Construct Validity of the Balloon Analogue Risk Task (BART): Relationship with MDMA Use by Inner-City Drug Users in Residential Treatment

In a program of research assessing the validity of the balloon analogue risk task (BART; C. W. Lejuez et al., 2002) as a measure of risk taking, the BART was administered to a sample of inner-city drug users in residential treatment (n = 76). Construct validity of the BART was evaluated by measuring risk-taking behavior and 3,4-methylenedioxymethamphetamine (MDMA) use while controlling for self-reported impulsivity, sensation seeking, polysubstance use, and demographic variables. Supporting the construct validity of the BART, while controlling for interrelated variables in the context of logistic regression analyses, (BART) risk-taking propensity accounted for significant incremental variance in differentiating MDMA users from non-users. BART scores, polysubstance use, and younger age were most associated with MDMA use, and together these variables were associated with 91% classification accuracy in predicting MDMA use.     

Bayesian parameter estimation in the Expectancy Valence model of the Iowa gambling task

The purpose of the popular Iowa gambling task is to study decision making deficits in clinical populations by mimicking real-life decision making in an experimental context. Busemeyer and Stout [Busemeyer, J. R., & Stout, J. C. (2002). A contribution of cognitive decision models to clinical assessment: Decomposing performance on the Bechara gambling task. Psychological Assessment, 14, 253-262] proposed an “Expectancy Valence” reinforcement learning model that estimates three latent components which are assumed to jointly determine choice behavior in the Iowa gambling task: weighing of wins versus losses, memory for past payoffs, and response consistency. In this article we explore the statistical properties of the Expectancy Valence model. We first demonstrate the difficulty of applying the model on the level of a single participant, we then propose and implement a Bayesian hierarchical estimation procedure to coherently combine information from different participants, and we finally apply the Bayesian estimation procedure to data from an experiment designed to provide a test of specific influence.     

Revisiting the Effects of Anger on Risk‐Taking: Empirical and Meta‐Analytic Evidence for Differences Between Males and Females

That anger elicited in one situation can carry over to drive risky behavior in another situation has been described since the days of Aristotle. The present studies examine the mechanisms through which and the conditions under which such behavior occurs. Across three experiments, as well as a meta‐analytic synthesis of the data, results reveal that incidental anger is significantly more likely to drive risky decision making among males than among females. Moreover, the experiments document that, under certain circumstances, such risk‐taking pays off financially. Indeed, the present experiments demonstrate that, because the expected‐value‐maximizing strategy in these studies rewarded risk‐taking, angry‐male individuals earned more money than did both neutral‐emotion males and angry females. In sum, these studies found evidence for robust disparities between males and females for anger‐driven risk‐taking. Importantly, although men did not experience more anger than women, they did show a heightened tendency to respond to anger with risk‐taking. Published 2016. This article has been contributed to by US Government employees and their work is in the public domain in the USA.     

A hierarchical bayesian model of human decision-making on an optimal stopping problem

We consider human performance on an optimal stopping problem where people are presented with a list of numbers independently chosen from a uniform distribution. People are told how many numbers are in the list, and how they were chosen. People are then shown the numbers one at a time, and are instructed to choose the maximum, subject to the constraint that they must choose a number at the time it is presented, and any choice below the maximum is incorrect. We present empirical evidence that suggests people use threshold-based models to make decisions, choosing the first currently maximal number that exceeds a fixed threshold for that position in the list. We then develop a hierarchical generative account of this model family, and use Bayesian methods to learn about the parameters of the generative process, making inferences about the threshold decision models people use. We discuss the interesting aspects of human performance on the task, including the lack of learning, and the presence of large individual differences, and consider the possibility of extending the modeling framework to account for individual differences. We also use the modeling results to discuss the merits of hierarchical, generative and Bayesian models of cognitive processes more generally.     

Global Model Analysis of Cognitive Variability

Residual fluctuations produced in typical experimental methodologies are examined as correlated noises. The effective range of the correlations was assessed by determining whether the decay over look-back time is better described as a power law or exponential. Both of these decay laws contain free parameters and it is argued that it is not possible to distinguish their models on the basis of simple measures of goodness-of-fit. Global analyses that evaluate models on the basis of how well they generalize are conducted. The models are examined in terms of three constructs that all bear on generalization: cross-validity, flexibility, and representativeness. Quantitative assessment of a large ensemble of data suggests that the correlations decay over time as a power law. The conclusion is that human residual fluctuation is a correlated fractal.     

