Momentum in Child Compliance and Opposition

Several event-dependent properties of child-mother interactions were studied to investigate momentum in child oppositional and compliant behavior. It was hypothesized that: (a) compared to an episode of child opposition, an episode of child compliance would be related to an increased likelihood of subsequent child compliance, and (b) the association between a target episode of child opposition and the likelihood of subsequent compliance or opposition would be mediated by the history of the dyadic interaction. These hypotheses were supported. In addition, the presence of interactional sequences predictive of child compliance was inversely related to mother dysphoria and unrelated to mother-reported child externalizing behavior disorder symptoms. Discussion focuses on antecedent interactional patterns as determinants of future child behavior.     

Influence of vowel context on the audio‐visual speech perception of voiced stop consonants

The work reported here investigated whether the extent of McGurk effect differs according to the vowel context, and differs when cross‐modal vowels are matched or mismatched in Japanese. Two audio‐visual experiments were conducted to examine the process of audio‐visual phonetic‐feature extraction and integration. The first experiment was designed to compare the extent of the McGurk effect in Japanese in three different vowel contexts. The results indicated that the effect was largest in the /i/ context, moderate in the /a/ context, and almost nonexistent in the /u/ context. This suggests that the occurrence of McGurk effect depends on the characteristics of vowels and the visual cues from their articulation. The second experiment measured the McGurk effect in Japanese with cross‐modal matched and mismatched vowels, and showed that, except with the /u/ sound, the effect was larger when the vowels were matched than when they were mismatched. These results showed, again, that the extent of McGurk effect depends on vowel context and that auditory information processing before phonetic judgment plays an important role in cross‐modal feature integration.     

中国背景下员工建言行为结构及中庸思维对其的影响

企业的自主创新离不开员工的自发性和创造性, 员工建言行为研究因此愈加受到重视。本研究拟探索中国背景下企业员工建言行为的结构(研究一), 并检验中国人的典型思维特征－中庸思维与中国背景建言行为的关系(研究二)。研究一通过对长三角地区30名企业员工的访谈提取建言行为关键事件, 编制初始问卷, 再通过对159名被试的探索性因素分析, 抽取出顾全大局式和自我冒进式两个建言行为维度; 另一批159名被试的验证性因素分析结果进一步证实了该结构。研究二以278份配对员工为样本, 层次回归分析结果表明, 中庸思维与顾全大局式建言存在正相关, 与自我冒进式建言存在负相关, 且授权对中庸思维与两类建言行为之间关系存在着调节作用。     

语篇语境对句子理解的影响：来自N400的证据

N400作为语义违反的特异性ERP组分,其波幅反映了语义整合加工过程的难度,因此可将其作为研究语篇语境意义建构过程的一项重要指标。语篇语境可分为语言语境和非语言语境。前人一系列基于语篇水平的N400研究表明,从语义的角度上看,语言语境对句子理解具有制约和协调作用,其机制涉及词汇语义启动、场景适宜性、文字内容信息以及因果推理等方面;非语言语境对句子理解具有制约和补充作用,但其机制尚不明确。两种不同来源的语义信息在实时理解中是同时得到加工的     

句子理解过程中词类加工的功能性质

句法优先理论假设词类加工功能上优先于语义、动词论元结构和话语信息加工。来自德语和法语的证据显示, 词类违反阻断语义整合和动词论元结构加工, 因而词类优先于语义和动词论元结构。关于词类加工在功能上是否优先于话语信息加工, 尚无来自任何语言的证据。汉语证据尽管显示词类并不优先于语义, 但未充分讨论任务因素的影响。将来研究有必要使用ERP技术和违反范式, 从语义整合、动词论元结构加工和话语水平加工等多个层面, 同时操纵词类的正确性和非句法因素, 考察句法特性上与德语和法语不同的语言, 如汉语和韩语。这方面研究将有助于洞察一个语言的语言学特性如何制约或调整词类加工的功能性质。     

Demonstrating the effect of context on order effects for an army air defense task using the patriot simulator

The results reported herein support the hypotheses that (1) situation-specific, contextual features of a task can cause people to use explanation-based reasoning (Pennington and Hastie, 1993); (2) such reasoning can cause experienced personnel, both individually and in two-person teams, to reinterpret the meaning of the same information when it is presented in two different ordered sequences; and (3) the result will be primacy or recency (or no) effects depending on whether the most recent conflicting information can be explained away or not, respectively. These results extend the belief-adjustment model proposed by Hogarth and Einhorn (1992), which does not address information reinterpretations, and always predicts recency effects for an evaluation task with a short series of conflicting information. More generally, the results demonstrate the importance of situation-specific, contextual features in understanding judgment processes. © 1997 John Wiley & Sons, Ltd.     

