connectionist autoassociator model of ICR, truncated recursive autoassociative chunk extractor (TRACX), is presented in which chunks are extracted by means of truncated recursion. The performance and robustness of the model is demonstrated in a series of 9 simulations of empirical data, covering a wide range of phenomena from the infant statistical learning and adult implicit learning literatures, as well as 2 simulations demonstrating the model's ability to generalize to new input and to develop internal representations whose structure reflects that of the items in the input sequence. TRACX outperforms PARSER (Perruchet & Vintner, 1998) and the simple recurrent network (SRN, Cleeremans & McClelland, 1991) in matching human sequence segmentation on existing data. A new study is presented exploring 8-month-olds' use of backward transitional probabilities to segment auditory sequences.