The Utility of Cognitive Plausibility in Language Acquisition Modeling: Evidence From Word Segmentation

The informativity of a computational model of language acquisition is directly related to how closely it approximates the actual acquisition task, sometimes referred to as the model's cognitive plausibility. We suggest that though every computational model necessarily idealizes the modeled task, an informative language acquisition model can aim to be cognitively plausible in multiple ways. We discuss these cognitive plausibility checkpoints generally and then apply them to a case study in word segmentation, investigating a promising Bayesian segmentation strategy. We incorporate cognitive plausibility by using an age‐appropriate unit of perceptual representation, evaluating the model output in terms of its utility, and incorporating cognitive constraints into the inference process. Our more cognitively plausible model shows a beneficial effect of cognitive constraints on segmentation performance. One interpretation of this effect is as a synergy between the naive theories of language structure that infants may have and the cognitive constraints that limit the fidelity of their inference processes, where less accurate inference approximations are better when the underlying assumptions about how words are generated are less accurate. More generally, these results highlight the utility of incorporating cognitive plausibility more fully into computational models of language acquisition.     

The form of the forgetting curve and the fate of memories

Psychologists have debated the form of the forgetting curve for over a century. We focus on resolving three problems that have blocked a clear answer on this issue. First, we analyzed data from a longitudinal experiment measuring cued recall and stem completion from 1 min to 28 days after study, with more observations per interval per participant than in previous studies. Second, we analyzed the data using hierarchical models, avoiding distortions due to averaging over participants. Third, we implemented the models in a Bayesian framework, enabling our analysis to account for the ability of candidate forgetting functions to imitate each other. An exponential function provided the best fit to individual participant data collected under both explicit and implicit retrieval instructions, but Bayesian model selection favored a power function. All analysis supported above chance asymptotic retention, suggesting that, despite quite brief study, storage of some memories was effectively permanent.     

A Bayesian hierarchical model for the measurement of working memory capacity

Working memory is the memory system that allows for conscious storage and manipulation of information. The capacity of working memory is extremely limited. Measurements of this limit, and what affects it, are critical to understanding working memory. Cowan (2001) and Pashler (1988) suggested applying multinomial tree models to data from change detection paradigms in order to estimate working memory capacity. Both Pashler and Cowan suggested simple formulas for estimating capacity with these models. However, in many cases, these simple formulas are inadequate, and may lead to inefficient or biased estimation of working memory capacity. I propose a Bayesian hierarchical alternative to the Pashler and Cowan formulas, and show that the hierarchical model outperforms the traditional formulas. The models are easy to use and appropriate for a wide range of experimental designs. An easy-to-use graphical user interface for fitting the hierarchical model to data is available.     

Cognitive Error Questionnaire (CEQ): Psychometric Properties and Factor Structure of the German Translation

A central component of Beck et al.’s (1979) cognitive theory of depression is faulty information processing reflected by so-called cognitive errors. These cognitive errors are the reason why depressed individuals systematically misinterpret the significance of events in a negative way. They are usually assessed with the application of the Cognitive Error Questionnaire (CEQ). This study examines the psychometric properties and factor structure of the German version of the CEQ in a sample of 796 volunteers at a German university. Results confirmed that the German CEQ has satisfactory to very good psychometric properties, like the American original. Confirmatory factor analyses demonstrated that a hierarchical four-factor model with four subscales and 1 s order factor fits the data best. Therefore, besides using the German CEQ in studies with German-speaking samples, the similarities in psychometric properties of the American and German CEQ allow for cross-cultural studies.     

Body-image thought processes: The development and initial validation of the Assessment of Body-Image Cognitive Distortions

No measures exist that specifically assess cognitive distortions related to body image per se, despite their theoretical and clinical significance. Most cognitive-distortion scales pertain to depression, anxiety, or eating disorders. Accordingly, the 37-item Assessment of Body-Image Cognitive Distortions (ABCD) was developed and validated in this study with a sample of 263 college women. The ABCD samples eight types of distorted thinking related to how persons process information about their physical appearance. Two 18-item parallel forms of the unidimensional measure were also constructed. All forms were highly internally consistent and relatively free from socially desirable responding. Convergent validity for all ABCD forms was established using several standardized measures of body image and eating attitudes. Multiple regression analysis showed that the ABCD was predictable from body-image evaluation, investment, and overweight preoccupation. The ABCD uniquely predicted body-image quality of life and disturbed eating attitudes above and beyond other body-image predictors. Heavier women and White women were more prone to body-image cognitive distortions than were thinner women and Black women. Finally, limitations of this preliminary study, directions for future research, and clinical implications are discussed.     

Resources masquerading as slots: Flexible allocation of visual working memory

Whether the capacity of visual working memory is better characterized by an item-based or a resource-based account continues to be keenly debated. Here, we propose that visual working memory is a flexible resource that is sometimes deployed in a slot-like manner. We develop a computational model that can either encode all items in a memory set, or encode only a subset of those items. A fixed-capacity mnemonic resource is divided among the items in memory. When fewer items are encoded, they are each remembered with higher fidelity, but at the cost of having to rely on an explicit guessing process when probed about an item that is not in memory. We use the new model to test the prediction that participants will more often encode the entire set of items when the demands on memory are predictable.     