消费者基于选择集的情境效应

消费者基于选择集的情境效应颠覆了传统的理性选择理论,表明了情境对消费者偏好和选择的系统性影响。情境效应主要的类型有吸引效应和折衷效应。情境效应具有感知偏差、风险和负面情绪规避以及信息推理等方面的内在机理。选择集结构和特征及消费者等因素都对情境效应产生影响,而情境效应会影响消费者偏好的构建。未来情境效应研究将会向复杂属性组合、复杂购买情境和新情境效应的发现等方面拓展。     

The Moral Community Divide: Underage Marijuana Use Across Religious Contexts

Compared to individual‐level research on religion and marijuana use, much less research has been conducted to investigate how the overall religious context of a geographic location may influence marijuana use during adolescence and early adulthood. Using multilevel analyses on two waves of the National Study of Youth and Religion (NSYR) merged with county‐level variables from the U.S. Census and the Religious Congregations and Membership Study (RCMS), this study finds that a county's higher Catholic population share is negatively associated with underage marijuana use frequency even after controlling for a wide range of individual and county‐level variables. Besides being robust, the Catholic contextual effect on marijuana use is also diffusive, influencing both Catholic and non‐Catholic youth who live in the same county. This study highlights the importance of viewing religious influence on substance use as a contextual, cultural force across different kinds of religious moral communities.     

Rater Model Using Signal Detection Theory for Latent Differential Rater Functioning

Differential rater functioning (DRF) occurs when raters show evidence of exercising differential severity or leniency when scoring examinees within different subgroups. Previous studies of DRF have examined rater bias using manifest variables (e.g., use of covariates) to determine the subgroups. These manifest variables include gender and the ethnicity of the examinee. For example, a rater may score males more severely. Ideally, each rater’s severity should be invariant across subgroups. This study examines DRF in the context of latent subgroups that classify possible sources of DRF based on raters’ scoring behavior rather than manifest factors. An extension of the latent class signal detection theory (LC-SDT) model for identifying DRF is proposed and examined using real-world data and simulations. Results from real-world data show that the signal detection approach leads to an effective method to identify latent DRF. Simulations with varying sample sizes and conditions of rater precision were shown to recover parameters at an adequate level, supporting its use to identify latent DRF in large-scale data. These findings suggest that the DRF extension of the LC-SDT can be a useful model to examine characteristics of raters and add information that can aid rater training.     

The Impact of Item Misspecification and Dichotomization on Class and Parameter Recovery in LCA of Count Data

Mixture analysis of count data has become increasingly popular among researchers of substance use, behavioral analysis, and program evaluation. However, this increase in popularity seems to have occurred along with adoption of some conventions in model specification based on arbitrary heuristics that may impact the validity of results. Findings from a systematic review of recent drug and alcohol publications suggested count variables are often dichotomized or misspecified as continuous normal indicators in mixture analysis. Prior research suggests that misspecifying skewed distributions of continuous indicators in mixture analysis introduces bias, though the consequences of this practice when applied to count indicators has not been studied. The present work describes results from a simulation study examining bias in mixture recovery when count indicators are dichotomized (median split; presence vs. absence), ordinalized, or the distribution is misspecified (continuous normal; incorrect count distribution). All distributional misspecifications and methods of categorizing resulted in greater bias in parameter estimates and recovery of class membership relative to specifying the true distribution, though dichotomization appeared to improve class enumeration accuracy relative to all other specifications. Overall, results demonstrate the importance of accurately modeling count indicators in mixture analysis, as misspecification and categorizing data can distort study outcomes.