The Addiction-Stroop test: Theoretical considerations and procedural recommendations.

Decisions about using addictive substances are influenced by distractions by addiction-related stimuli, of which the user might be unaware. The addiction-Stroop task is a paradigm used to assess this distraction. The empirical evidence for the addiction-Stroop effect is critically reviewed, and meta-analyses of alcohol-related and smoking-related studies are presented. Studies finding the strongest effects were those in which participants had strong current concerns about an addictive substance or such concerns were highlighted through experimental manipulations, especially those depriving participants of the substance. Theories to account for addiction-related attentional bias are discussed, of which the motivational theory of current concerns appears to provide the most complete account of the phenomenon. Recommendations are made for maximizing the precision of the addiction-Stroop test in future research. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A NON-PARAMETRIC APPROACH TO SIMPLICITY CLUSTERING

The simplicity principle—an updating of Ockham's razor to take into account modern information theory—states that the preferred theory for a set of data is the one that allows for the most efficient encoding of the data. We consider this in the context of classification, or clustering, as a data reduction technique that helps describe a set of objects by dividing the objects into groups. The simplicity model we present favors clusters such that the similarity of the items in the clusters is maximal, while the similarity of items between clusters is minimal. Several novel features of our clustering criterion make it especially appropriate for clustering of data derived from, psychological procedures (e.g., similarity ratings): It is non-parametric, and may be applied in situations where the metric axioms are violated without requiring (information-forgetting) transformation procedures. We illustrate the use of the criterion with a selection of data sets. A distinctive aspect of this research is that it motivates a clustering algorithm from psychological principles.     

The role of similarity in artificial grammar learning.

The authors examine the role of similarity in artificial grammar learning (AGL; A. S. Reber, 1989). A standard finite-state language was used to create stimuli that were arrangements of embedded geometric shapes (Experiment 1), connected lines (Experiment 2), and sequences of shapes (Experiment 3). Main effects for well-known predictors from the literature (grammaticality, associative global and anchor chunk strength, novel global and anchor chunk strength, length of items, and edit distance) were observed, thus replicating previous work. However, the authors extend previous research by using a widely known similarity-based exemplar model of categorization (the generalized context model; R. M. Nosofsky, 1989) to fit grammaticality judgments, by nested regression analyses. The results suggest that any explanation of AGL that is based on the existing theories is incomplete without a similarity process as well. Also, the results provide a foundation for further interpreting AGL in the wider context of categorization research. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Feast your eyes: Hunger and trait reward drive predict attentional bias for food cues.

Appraisal theories of emotion predict that the relevance of a stimulus to a person's needs and goals influences attentional allocation. We used a modified visual probe task to examine the influence of hunger and trait reward drive on food-related attentional bias. Both hunger and trait reward drive predicted degree of attentional “disengagement” from food images at short (100 ms), but not long (500, 2,000 ms) stimulus durations. Effects of hunger were found for both bland and appetizing foods, while effects of reward drive were restricted to appetizing foods. Our findings extend previous research showing delayed “disengagement” from threat-related stimuli, suggesting that both organismic- and goal-relevance are key biasing factors in attentional competition. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Distributional-based texture classification using non-parametric statistics

Texture classification is an important problem in image analysis. In the present study, an efficient strategy for classifying texture images is introduced and examined within a distributional-statistical framework. Our approach incorporates the multivariate Wald–Wolfowitz test (WW-test), a non-parametric statistical test that measures the similarity between two different sets of multivariate data, which is utilized here for comparing texture distributions. By summarizing the texture information using standard feature extraction methodologies, the similarity measure provides a comprehensive estimate of the match between different images based on graph theory. The proposed “distributional metric” is shown to handle efficiently the texture-space dimensionality and the limited sample size drawn from a given image. The experimental results, from the application on a typical texture database, clearly demonstrate the effectiveness of our approach and its superiority over other well-established texture distribution (dis)similarity metrics. In addition, its performance is used to evaluate several approaches for texture representation. Even though the classification results are obtained on grayscale images, a direct extension to color-based ones can be straightforward.

