Learning through observing: Effects of modeling truth‐ and lie‐telling on children’s honesty

The current study examined the influence of observing another's lie‐ or truth‐telling – and its consequences – on children's own honesty about a transgression. Children (N = 224, 5–8 years of age) observed an experimenter (E) tell the truth or lie about a minor transgression in one of five conditions: (a) Truth‐Positive Outcome – E told the truth with a positive outcome; (b) Truth‐Negative Outcome – E told the truth with a negative outcome; (c) Lie‐Positive Outcome – E lied with a positive outcome; (d) Lie‐Negative Outcome – E lied with a negative outcome; (e) Control – E did not tell a lie or tell the truth. Later, to examine children's truth‐ or lie‐telling behavior, children participated in a temptation resistance paradigm where they were told not to peek at a trivia question answer. They either peeked or not, and subsequently lied or told the truth about that behavior. Additionally, children were asked to give moral evaluations of different truth‐ and lie‐telling vignettes. Overall, 85% of children lied. Children were less likely to lie about their own transgression in the TRP when they had previously witnessed the experimenter tell the truth with a positive outcome or tell a lie with a negative outcome.     

Using the UTAUT2 model to explain public acceptance of conditionally automated (L3) cars: A questionnaire study among 9,118 car drivers from eight European countries

We investigated public acceptance of conditionally automated (SAE Level 3) passenger cars using a questionnaire study among 9,118 car-drivers in eight European countries, as part of the European L3Pilot project. 71.06% of respondents considered conditionally automated cars easy to use while 28.03% of respondents planned to buy a conditionally automated car once it is available. 41.85% of respondents would like to use the time in the conditionally automated car for secondary activities. Among these 41.85%, respondents plan to talk to fellow travellers (44.76%), surf the internet, watch videos or TV shows (44%), observe the landscape (41.70%), and work (17.06%). The UTAUT2 (Unified Theory of Acceptance and Use of Technology) was applied to investigate the effects of performance and effort expectancy, social influence, facilitating conditions, and hedonic motivation on the behavioural intention to use conditionally automated cars. Structural equation analysis revealed that the UTAUT2 can be applied to conditional automation, with hedonic motivation, social influence, and performance expectancy influencing the behavioural intention to buy and use a conditionally automated car. The present study also found positive effects of facilitating conditions on effort expectancy and hedonic motivation. Social influence was a positive predictor of hedonic motivation, facilitating conditions, and performance expectancy. Age, gender and experience with advanced driver assistance systems had significant, yet small (<0.10), effects on behavioural intention. The implications of these results on the policy and best practices to enable large-scale implementation of conditionally automated cars on public roads are discussed.     

术后乳腺癌患者体象对创伤后应激障碍和创伤后成长的影响：反刍的中介作用

为探讨乳腺癌患者反刍这一认知情绪调节方式在体象和创伤后应激障碍（PTSD）、创伤后成长（PTG）之间的中介作用,采用体象量表（BIS）、认知情绪调节问卷中文版（CERQ-C）、创伤后应激障碍量表（PSS）和创伤后成长问卷（PTGI）对150名乳腺癌术后患者进行研究。结果发现：（1）乳腺癌患者的体象可以正向预测PTSD和PTG;（2）乳腺癌患者的反刍在体象和PTSD的闯入性症状、回避性症状中起中介作用,在体象与PTG中的中介作用不显著。结果表明,体象既可以正向预测乳腺癌患者的PTSD和PTG,也可以通过反刍的中介作用影响PTSD。     

The Process Through Which an Advocacy Intervention Resulted in Positive Change for Battered Women Over Time

A prior experimental evaluation of a community-based advocacy program for women with abusive partners demonstrated positive change in the lives of women even 2 years postintervention (C. M. Sullivan & D. I. Bybee, 1999). The current study explored the complex mediational process through which this change occurred, using longitudinal structural equation modeling and formal tests of mediation. As hypothesized, the advocacy intervention first resulted in women successfully obtaining desired community resources and increasing their social support, which enhanced their overall quality of life. This improvement in well-being appeared to serve as a protective factor from subsequent abuse, as women who received the intervention were significantly less likely to be abused at 2-year follow-up compared with women in the control condition. Increased quality of life completely mediated the impact of the advocacy intervention on later reabuse. Discussion places advocacy for women in the context of other efforts that are needed to build an effective community response to preventing intimate violence against women.     

