道德困境研究的范式沿革及其理论价值

通过评述道德困境研究范式的发展过程, 系统阐释了经典两难法、加工分离法、CNI模型法和CAN算法的优缺点和理论价值。后来的研究范式均在一定程度上克服了之前研究范式的局限。加工分离法克服了经典两难法的加工纯粹性假设等局限, CNI模型法在加工分离法基础上进一步分离了道德困境决策的多种心理过程, CAN算法则修正了CNI模型法的序列加工的不恰当预设。研究范式的沿革启示研究者综合应用新方法来解决研究争议和重新审视以往道德理论, 合理应用新方法来探索其他具有潜在冲突性的研究议题。总之, 本文为道德困境及相关研究提供了方法学参考。     

同伴对青少年冒险行为的影响及其作用机制

青少年更倾向于从事冒险或鲁莽行为, 并且更容易受到同伴影响。从个人特质和社交互动等多个角度探讨的研究结果表明, 同伴对青少年冒险行为存在助长作用, 即当同伴在场时, 青少年的行为会更加冒险。同伴对青少年冒险行为的作用机制可以从心理、神经、遗传、社会文化等多个层面进行解释：在心理层面, 这种影响源于青少年寻求同伴认可和归属感的需要, 以及社会学习的作用; 在神经机制层面, 源于认知控制网络和刺激处理系统的相互作用; 在遗传层面, 源于特定基因的易感性等; 在社会文化层面, 同伴影响会因文化差异而带来程度与表现上的差异。未来研究应从发展、文化心理、构建复杂模型网络等视角深入探究同伴对青少年冒险行为的影响。     

消费者购买决定与前额叶脑活动的不对称性——来自EEG的证据

摘要：目前营销科学界对消费者购买决策阶段的神经运作机制仍然缺乏准确的理解,本研究探索了大脑前额叶不对称性是否与消费者购买决策有关。采用田野实验方法,21名受试者在真实的在线购物中,分别对自己购物车里5件物品做出真实的购买决策,他们的EEG脑电图被完整记录了下来,共观察到104次购买决策过程。结果表明,在产品页面观看过程中,受试者的gamma频带前额叶不对称,与随后真实购买行为显著相关。alpha、beta频带额叶不对称与购买决策无关。结果还发现与商品的售价对beta和gamma不对称指数的调节效应,且调节效果正好相反。本研究发现了消费者在购买决策过程中一种独特的EEG成分——前额叶gamma波不对称指数（PAIγ）与肯定的购买意愿和实际购买行为相关性最强。     

Random effects and extended generalized partial credit models

In this paper it is shown that under the random effects generalized partial credit model for the measurement of a single latent variable by a set of polytomously scored items, the joint marginal probability distribution of the item scores has a closed-form expression in terms of item category location parameters, parameters that characterize the distribution of the latent variable in the subpopulation of examinees with a zero score on all items, and item-scaling parameters. Due to this closed-form expression, all parameters of the random effects generalized partial credit model can be estimated using marginal maximum likelihood estimation without assuming a particular distribution of the latent variable in the population of examinees and without using numerical integration. Also due to this closed-form expression, new special cases of the random effects generalized partial credit model can be identified. In addition to these new special cases, a slightly more general model than the random effects generalized partial credit model is presented. This slightly more general model is called the extended generalized partial credit model. Attention is paid to maximum likelihood estimation of the parameters of the extended generalized partial credit model and to assessing the goodness of fit of the model using generalized likelihood ratio tests. Attention is also paid to person parameter estimation under the random effects generalized partial credit model. It is shown that expected a posteriori estimates can be obtained for all possible score patterns. A simulation study is carried out to show the usefulness of the proposed models compared to the standard models that assume normality of the latent variable in the population of examinees. In an empirical example, some of the procedures proposed are demonstrated.     

Is personality stable in adolescence? Disentangling time-invariant and time-varying factors of personality in a trait–state–occasion model

This study applied trait–state–occasion (TSO) modelling to investigate the extent to which inter-individual differences in personality were accounted for by time-invariant and time-varying factors during adolescence. The participants were 753 high school students, and only 661 students were followed for 4 years. The mean age in the first year was 15.23 years and 52.8% were females. The results showed that the full TSO model had the best representation of adolescent personality, and the means of the proportions of variance explained by trait factors for the Big Five ranged between 56% and 83%, suggesting that inter-individual differences in personality are mainly accounted for by stable trait variance over the course of adolescence. Furthermore, the five dimensions of personality differed not only in the amounts of trait variance but also in the stability of time-varying factors. These findings have significant implications for personality research.     

The argument from surprise

I develop an account of productive surprise as an epistemic virtue of scientific investigations which does not turn on psychology alone. On my account, a scientific investigation is potentially productively surprising when (1) results can conflict with epistemic expectations, (2) those expectations pertain to a wide set of subjects. I argue that there are two sources of such surprise in science. One source, often identified with experiments, involves bringing our theoretical ideas in contact with new empirical observations. Another, often identified with simulations, involves articulating and bringing together different parts of our knowledge. Both experiments and simulations, then, can surprise.     

Naïve and Robust: Class‐Conditional Independence in Human Classification Learning

Humans excel in categorization. Yet from a computational standpoint, learning a novel probabilistic classification task involves severe computational challenges. The present paper investigates one way to address these challenges: assuming class‐conditional independence of features. This feature independence assumption simplifies the inference problem, allows for informed inferences about novel feature combinations, and performs robustly across different statistical environments. We designed a new Bayesian classification learning model (the dependence‐independence structure and category learning model, DISC‐LM) that incorporates varying degrees of prior belief in class‐conditional independence, learns whether or not independence holds, and adapts its behavior accordingly. Theoretical results from two simulation studies demonstrate that classification behavior can appear to start simple, yet adapt effectively to unexpected task structures. Two experiments—designed using optimal experimental design principles—were conducted with human learners. Classification decisions of the majority of participants were best accounted for by a version of the model with very high initial prior belief in class‐conditional independence, before adapting to the true environmental structure. Class‐conditional independence may be a strong and useful default assumption in category learning tasks.     

Numerical approximation of the observed information matrix with Oakes' identity

An efficient and accurate numerical approximation methodology useful for obtaining the observed information matrix and subsequent asymptotic covariance matrix when fitting models with the EM algorithm is presented. The numerical approximation approach is compared to existing algorithms intended for the same purpose, and the computational benefits and accuracy of this new approach are highlighted. Instructive and real-world examples are included to demonstrate the methodology concretely, properties of the estimator are discussed in detail, and a Monte Carlo simulation study is included to investigate the behaviour of a multi-parameter item response theory model using three competing finite-difference algorithms.     

Comparing instructor-led,video-model,and no-instruction control tutorials for creating single-subject graphs in Microsoft Excel: A systematic replication and extension

Visual inspection of single-subject data is the primary method for behavior analysts to interpret the effect of an independent variable on a dependent variable; however, there is no consensus on the most suitable method for teaching graph construction for single-subject designs. We systematically replicated and extended Tyner and Fienup (2015) using a repeated-measures between-subjects design to compare the effects of instructor-led, video-model, and no-instruction control tutorials on the graphing performance of 81 master's students with some reported Microsoft Excel experience. Our mixed-design analysis revealed a statistically significant main effect of pretest, tutorial, and posttest submissions for each tutorial group and a nonsignificant main effect of tutorial group. Tutorial group significantly interacted with submissions, suggesting that both instructor-led and video-model tutorials may be superior to providing graduate students with a written list of graphing conventions (i.e., control condition). Finally, training influenced performance on an untrained graph type (multielement) for all tutorial groups.