Search

Although substance use disorders are widely known to be influenced by myriad etiologic factors, recent research promotes the notion that liability toward addiction broadly construed can be described by a single, unitary dimension that we term “general addiction liability.” Here, we revisit the concept of general addiction liability by placing it at greater theoretical and empirical risk. To do so, we used data from two epidemiologic samples (Nrange = 262–8,552) and employed varied quantitative methods to examine the associations between alcohol, cannabis, tobacco, and opioid use disorders. We did not find strong evidence for general addiction liability. Nevertheless, consequence-based features (e.g., social/interpersonal harm, hazardous use) tended to form cross-substance connections. We contextualize our findings in the broader literature on addiction liability and offer several explanations for why we and others arrive at competing conclusions with respect to the robustness and nature of general addiction liability.

Across various structured diagnostic instruments, the criteria used to diagnose alcohol use disorder (AUD) are not assessed consistently. For example, different instruments often pose questions that reflect different thresholds of the underlying symptoms. We consider the criteria for cravingand the inability to cut down or stop drinkingto demonstrate the influence of using different thresholds for a positive symptom endorsement with respect to the estimated edges of a symptom network. Results indicate that the utilization of these differing thresholds leads to significant differences in edge weights. Generally, higher thresholds relate more strongly to lower prevalence rate criteria and the reverse for lower thresholds. These findings have implications for reproducibility of effects in symptom networks and their generalization across studies.

Key to an understanding of alcohol use disorder (AUD) are the drinking-related reductions that begin in young adulthood and continue throughout the adult lifespan. Research is needed to precisely characterize the form of these reductions, including possible developmental differences across the lifespan. Using U.S.-representative data, we estimated multiple-group Markov models characterizing longitudinal transitions among five drinking statuses and differences in transition patterns across six adult age periods. While past research indicates relative developmental stability in overallAUD-desistance rates, we found far higher rates of Severe AUD desistance in young adulthood relative to later ages. Especially considering the dramatic change reflected by Severe AUD desistance (from 6+ symptoms to 0–1 symptoms), this result indicates a substantial developmental shift, with Severe AUD desistance rates peaking at 43% to 50% across ages 25 to 34 and then dropping to 22% to 24% across ages 35 to 55. We discuss implications regarding practical significance of young-adult “maturing out” and predictions regarding lifespan variability in desistance mechanisms.

Deterministic blockmodeling of a two-mode binary network matrix based on structural equivalence is a well-known problem in the social network literature. Whether implemented in a standalone fashion, or embedded within a metaheuristic framework, a popular relocation heuristic (RH) has served as the principal solution tool for this problem. In this paper, we establish that a two-mode KL-median heuristic (TMKLMedH) seeks to optimize the same criterion as the RH for deterministic blockmodeling. The TMKLMedH runs much faster than the RH, so many more restarts of the TMKLMedH can be accomplished when the two methods are constrained to the same time limit. Three computational comparisons of RH and TMKLMedH were conducted using both synthetic and real-world networks. In all three comparisons, the superiority of TMKLMedH was unequivocal.

Across various cultures there are robust stereotypes regarding how alcohol intoxication alters individuals’ normative personalities. However, whether these stereotypes are rooted in genuine average effects or in salient, socially proliferated exemplars remains unclear. The current study tested if differences between sober and intoxicated personality expression can be observed reliably by trained raters during a drinking episode. Participants (N= 156), half of whom received alcohol, attended laboratory sessions in same-gender friend groups and engaged in activities designed to elicit a range of personality expression. Participants completed self-reports of their “typical” sober and drunk personalities 2 weeks prior to their sessions and via two short measures during the session. In addition, participants were recorded and rated by multiple (range = 5–17) trained raters using three personality measures. Self-perceptions of sober-to-drunk personality differences were more pervasive than observer-perceptions, but alcohol-induced changes in Extraversion, specifically, were robust across measures and reporters.

