Making the future memorable: The phenomenology of remembered future events

Although our ability to remember future simulations conveys an adaptive advantage, enabling us to better prepare for upcoming events, the factors influencing the memorability of future simulations are not clear. In this study, participants generated future simulations that combined specific people, places and objects from memory, and for each trial, made a series of phenomenological ratings about the event components and the simulation as a whole. Memory for simulations was later assessed using a cued-recall test. We used multilevel modelling to determine whether the phenomenological qualities of event components (familiarity, emotionality and significance) and simulations (detail, plausibility) were predictive of whether the simulation was successfully encoded and later accessible. Our results demonstrate that person familiarity, detail and plausibility were significant predictors of whether a given future simulation was encoded into memory and later accessible. These findings suggest that scaffolding future simulations with pre-existing episodic memories is the path to a memorable future.     

多层多项Logit模型：原理与应用

常用的多层线性模型要求因变量是服从正态分布的连续变量,却无法处理因变量为离散变量的嵌套数据.多层多项Logit模型能够处理因变量是无序多分类的多层嵌套数据,但这一模型在我国心理学研究中却鲜有介绍和应用.研究简要介绍了多层多项Logit模型的原理、参数估计和假设检验,然后分别用该模型和传统Logistic回归模型探讨个体因素和区域因素对35721名学生进入不同高校就读机会的影响并对结果进行了解释,结果表明多层多项Logit模型比传统Logistic回归模型更拟合数据.     

Gender differences in the mean level,variability, and profile shape of student achievement: Results from 41 countries

A domain-specific hierarchical conceptualization of mathematics achievement can be represented by the standard psychometric model in which a single latent dimension accounts for observed individual differences in scores on the respective subdomains (e.g., quantity). Alternatively, a fully hierarchical conceptualization of achievement can be represented by a nested-factor model in which individual differences in subdomain-specific scores are explained by both general student achievement and specific mathematics achievement. The authors applied both models to study the gender similarity hypothesis, the greater male variability hypothesis, and the masking hypothesis, which predicts that gender differences in general student achievement mask gender differences in both the means and the variability of specific mathematics achievement. Representative data were obtained from 275,369 15-year-old students in 41 countries. The results supported these hypotheses in most countries, demonstrating that a fully hierarchical conceptualization of achievement in terms of the nested-factor model significantly contributes to a better understanding of gender differences in the mean level, variability, and shape of students' achievement profiles.     

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 model decomposition of the BART: Assessment and application

The Balloon Analogue Risk Task, or BART, aims to measure risk taking behavior in a controlled setting. In order to quantify the processes that underlie performance on the BART, Wallsten, Pleskac, and Lejuez (2005) proposed a series of mathematical models whose parameters have a clear psychological interpretation. Here we examine a 2-parameter simplification of Wallsten et al.’s preferred 4-parameter model. A parameter recovery study shows that — with plausible restrictions on the number of participants and trials — both parameters (i.e., risk taking γ⁺ and response consistency β) can be estimated accurately. To demonstrate how the 2-parameter model can be used in practice, we implemented a Bayesian hierarchical version and applied it to an empirical data set in which participants performed the BART following various amounts of alcohol intake.     

Neighborhood Predictors of Intimate Partner Violence: A Theory‐Informed Analysis Using Hierarchical Linear Modeling

Due to high prevalence rates and deleterious effects on individuals, families, and communities, intimate partner violence (IPV) is a significant public health problem. Because IPV occurs in the context of communities and neighborhoods, research must examine the broader environment in addition to individual‐level factors to successfully facilitate behavior change. Drawing from the Social Determinants of Health framework and Social Disorganization Theory, neighborhood predictors of IPV were tested using hierarchical linear modeling. Results indicated that concentrated disadvantage and female‐to‐male partner violence were robust predictors of women's IPV victimization. Implications for theory, practice, and policy, and future research are discussed.     

Children's Representation and Imitation of Events: How Goal Organization Influences 3‐Year‐Old Children's Memory for Action Sequences

Children's imitation of adults plays a prominent role in human cognitive development. However, few studies have investigated how children represent the complex structure of observed actions which underlies their imitation. We integrate theories of action segmentation, memory, and imitation to investigate whether children's event representation is organized according to veridical serial order or a higher level goal structure. Children were randomly assigned to learn novel event sequences either through interactive hands‐on experience (Study 1) or via storybook (Study 2). Results demonstrate that children's representation of observed actions is organized according to higher level goals, even at the cost of representing the veridical temporal ordering of the sequence. We argue that prioritizing goal structure enhances event memory, and that this mental organization is a key mechanism of social‐cognitive development in real‐world, dynamic environments. It supports cultural learning and imitation in ecologically valid settings when social agents are multitasking and not demonstrating one isolated goal at a time.     

Exploring the effects of congruence and Holland’s personality codes on job satisfaction: An application of hierarchical linear modeling techniques

This study applied hierarchical linear modeling to investigate the effect of congruence on intrinsic and extrinsic aspects of job satisfaction. Particular focus was given to differences in job satisfaction by gender and by Holland’s first-letter codes. The study sample included nationally represented 1462 female and 1280 male college graduates who held full-time jobs in 2000. Congruence was measured by using actual occupational and academic major codes. After controlling for the differences across three-letter Holland personality codes, results indicate that job satisfaction was largely a function of individual attributes rather than disparities in personality types. Holland’s congruence was most effective in explaining intrinsic dimensions of job satisfaction for females, and less effective for extrinsic job satisfaction for both genders. Social females and males were less satisfied with extrinsic satisfaction, while Conventional and Social females were more satisfied with intrinsic features in their vocational environments.     

The Hierarchical Model of Approach-Avoidance Motivation

Approach motivation is the energization of behavior by, or the direction of behavior toward, positive stimuli (objects, events, possibilities), whereas avoidance motivation is the energization of behavior by, or the direction of behavior away from, negative stimuli (objects, events, possibilities). In this article, I provide a brief overview of this distinction between approach and avoidance motivation. In addition, I provide a brief overview of a model of motivation in which this approach-avoidance distinction plays an integral role—the hierarchical model of approach-avoidance motivation.     

Exemplars, prototypes, similarities, and rules in category representation: an example of hierarchical bayesian analysis

This article demonstrates the potential of using hierarchical Bayesian methods to relate models and data in the cognitive sciences. This is done using a worked example that considers an existing model of category representation, the Varying Abstraction Model (VAM), which attempts to infer the representations people use from their behavior in category learning tasks. The VAM allows for a wide variety of category representations to be inferred, but this article shows how a hierarchical Bayesian analysis can provide a unifying explanation of the representational possibilities using 2 parameters. One parameter controls the emphasis on abstraction in category representations, and the other controls the emphasis on similarity. Using 30 previously published data sets, this work shows how inferences about these parameters, and about the category representations they generate, can be used to evaluate data in terms of the ongoing exemplar versus prototype and similarity versus rules debates in the literature. Using this concrete example, this article emphasizes the advantages of hierarchical Bayesian models in converting model selection problems to parameter estimation problems, and providing one way of specifying theoretically based priors for competing models.