Interpreting Silent Gesture: Cognitive Biases and Rational Inference in Emerging Language Systems

Natural languages make prolific use of conventional constituent‐ordering patterns to indicate “who did what to whom,” yet the mechanisms through which these regularities arise are not well understood. A series of recent experiments demonstrates that, when prompted to express meanings through silent gesture, people bypass native language conventions, revealing apparent biases underpinning word order usage, based on the semantic properties of the information to be conveyed. We extend the scope of these studies by focusing, experimentally and computationally, on the interpretation of silent gesture. We show cross‐linguistic experimental evidence that people use variability in constituent order as a cue to obtain different interpretations. To illuminate the computational principles that govern interpretation of non‐conventional communication, we derive a Bayesian model of interpretation via biased inductive inference and estimate these biases from the experimental data. Our analyses suggest people's interpretations balance the ambiguity that is characteristic of emerging language systems, with ordering preferences that are skewed and asymmetric, but defeasible.     

The Cognitive Architecture of Perceived Animacy: Intention,Attention, and Memory

Human vision supports social perception by efficiently detecting agents and extracting rich information about their actions, goals, and intentions. Here, we explore the cognitive architecture of perceived animacy by constructing Bayesian models that integrate domain‐specific hypotheses of social agency with domain‐general cognitive constraints on sensory, memory, and attentional processing. Our model posits that perceived animacy combines a bottom–up, feature‐based, parallel search for goal‐directed movements with a top–down selection process for intent inference. The interaction of these architecturally distinct processes makes perceived animacy fast, flexible, and yet cognitively efficient. In the context of chasing, in which a predator (the “wolf”) pursues a prey (the “sheep”), our model addresses the computational challenge of identifying target agents among varying numbers of distractor objects, despite a quadratic increase in the number of possible interactions as more objects appear in a scene. By comparing modeling results with human psychophysics in several studies, we show that the effectiveness and efficiency of human perceived animacy can be explained by a Bayesian ideal observer model with realistic cognitive constraints. These results provide an understanding of perceived animacy at the algorithmic level—how it is achieved by cognitive mechanisms such as attention and working memory, and how it can be integrated with higher‐level reasoning about social agency.     

Effects of hearing and vision impairments on the Montreal Cognitive Assessment

Many standardized measures of cognition include items that must be seen or heard. Nevertheless, it is not uncommon to overlook the possible effects of sensory impairment(s) on test scores. In the current study, we investigated whether sensory impairments could affect performance on a widely used screening tool, the Montreal Cognitive Assessment (MoCA). Three hundred and one older adults (mean age = 71 years) completed the MoCA and also hearing and vision tests. Half of the participants had normal hearing and vision, 38% impaired hearing, 5% impaired vision, and 7% had dual-sensory impairment. More participants with normal sensory acuity passed the MoCA compared to those with sensory loss, even after modifying scores to adjust for sensory factors. The results suggest that cognitive abilities may be underestimated if sensory problems are not considered and that people with sensory loss are at greater risk of cognitive decline.     

老年人认知功能的变化轨迹：基于潜变量增长模型的分析

摘 要 本研究采用中国老年健康影响因素跟踪调查（CLHLS）的4次数据（2002,2005,2008,2011）,对老年人认知功能的变化趋势以及影响因素进行了探讨。结果显示：（1）老年人认知功能在4次测查中呈非线性的下降趋势。（2）日常生活活动能力较低的个体其认知功能也较低;老年人读书年限越高,其认知功能水平越高;女性的认知功能水平低于男性;不饮酒的老年人认知功能低于饮酒的老年人。（3）读书年限与饮酒会正向预测模型的斜率。     

Investigating the relationship of working memory tasks and fluid intelligence tests by means of the fixed-links model in considering the impurity problem

The impurity of measures is considered as cause of erroneous interpretations of observed relationships. This paper concentrates on impurity with respect to the relationship between working memory and fluid intelligence. The means for the identification of impurity was the fixed-links model, which enabled the decomposition of variance into experimental and non-experimental parts. A substantial non-experimental part could be expected to signify impurity. In a sample of 345 participants error scores and reaction times, which were obtained by the Exchange Test, represented working memory, and Advanced Progressive Matrices served as measure of fluid intelligence. The four independent latent variables of the model associated with error scores and reaction times led to a multiple correlation .67 with the latent variable of fluid intelligence. However, there was impurity since the decomposition by means of the fixed-links model showed that only 45% of the common variance was due to working memory.     

测验相对拟合检验方法CVLL法在认知诊断中的拓展及应用

本文将IRT中表现较好的CVLL法引入到认知诊断领域,同时比较并分析CVLL及认知诊断领域已有的测验相对拟合检验统计量的表现,为实际工作者在认知诊断模型选用上提供方法学支持和借鉴。结果表明:CVLL的表现比其它传统测验相对拟合统计量要好;且当对Q矩阵进行误设时,该统计量也能选择较优的Q矩阵,说明CVLL在Q矩阵侦查上有较好的应用前景。