The Simplicity and Power model for inductive inference

With this paper we wish to present a simplicity (informally ‘simple explanations are the best’) formalism that is easily and directly applicable to modeling problems in cognitive science. While simplicity has been extensively advocated as a psychologically relevant principle, a general modeling formalism has been lacking. The Simplicity and Power model (SP) is a particular simplicity-based framework, that has been supported in machine learning (Wolff, Unifying computing and cognition: the SP theory and its applications, 2006). We propose its utility in cognitive modeling. For illustration, we provide SP demonstrations of the trade-off between encoding with whole exemplars versus parts of stimuli in learning and the effect of wide versus narrow distributions in categorization. In both cases, SP computations show how simplicity can account for these contrasts, in terms of how the frequency of individual exemplars in training compares to the frequency of their constituent parts.     

Predicting category intuitiveness with the rational model, the simplicity model, and the generalized context model.

Na?ve observers typically perceive some groupings for a set of stimuli as more intuitive than others. The problem of predicting category intuitiveness has been historically considered the remit of models of unsupervised categorization. In contrast, this article develops a measure of category intuitiveness from one of the most widely supported models of supervised categorization, the generalized context model (GCM). Considering different category assignments for a set of instances, the authors asked how well the GCM can predict the classification of each instance on the basis of all the other instances. The category assignment that results in the smallest prediction error is interpreted as the most intuitive for the GCM—the authors refer to this way of applying the GCM as “unsupervised GCM.” The authors systematically compared predictions of category intuitiveness from the unsupervised GCM and two models of unsupervised categorization: the simplicity model and the rational model. The unsupervised GCM compared favorably with the simplicity model and the rational model. This success of the unsupervised GCM illustrates that the distinction between supervised and unsupervised categorization may need to be reconsidered. However, no model emerged as clearly superior, indicating that there is more work to be done in understanding and modeling category intuitiveness. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

L-3,4-dihydroxyphenylalanine increases the quantal size of exocytotic dopamine release in vitro

The catecholamine precursor L-3,4-dihydroxyphenylalanine (L-DOPA) is used to augment striatal dopamine (DA), although its mechanism of altering neurotransmission is not well understood. We observed the effects of L-DOPA on catecholamine release in ventral midbrain neuron and PC12 pheochromocytoma cell line cultures. In ventral midbrain neuron cultures exposed to 40 mM potassium-containing media, L-DOPA (100 microM for 1 h) increased DA release by > 10-fold. The elevated extracellular DA levels were not significantly blocked by the DA/norepinephrine transport inhibitor nomifensine, demonstrating that reverse transport through catecholamine-uptake carriers plays little role in this release. In PC12 cells, where DA release from individual secretory vesicles can be observed, L-DOPA (50 microM for 1 h) elevated DA release in high-potassium media by 370%. Amperometric measurements demonstrated that L-DOPA (50 microM for 40-70 min) did not raise the frequency of vesicular exocytosis but increased the average size of quantal release to at least 250% of control levels. Together, these findings suggest that L-DOPA can increase stimulation-dependent transmitter release from DA cells by augmenting cytosolic neurotransmitter, leading to increased quantal size.     

A quantum theoretical explanation for probability judgment errors.

A quantum probability model is introduced and used to explain human probability judgment errors including the conjunction and disjunction fallacies, averaging effects, unpacking effects, and order effects on inference. On the one hand, quantum theory is similar to other categorization and memory models of cognition in that it relies on vector spaces defined by features and similarities between vectors to determine probability judgments. On the other hand, quantum probability theory is a generalization of Bayesian probability theory because it is based on a set of (von Neumann) axioms that relax some of the classic (Kolmogorov) axioms. The quantum model is compared and contrasted with other competing explanations for these judgment errors, including the anchoring and adjustment model for probability judgments. In the quantum model, a new fundamental concept in cognition is advanced—the compatibility versus incompatibility of questions and the effect this can have on the sequential order of judgments. We conclude that quantum information-processing principles provide a viable and promising new way to understand human judgment and reasoning. (PsycINFO Database Record (c) 2011 APA, all rights reserved)