Beyond bruises and broken bones: the joint effects of stress and injuries on battered women's health

We investigated the joint mediating effects of injuries and stress on the relationship between abuse and women's health. A community sample of 397 women, half of whom had been assaulted by an intimate partner within the prior 6 months, was interviewed about their experience of intimate partner violence, injuries, stress, income, depression, and physical health problems. Structural equation modeling techniques confirmed the complex model of hypothesized indirect effects of abuse on women's physical health problems through injuries, stress, and depression. Stress accounted for 80% of the indirect effect of abuse on women's physical health. Its direct effect on physical health was somewhat larger than its indirect effect through depression, but both processes played a key role in determining the effect of abuse on women's physical health problems. Furthermore, abuse was a stronger predictor of women's stress than was poverty. Implication and future research are discussed.     

The competition-among-relations-in-nominals theory of conceptual combination: implications for stimulus class formation and class expansion

One way in which new concepts are added to the conceptual system is through conceptual combination. The competition-among-relations-in-nominals (CARIN) theory (Gagné & Shoben, 1997) proposes that conceptual combination involves specifying a thematic relation (e.g., noun MADE OF modifier) to link the constituent concepts (e.g., chocolate and bee). This theory claims that relations have different strengths for various concepts that correspond to how often a modifier and relation have been paired in previous encounters with combined concepts and that this relational knowledge strongly affects the ease with which combined concepts can be formed. A mathematical model that incorporates key claims of the theory is presented, and empirical findings that are relevant to evaluating the CARIN theory are reviewed. The parallels between the CARIN theory and approaches to stimulus class formation are also discussed.     

Facilitative orthographic neighborhood effects: the SERIOL model account

A large orthographic neighborhood (N) facilitates lexical decision for central and left visual field/right hemisphere (LVF/RH) presentation, but not for right visual field/left hemisphere (RVF/LH) presentation. Based on the SERIOL model of letter-position encoding, this asymmetric N effect is explained by differential activation patterns at the orthographic level. This analysis implies that it should be possible to negate the LVF/RH N effect and create an RVF/LH N effect by manipulating contrast levels in specific ways. In Experiment 1, these predictions were confirmed. In Experiment 2, we eliminated the N effect for both LVF/RH and central presentation. These results indicate that the letter level is the primary locus of the N effect under lexical decision, and that the hemispheric specificity of the N effect does not reflect differential processing at the lexical level.     

Getting the Big Picture in Community Science: Methods That Capture Context

Community science has a rich tradition of using theories and research designs that are consistent with its core value of contextualism. However, a survey of empirical articles published in the American Journal of Community Psychology shows that community scientists utilize a narrow range of statistical tools that are not well suited to assess contextual data. Multilevel modeling, geographic information systems (GIS), social network analysis, and cluster analysis are recommended as useful tools to address contextual questions in community science. An argument for increased methodological consilience is presented, where community scientists are encouraged to adopt statistical methodology that is capable of modeling a greater proportion of the data than is typical with traditional methods.     

Processes of similarity judgment

Similarity underlies fundamental cognitive capabilities such as memory, categorization, decision making, problem solving, and reasoning. Although recent approaches to similarity appreciate the structure of mental representations, they differ in the processes posited to operate over these representations. We present an experiment that differentiates among extant structural accounts of similarity in their ability to account for patterns of similarity ratings. These data pose a challenge for transformation-based models and all but one mapping-based model, the Similarity as Interactive Activation and Mapping (SIAM) model of similarity.     

How Should a Speech Recognizer Work?

Although researchers studying human speech recognition (HSR) and automatic speech recognition (ASR) share a common interest in how information processing systems (human or machine) recognize spoken language, there is little communication between the two disciplines. We suggest that this lack of communication follows largely from the fact that research in these related fields has focused on the mechanics of how speech can be recognized. In Marr's (1982) terms, emphasis has been on the algorithmic and implementational levels rather than on the computational level. In this article, we provide a computational-level analysis of the task of speech recognition, which reveals the close parallels between research concerned with HSR and ASR. We illustrate this relation by presenting a new computational model of human spoken-word recognition, built using techniques from the field of ASR that, in contrast to current existing models of HSR, recognizes words from real speech input.