The formation of linked perceptual classes

Multiple-exemplar training with stimuli in four domains induced two new fill-based (A1' and A2') and satellite-image-based (B1' and B2') perceptual classes. Conditional discriminations were established between the endpoints of the A1' and B1' classes as well as the A2' and B2' classes. The emergence of linked perceptual classes was evaluated by the performances occasioned by nine cross-class probes that contained fill variants as samples and satellite variants as comparisons, along with nine other cross-class probes that consisted of satellite variants as samples and fill variants as comparisons. The 18 probes were first presented serially and then concurrently. Class-consistent responding indicated the emergence of linked perceptual classes. Of the linked perceptual classes, 70% emerged during the initial serial test. An additional 20% of the linked perceptual classes emerged during the subsequently presented concurrent test block. Thus, linked perceptual classes emerged on an immediate or delayed basis. Linked perceptual classes, then, share structural and fuctional similarities with equivalence classes, generalized equivalence classes, cross-modal classes, and complex maturally occurring categories, and may clarify processes such as intersensory perception.     

Analyzing driver's response to the yellow onset at signalized intersections

Intersections are critical points within the highway system at which the risk of crashes increases. This study seeks to better understand drivers’ behavior at an intersection by examining the relationship between their observed driving behavior, psychological attributes and decision to proceed through an intersection. A driving simulator and self-report questionnaire were used to understand driver decision-making at the onset of the yellow phase across several signalized intersections. The simulator measured driving outcomes such as speed, braking, and throttle as drivers cross through four increasingly difficult intersections. The questionnaires measured demographics, psychological traits including mindfulness and impulsiveness along with self-reported driving behaviors. A total of 102 participants completed the questionnaire as well as the driving simulator experiment. Hierarchical clustering served to classify drivers into four groups on the basis of their observed driving in the simulator: the safest drivers, safe drivers, speed demons, and aggressive drivers. These driving styles moderated the relationship between the drivers’ psychological traits and their decision to stop or proceed at each intersection. Results showed that mindfulness was highly related to the safest drivers’ decision to stop at the first intersection, while impulsiveness and anxiety were related to the speed demons’ decision to stop at the third intersection. These findings lay a strong foundation for developing progressive educational campaigns incorporating driver psychology in their methodology. Findings also provide support for research linking driving performance and psychological traits with implications for intersection design.     

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.