BackgroundAlcohol research may benefit from controlled and validated picture sets. We have constructed the Amsterdam Beverage Picture Set (ABPS), which was designed for alcohol research in general and cognitive bias measurement and modification in particular. Here, we first formulate a position on alcohol stimulus validity that prescribes that alcohol‐containing pictures, compared to nonalcohol‐containing pictures, should induce a stronger urge to drink in heavy drinkers than in light drinkers. Because a perceptually simple picture might induce stronger cognitive biases but the presence of a drinking context might induce a stronger urge to drink, the ABPS contains pictures with and without drinking context. By limiting drinking contexts to simple consumption scenes instead of real‐life scenes, complexity was minimized. A validation study was conducted to establish validity, to examine ABPS drinking contexts, and to explore the role of familiarity, valence, arousal, and control.MethodsTwo hundred ninety‐one psychology students completed the Alcohol Use Disorders Identification Test, as well as rating and recognition tasks for a subset of the ABPS pictures.ResultsThe ABPS was well‐recognized, familiar, and heavy drinkers reported a greater urge to drink in response to the alcohol‐containing pictures only. Alcohol presented in drinking context did not elicit a stronger urge to drink but was recognized more slowly than alcohol presented without context.ConclusionsThe ABPS was found to be valid, although pictures without context might be preferable for measuring cognitive biases than pictures with context. We discuss how an explicit approach to picture construction may aid in creating variations of the ABPS. Finally, we describe how ABPS adoption across studies may allow more reproducible and comparable results across paradigms, while allowing researchers to apply picture selection criteria that correspond to a wide range of theoretical positions. The latter is exemplified by ABPS derivatives and adoptions that are currently under way.

Web-based ABM training is ineffective in fostering cognitive bias reduction and continued smoking abstinence. However, the positive effects in heavy smokers-as indicated by exploratory subsample analyses-warrant further research into the potential of multiple sessions ABM training to foster continued smoking abstinence in heavy smokers who make a quit-attempt. (PsycINFO Database Record

Recently, it has been suggested that impairments in executive functioning might be risk factors for the onset of alcohol use rather than a result of heavy alcohol use. In the present study, we examined whether two aspects of executive functioning, working memory and response inhibition, predicted the first alcoholic drink and first binge drinking episode in young adolescents using discrete survival analyses. Adolescents were selected from several Dutch secondary schools including both mainstream and special education (externalizing behavioral problems). Participants were 534 adolescents between 12 and 14 years at baseline. Executive functioning and alcohol use were assessed four times over a period of two years. Working memory uniquely predicted the onset of first drink (p=.01) and first binge drinking episode (p=.04) while response inhibition only uniquely predicted the initiating of the first drink (p=.01). These results suggest that the association of executive functioning and alcohol consumption found in former studies cannot simply be interpreted as an effect of alcohol consumption, as weaknesses in executive functioning, found in alcohol naïve adolescents, predict the initiating of (binge) drinking. Though, prolonged and heavy alcohol use might further weaken already existing deficiencies.

Estimating the reliability of cognitive task datasets is commonly done via split-half methods. We review four methods that differ in how the trials are split into parts: a first-second half split, an odd-even trial split, a permutated split, and a Monte Carlo-based split. Additionally, each splitting method could be combined with stratification by task design. These methods are reviewed in terms of the degree to which they are confounded with four effects that may occur in cognitive tasks: effects of time, task design, trial sampling, and non-linear scoring. Based on the theoretical review, we recommend Monte Carlo splitting (possibly in combination with stratification by task design) as being the most robust method with respect to the four confounds considered. Next, we estimated the reliabilities of the main outcome variables from four cognitive task datasets, each (typically) scored with a different non-linear algorithm, by systematically applying each splitting method. Differences between methods were interpreted in terms of confounding effects inflating or attenuating reliability estimates. For three task datasets, our findings were consistent with our model of confounding effects. Evidence for confounding effects was strong for time and task design and weak for non-linear scoring. When confounding effects occurred, they attenuated reliability estimates. For one task dataset, findings were inconsistent with our model but they may offer indicators for assessing whether a split-half reliability estimate is appropriate. Additionally, we make suggestions on further research of reliability estimation, supported by a compendium R package that implements each of the splitting methods reviewed here.