个体解决三项系列问题的心理模型

该研究设计了2种实验条件,对360名6-19岁个体解决三项系列问题的能力发展进行了研究,旨在讨论个体解决三项系列问题的心理模型的发展问题,结果表明：(1)6-19岁个体对三项系列问题中有必然逻辑答案的题目的判断能力的发展速度比较平稳;(2)个体对三项系列问题中有必然逻辑答案的题目的判断能力先于对没有逻辑答案的题目,6-9岁个体对三项系列问题中没有必然逻辑答案的题目的判断能力的发展速度低于10岁以后的个体;(3)空间模型、语义模型、语义一空间混合模型和枢纽项比较模型是相互补充的,是在不同认知发展阶段依一定次序,按一定的阶段逐渐形成的,经历了5个不同的发展阶段,最终形成以枢纽项比较模型为核心、四种心理模型并存于个体的心理结构之中的复合模型。     

A caveat on the Savage–Dickey density ratio: The case of computing Bayes factors for regression parameters

The Savage–Dickey density ratio is a simple method for computing the Bayes factor for an equality constraint on one or more parameters of a statistical model. In regression analysis, this includes the important scenario of testing whether one or more of the covariates have an effect on the dependent variable. However, the Savage–Dickey ratio only provides the correct Bayes factor if the prior distribution of the nuisance parameters under the nested model is identical to the conditional prior under the full model given the equality constraint. This condition is violated for multiple regression models with a Jeffreys–Zellner–Siow prior, which is often used as a default prior in psychology. Besides linear regression models, the limitation of the Savage–Dickey ratio is especially relevant when analytical solutions for the Bayes factor are not available. This is the case for generalized linear models, non-linear models, or cognitive process models with regression extensions. As a remedy, the correct Bayes factor can be computed using a generalized version of the Savage–Dickey density ratio.     

On the current status of the evolutionary theory of behavior dynamics

The evolutionary theory of behavior dynamics is a complexity theory that instantiates the Darwinian principles of selection, reproduction, and mutation in a genetic algorithm. The algorithm is used to animate artificial organisms that behave continuously in time and can be placed in any experimental environment. The present paper is an update on the status of the theory. It includes a summary of the evidence supporting the theory, a list of the theory's untested predictions, and a discussion of how the algorithmic operations of the theory may correspond to material reality. Based on the evidence reviewed here, the evolutionary theory appears to be a strong candidate for a comprehensive theory of adaptive behavior.     

Out-of-bag Prediction Error: A Cross Validation Index for Generalized Structured Component Analysis

Cross validation is a useful way of comparing predictive generalizability of theoretically plausible a priori models in structural equation modeling (SEM). A number of overall or local cross validation indices have been proposed for existing factor-based and component-based approaches to SEM, including covariance structure analysis and partial least squares path modeling. However, there is no such cross validation index available for generalized structured component analysis (GSCA) which is another component-based approach. We thus propose a cross validation index for GSCA, called Out-of-bag Prediction Error (OPE), which estimates the expected prediction error of a model over replications of so-called in-bag and out-of-bag samples constructed through the implementation of the bootstrap method. The calculation of this index is well-suited to the estimation procedure of GSCA, which uses the bootstrap method to obtain the standard errors or confidence intervals of parameter estimates. We empirically evaluate the performance of the proposed index through the analyses of both simulated and real data.     

The five factor model of personality and alexithymia: A meta-analysis

The main objective of the study was to examine the relationship between the Five Factor Model of Personality and alexithymia. Data were collected from 25 studies, which included 30 independent samples, 1384 effect sizes, and 7440 participants. All analyses were conducted with random effects models. Greater neuroticism and lower extraversion, openness to experience, agreeableness, and conscientiousness were associated with higher cognitive alexithymia. Greater neuroticism and openness to experience were linked to lower affective alexithymia. Additionally, age moderated the relationship between the Five Factor Model of Personality and cognitive alexithymia, in that the association between neuroticism and alexithymia became stronger with age whereas the relations of extraversion, openness to experience, agreeableness, and conscientiousness with alexithymia became weaker with age.     

Similarity to prototypical heavy drinkers and non-drinkers predicts AUDIT-C and risky drinking in young adults: prospective study

Objective: The aim of the present study was to explore whether constructs within the Prototype Willingness Model (PWM) predicted risky drinking as measured by AUDIT-C, drinking harms and unplanned drunkenness in a sample of UK young adults. Previous studies exploring the PWM often do not use validated measures of alcohol consumption, and the outcomes of risky drinking are underexplored. Design: An online prospective study design with 4 week follow-up was employed and 385 young adults completed the study (M age?=?21.76, SD?=?3.39, 69.6% female; 85.2% students). Main outcome measures: Intentions to get drunk, AUDIT-C, drinking harms experienced in the last 4 weeks, and unplanned drunkenness in the last 4 weeks. Results: Heavy and non-drinker prototype similarity predicted AUDIT-C, drinking harms and unplanned drunkenness when controlling for past behaviour and reasoned action pathway constructs. Intentions and willingness both mediated the relationship between prototype perceptions and AUDIT-C. Conclusion: This study supports the use of the PWM in the prediction of AUDIT-C, drinking harms and unplanned drinking in a UK sample. Prototype perceptions influenced behaviour via both reasoned and reactive cognitions. Targeting similarity to heavy and non-drinker prototypes should be the focus of future interventions in this population.     

Why does impression management positively influence interview ratings? The mediating role of competence and warmth

Though interviews assess job applicants' skills and abilities, they can be influenced by extraneous factors, including impression management (IM) tactics. Interviewees’ self‐promotion and ingratiation IM tactics predict higher interview ratings; however, researchers have yet to determine why these tactics work. We assessed whether two fundamental dimensions of social perception, competence and warmth, mediate the relationship between IM tactics and interview ratings. We hypothesized that interviewee competence mediates the relationship between self‐promotion and interview ratings, and interviewee warmth mediates the relationship between ingratiation and interview ratings. Using real employment interviews, we found that competence mediates the relationship between self‐promotion and interview ratings, but warmth did not mediate the relationship between ingratiation and interview ratings in the way we expected.     

Children’s scale errors are a natural consequence of learning to associate objects with actions: A computational model

Young children sometimes attempt an action on an object, which is inappropriate because of the object size—they make scale errors. Existing theories suggest that scale errors may result from immaturities in children's action planning system, which might be overpowered by increased complexity of object representations or developing teleofunctional bias. We used computational modelling to emulate children's learning to associate objects with actions and to select appropriate actions, given object shape and size. A computational Developmental Deep Model of Action and Naming (DDMAN) was built on the dual‐route theory of action selection, in which actions on objects are selected via a direct (nonsemantic or visual) route or an indirect (semantic) route. As in case of children, DDMAN produced scale errors: the number of errors was high at the beginning of training and decreased linearly but did not disappear completely. Inspection of emerging object–action associations revealed that these were coarsely organized by shape, hence leading DDMAN to initially select actions based on shape rather than size. With experience, DDMAN gradually learned to use size in addition to shape when selecting actions. Overall, our simulations demonstrate that children's scale errors are a natural consequence of learning to associate objects with actions.