Reversed effects in closed and open economies

Pigeons received food according to either fixed-interval, fixed-ratio, or random-interval schedules in both closed and open feeding economies. In the closed economy, they were not food deprived, they controlled the amount of food received at each meal, and they had no other source of food. In the open economy, each feeding bout consisted of one feeder cycle, and the pigeons received supplemental feeding as needed to maintain them at 80% of their free-feeding weights. Response rate always increased with larger schedule requirements in the closed economy, but it either decreased steadily or increased and then decreased in the open economy. Initial pauses lengthened with longer fixed intervals or fixed ratios (FR) in the open economy but less so in the closed economy. Responding continued under FR 10,000 schedules in the closed economy, but never survived FR 400 in the open economy. In the open economy, fixed-interval schedules could maintain far more behavior than could either fixed ratios or random intervals. Familiar concepts such as matching and arousal can describe at least some of the behavior in the open economy, but current theory does not apply well to behavior in the closed economy. An explanation of economy-dependent effects might begin with the possibility that the two economies invoke different evolved survival strategies. These strategies influence behavior by means of different mechanisms and laws. The strategy for the closed economy may relate to weight conservation, but that for the open economy may be based on energy conservation.     

ANOVA and ANCOVA of pre- and post-test,ordinal data

With random assignment to treatments and standard assumptions, either a one-way ANOVA of post-test scores or a two-way, repeated measures ANOVA of pre- and post-test scores provides a legitimate test of the equal treatment effect null hypothesis for latent variable . In an ANCOVA for pre- and post-test variablesX andY which are ordinal measures of and , respectively, random assignment and standard assumptions ensure the legitimacy of inferences about the equality of treatment effects on latent variable . Sample estimates of adjustedY treatment means are ordinal estimators of adjusted post-test means on latent variable .     

Operant hoarding: a new paradigm for the study of self-control.

In the first of four experiments, rats were exposed to a modified multiple continuous reinforcement-extinction schedule during 15-min daily sessions. In one condition (saves condition) with the cuelight on, a single lever press produced a food pellet, briefly extinguished the cuelight, and started a clock. Saves (additional lever presses with interresponse times less than 1 s) produced an additional food pellet, briefly extinguished the cuelight, and restarted the interresponse time clock. The cuelight was extinguished 1 s after the last lever press and remained off during a 10-s period of extinction, during which no food pellets were delivered. In the other condition (savings account condition), the contingencies were the same except that the cuelight was extinguished and was not reilluminated after the initial lever press, and the delivery of all food pellets in the reinforcement component was delayed until the onset of extinction. In both conditions, rats made saves, but mean saves (total saves divided by the number of reinforcement components) were slightly reduced in the savings account condition. In Experiment 2, using six equally spaced 15-min sessions per day on alternate days, saves were either followed immediately with food and brief cuelight offset (saves condition) or were not reinforced at all. Mean saves were much greater when saves were reinforced. In Experiment 3, during 5-min daily sessions, saves earned a single pellet (savings account condition) or a number of pellets equal to the ordinal number of the lever press (interest condition). Rats made fewer mean saves, with little change in the food rate, when saves earned interest. In Experiment 4, the rats earned all their food in the operant situation during 24 daily 5-min sessions, these separated by 55-min intersession intervals during which no food was available; otherwise, the conditions were the same as in Experiment 3. In Experiment 4, the shift to interest for saves led to an increase in mean daily mean saves (total daily mean saves divided by the number of daily sessions) as well as to an increase in the number of food pellets delivered in each session. The results are discussed in terms of self-control and behavioral economics.     