Variability in sensitivity to the acute effects of alcohol is an important risk factor for the development of alcohol use disorder (AUD). The most commonly used retrospective self‐report measure of sensitivity, the Self‐Rating of the Effects of Alcohol (SRE) form, queries a limited number of alcohol effects and relies on respondents’ ability to recall experiences that might have occurred in the distant past. Here, we investigated the construct validity of an alternative measure that queries a larger number of alcohol effects, the Alcohol Sensitivity Questionnaire (ASQ), and compared it to the SREin predicting momentary subjective responses to an acute dose of alcohol. Healthy young adults (N = 423) completed the SREand the ASQand then were randomly assigned to consume either alcohol or a placebo beverage (between‐subjects manipulation). Stimulation and sedation (Biphasic Alcohol Effects Scale) and subjective intoxication were measured multiple times after drinking. Hierarchical linear models showed that the ASQreliably predicted each of these outcomes following alcohol but not placebo consumption, provided unique prediction beyond that associated with differences in recent alcohol involvement, and was preferred over the SRE(in terms of model fit) in direct model comparisons of stimulation and sedation. The ASQcompared favorably with the better‐known SREin predicting increased stimulation and reduced sedation following an acute alcohol challenge. The ASQappears to be a valid self‐report measure of alcohol sensitivity and therefore holds promise for identifying individuals at‐risk for AUDand related problems. The Alcohol Sensitivity Questionnaire (ASQ), a new retrospective, self‐report measure of sensitivity to alcohol's effects, outperformed the better‐known Self‐Rating of the Effects of Alcohol (SRE) form in predicting changes in stimulation and sedation following acute alcohol challenge. Given its assessment of a larger number and greater diversity of effects, the ASQrepresents a valid alternative to the SREfor characterizing individual differences in risk for alcohol use disorder stemming from alcohol sensitivity.

AbstractThis paper presents a variable neighborhood search (VNS) algorithm that is specially designed for the blockmodeling of two-mode binary network matrices in accordance with structural equivalence. Computational results for 768 synthetic test networks revealed that the VNS heuristic outperformed a relocation heuristic (RH) and a tabu search (TS) method for the same problem. Next, the three heuristics were applied to two-mode network data pertaining to the votes of member countries on resolutions in the United Nations General Assembly. A comparative analysis revealed that the VNS heuristic often provided slightly better criterion function values than RH and TS, and that these small differences in criterion function values could sometimes be associated with substantial differences in the actual partitions obtained. Overall, the results suggest that the VNS heuristic is a promising approach for blockmodeling of two-mode binary networks. Recommendations for extensions to stochastic blockmodeling applications are provided.

Abstract: Variable selection is a venerable problem in multivariate statistics. In the context of discriminant analysis, the goal is to select a subset of variables that accomplishes one of two objectives: (1) the provision of a parsimonious, yet descriptive, representation of group structure, or (2) the ability to correctly allocate new cases to groups. We present an exact (branch-and-bound) algorithm for variable selection in linear discriminant analysis that identifies subsets of variables that minimize Wilks’ . An important feature of this algorithm is a variable reordering scheme that greatly reduces computation time. We also present an approximate procedure based on tabu search, which can be implemented for a variety of objective criteria designed for either the descriptive or allocation goals associated with discriminant analysis. The tabu search heuristic is especially useful for maximizing the hit ratio (i.e., the percentage of correctly classified cases). Computational results for the proposed methods are provided for two data sets from the literature. [Copyright &y& Elsevier]

Background: Research has documented normative patterns of personality change during emerging and young adulthood that reflect decreases in traits associated with substance use, such as impulsivity. However, evidence suggests variability in these developmental changes.

Abstract: Establishing blockmodels for one- and two-mode binary network matrices has typically been accomplished using multiple restarts of heuristic algorithms that minimize functions of inconsistency with an ideal block structure. Although these algorithms likely yield exceptional performance, they are not assured to provide blockmodels that optimize the functional indices. In this paper, we present integer programming models that, for a prespecified image matrix, can produce guaranteed optimal solutions for matrices of nontrivial size. Accordingly, analysts performing a confirmatory analysis of a prespecified blockmodel structure can apply our models directly to obtain an optimal solution. In exploratory cases where a blockmodel structure is not prespecified, we recommend a two-stage procedure, where a heuristic method is first used to identify an image matrix and the integer program is subsequently formulated and solved to identify the optimal solution for that image matrix. Although best suited for ideal block structures associated with structural equivalence, the integer programming models have the flexibility to accommodate functional indices pertaining to regular equivalence. Computational results are reported for a variety of one- and two-mode matrices from the blockmodeling literature. [Copyright &y& Elsevier]