基于大数据的文化心理分析

随着大数据技术与文化心理学的融合发展, 计算文化心理学作为一门新兴交叉学科逐渐兴起, 大尺度、近乎全样本的文化心理分析真正得以实现。计算文化心理学关注的文化变量主要围绕个人主义/集体主义这一文化心理学使用最为广泛的维度展开, 分析方法包括特征词典、机器学习、社会网络分析、仿真模拟等, 分析思路包括时间维度上的文化变迁效应以及空间维度上的文化地理效应。当然, 计算文化心理学在为传统文化心理研究提供新方法、新范式的同时, 也存在解码失真、样本偏差、词语多义性、隐私风险等局限, 未来研究应重视变量理论解释、文化动态演化分析、学科深度整合、生态效度等问题。     

A note on the use of rank-ordered logit models for ordered response categories

Models for rankings have been shown to produce more efficient estimators than comparable models for first/top choices. The discussions and applications of these models typically only consider unordered alternatives. But these models can be usefully adapted to the case where a respondent ranks a set of ordered alternatives that are ordered response categories. This paper proposes eliciting a rank order that is consistent with the ordering of the response categories, and then modelling the observed rankings using a variant of the rank ordered logit model where the distribution of rankings has been truncated to the set of admissible rankings. This results in lower standard errors in comparison to when only a single top category is selected by the respondents. And the restrictions on the set of admissible rankings reduces the number of decisions needed to be made by respondents in comparison to ranking a set of unordered alternatives. Simulation studies and application examples featuring models based on a stereotype regression model and a rating scale item response model are provided to demonstrate the utility of this approach.     

Dyadic longitudinal models: A critical review

In this review, we discuss the most commonly used models to analyze dyadic longitudinal data. We start the review with a definition of dyadic longitudinal data that allows relationship researchers to identify when these models might be appropriate. Then, we go on to describe the three major models commonly used when one has dyadic longitudinal data: the dyadic growth curve model (DGCM), the actor–partner interdependence model (APIM), and the common fate growth model (CFGM). We discuss when each model might be used and strengths and weaknesses of each model. We end with additional thoughts that focus on extensions to new methods being discussed in the literature, along with some of the challenges of collecting and analyzing dyadic longitudinal data that might be helpful for future dyadic researchers.     

Mother–child relationship quality from preschool to adolescence: Variation by maternal education

Using longitudinal data from the National Institute of Child Health and Human Development Study of Early Child Care and Youth Development (N = 1088), we examine changes in maternal perception of closeness and conflict in the mother–child relationship from the child's preschool to adolescent years, with attention to variation by maternal education. Analyses using individual growth models show that mother–child closeness increases, while mother–child conflict decreases, from preschool to first grade. From first grade to age 15, mother–child closeness decreases, while mother–child conflict increases, both gradually. The decrease in mother–child conflict from preschool to first grade and the increases in mother–child conflict from first to fifth grade, sixth grade, and age 15 are less steep for mothers with a college degree than for mothers without a college degree. These findings underscore the importance of examining changes in parent–child relationships using longitudinal data across children's developmental stages and their variations by parental social and economic status.     

定序变量回归模型在心理数据分析中的应用

定序变量在心理现象和心理数据中随处可见, 采用综合的定序变量回归分析模型可以对“镜像模式”和“漏斗模型”的心理现象做出合理的解释和预测。首先通过非参数检验对影响因素进行初步降维, 其次用Probit定序回归对降维后的影响因素贡献率进行判别, 从而进一步筛选具有显著性判断水平的有效指标, 最后用Logistic回归模型对某种特定的心理现象发生与否进行信息量足够大的解释和预测。大学毕业生工作生活质量满意度的预测对这种综合定序变量回归分析模型的实例拟合, 证实了综合定序变量回归分析模型在心理现象和心理数据分析中的应用价值。     

项目反应理论中潜在心理特质“填补”的参数估计方法及其演变

在心理与教育测量中, 项目反应理论(Item Response Theory, IRT)模型的参数估计方法是理论研究与实践应用的基本工具。最近, 由于IRT模型的不断扩展与EM (expectation-maximization)算法自身的固有问题, 参数估计方法的改进与发展显得尤为重要。这里介绍了IRT模型中边际极大似然估计的发展, 提出了它的阶段性特征, 即联合极大似然估计阶段、确定性潜在心理特质“填补”阶段、随机潜在心理特质“填补”阶段, 重点阐述了它的潜在心理特质“填补” (data augmentation)思想。EM算法与Metropolis-Hastings Robbins-Monro (MH-RM)算法作为不同的潜在心理特质“填补”方法, 都是边际极大似然估计的思想跨越。目前, 潜在心理特质“填补”的参数估计方法仍在不断发展与完善。