Abstract: The clustering of two-mode proximity matrices is a challenging combinatorial optimization problem that has important applications in the quantitative social sciences. We focus on one particular type of problem related to the clustering of a two-mode binary matrix, which is relevant to the establishment of generalized blockmodels for social networks. In this context, clusters for the rows of the two-mode matrix intersect with clusters of the columns to form blocks, which should ideally be either complete (all 1s) or null (all 0s). A new procedure based on variable neighborhood search is presented and compared to an existing two-mode K-means clustering algorithm. The new procedure generally provided slightly greater explained variation; however, both methods yielded exceptional recovery of cluster structure. [Copyright &y& Elsevier]

Abstract: We show that seriation of the rows and columns of a two-mode, binary matrix can be an effective method for producing a reordering of the matrix that reveals a blockmodel structure of the data. The objective criterion of the seriation process is based on Robinson patterning of matrix elements. The key advantages of the proposed method are: (a) it can be used in conjunction with existing two-mode blockmodeling algorithms by facilitating selection of the number of classes for the rows and columns of the matrix and the appropriate types of ideal blocks; (b) the model uses a well-grounded index based on Robinson structure, (c) guaranteed optimal solutions can be obtained for problems of practical size, and (d) the seriation method is frequently capable of producing a solution that has a substantive interpretation with respect to the orderings of the row objects and column items. [Copyright &y& Elsevier]

Chronic heart failure (CHF) is associated with reduced functional capacity and quality of life among older adults. Complex CHF medication regimens challenge older patients' ability to adhere to these regimens, in part because of cognitive declines and poor communication. We developed patient-centered instructions for CHF medications as part of a pharmacy-based educational intervention to improve health-related outcomes and adherence among older adults with heart failure. The present paper reports a preliminary study to test whether the instructions were easy to understand compared to instructions for the same medications available in a large chain pharmacy. Thirty-two patients varying in education, literacy, and cognitive abilities were presented instructions with patient-centered or standard formats for familiar and unfamiliar medications. Answer time and accuracy for questions were measured while patients looked at the instruction. The patient-centered instructions were understood more quickly than the standard instructions. They were also understood more accurately for unfamiliar medications, while the standard instructions were understood more accurately for familiar medications. These findings suggest that patient-centered instructions may improve patients' medication knowledge compared to standard pharmacy instructions.

Suicide rates among military-connected populations have increased over the past 15 years. Meta-analytic studies indicate prediction of suicide outcomes is lacking. Machine-learning approaches have been promoted to enhance classification models for suicide-related outcomes. In the present study, we compared the performance of three primary machine-learning approaches (i.e., elastic net, random forests, stacked ensembles) and a traditional statistical approach, generalized linear modeling (i.e., logistic regression), to classify suicide thoughts and behaviors using data from the Military Suicide Research Consortium’s Common Data Elements (CDE; n= 5,977–6,058 across outcomes). Models were informed by (a) selected items from the CDE or (b) factor scores based on exploratory and confirmatory factor analyses on the selected CDE items. Results indicated similar classification performance across models and sets of features. In this study, we suggest the need for robust evidence before adopting more complex classification models and identify measures that are particularly relevant in classifying suicide-related outcomes.

It has been established in the fields of computer science and physics that many NP-hard and NP-complete graph-theoretic problems can be formulated and solved as Ising spin models. We discuss several problems that have a history in mathematical psychology, most notably max-cut clustering, graph coloring, a linear ordering problem related to paired comparison ranking and directed acyclic graphs, and the problem of finding a minimum subset of points necessary to contain another point within a convex hull. New Ising spin models are presented for the latter two problems. Additionally, we provide MATLAB software programs for obtaining solutions via enumeration of all spin ensembles (when computationally feasible) and simulated annealing. We are not claiming that the Ising spin model should be the preferred approach for formulating and solving graph-theoretic problems on conventional digital computers but it does provide a unifying framework for these problems, which may lead to future computational improvements. Recent progress in the development of quantum computing architecture has shown that Ising spin models can afford enormous improvements in algorithm efficiency when implemented on these platforms, potentially leading to widespread use of the methodology in the future. • Ising spin optimization problems in quantitative psychology. • Novel Ising spin model for linear ordering problem. • Novel Ising spin model for minimum-subset convex hulls. • MATLAB simulated annealing code for optimization via Ising spins. [ABSTRACT FROM AUTHOR]