Does a prosocial attitude reduce risky driving behaviour under time pressure?

Time pressure could make drivers exhibit more risky driving behaviour. Attitudes can influence people’s behaviours, but few studies have explored the influence of prosocial attitudes on driving behaviour. The purpose of this study was to explore the influence of prosocial attitudes on driving behaviour under time pressure. A 2 (high/low prosocial attitude) *2 (present/no time pressure) mixed design was used to investigate the interaction between prosocial attitude and time pressure on driving behaviour. Prosocial attitudes and time pressure have a significant main effect on driving behaviour. Drivers with high prosocial attitudes made lane changes at a greater distance from pedestrians and decelerated to a greater degree than drivers with low prosocial attitudes when interacting with pedestrians. Under time pressure, people drive faster and accelerate more quickly. Specifically, we found an interaction between time pressure and prosocial attitudes on driving behaviour. Drivers with low prosocial attitudes showed higher speeds than drivers with high prosocial attitudes under the time pressure scenario on foggy roads. The results showed that high prosocial attitudes lead to friendly interactions with pedestrians and careful driving in specific situations, even under time pressure. The present study not only expands the research on driving behaviour and attitude but can also provide some data support and guidance for driver selection and training.     

An intelligent facial expression recognition system with emotion intensity classification

Facial expressions play a crucial role in emotion recognition as compared to other modalities. In this work, an integrated network, which is capable of recognizing emotion intensity levels from facial images in real time using deep learning technique is proposed. The cognitive study of facial expressions based on expression intensity levels are useful in applications such as healthcare, coboting, Industry 4.0 etc. This work proposes to augment emotion recognition with 2 other important parameters, valence and emotion intensity. This helps in better automated responses by a machine to an emotion. The valence model helps in classifying emotion as positive and negative emotions and discrete model classifies emotions as happy, anger, disgust, surprise and neutral state using Convolution Neural Network (CNN). Feature extraction and classification are carried out using CMU Multi-PIE database. The proposed architecture achieves 99.1% and 99.11% accuracy for valence model and discrete model respectively for offline image data with 5-fold cross validation. The average accuracy achieved in real time for valance model and discrete model is 95% & 95.6% respectively. Also, this work contributes to build a new database using facial landmarks, with three intensity levels of facial expressions which helps to classify expressions into low, mild and high intensities. The performance is also tested for different classifiers. The proposed integrated system is configured for real time Human Robot Interaction (HRI) applications on a test bed consisting of Raspberry Pi and RPA platform to assess its performance.     

Attention in risky choice

Previous research on the processes involved in risky decisions has rarely linked process data to choice directly. We used a simple measure based on the relative amount of attentional deployment to different components (gains/losses and their probabilities) of a risky gamble during the choice process, and we related this measure to the actual choice. In an experiment we recorded the decisions, decision times, and eye movements of 80 participants who made decisions on 11 choice problems. We used the number of eye fixations and fixation transitions to trace the deployment of attention during the choice process and obtained the following main results. First, different components of a gamble attracted different amounts of attention depending on participants' actual choice. This was reflected in both the number of fixations and the fixation transitions. Second, the last-fixated gamble but not the last-fixated reason predicted participants' choices. Third, a comparison of data obtained with eye tracking and data obtained with verbal protocols from a previous study showed a large degree of convergence regarding the process of risky choice. Together these findings tend to support dimensional decision strategies such as the priority heuristic.     

Narrative innovation in life design counseling: The case of Ryan

The main aim of this research is to study the patterns of narrative change in a case of life design counseling (LDC). The innovative moments coding system (IMCS) was used to conduct an intensive analysis of the career counseling sessions. This coding system is grounded in a narrative conception of the self, and suggests that narrative change results from the elaboration and development of narrative exceptions to a client's core problematic self-narrative. The results supports the adequacy of the IMCS to the study of change in career counseling and reveal that the intervention promotes a pattern of change characterized by three types of innovative moments: action, reflection, and protest. The results are discussed taking into account their implications for career counseling theory, research and practice.     

SC-IAT范式下大学生感恩的内隐性

目的：研究探讨了单类内隐联想测验（SC-IAT）测量大学生感恩的可行性,并分析内隐感恩与外显感恩的关系。方法：采用inquisit3.0.2.0编制SC-IAT感恩测量程序,测量了大学生的内隐感恩;采用青少年感恩量表（AGS）测量了大学生的外显感恩,并运用相关技术分析两者之间的关系。结果：采用SC-IAT测量大学生感恩的内隐效应显著,内隐感恩不存在性别、生源地和独生与否等人口统计学指标上的显著差异,大学生的内隐感恩与外显感恩之间相关不显著。结论：（1）大学生整体上存在着积极的内隐感恩;（2）内隐感恩与外显感恩相互分离,是两个不同的建构。     

内隐联系测验的修正——简式内隐联系测验

简式内隐联系测验(Brief Implicit Association Test,BIAT),是对内隐联系测验(Implicit Association Test,IAT)(也称为内隐联想测验)的修正,它由两部分联合任务组成,使用传统IAT中的两类别的属性材料和两类别的概念材料以及与IAT相同的联接-反应对。在设计BIAT时,焦点类别的选定对其测量效果有着至关重要的影响,Sriram和Greenwald的研究已经初步证明BIAT方法可以成功运用于测量内隐态度,认同和刻板印象。本文回顾了内隐联系测验发展过程中的各种变式,着重介绍了简式内隐联系测验,结合Sriram的应用对其进行综合评价并建议在今后的研究中对此方法进行验证。     

Opening the black box: Presenting a model for evaluating organizational-level interventions

Organizational-level occupational health interventions are often recommended when improvements in working conditions, employee health, and well-being are sought within organizations. Research has revealed that these interventions result in inconsistent effects despite being based on theoretical frameworks. This inconsistency indicates that intervention studies need to be designed to examine directly how and why such interventions bring about change and why they sometimes fail. We argue that intervention studies should include a process evaluation that includes a close examination of the psychological and organizational mechanisms that hinder and facilitate desired intervention outcomes. By drawing on existing intervention literature we present an evidence-based model containing three levels of elements that appear to be crucial in process evaluation. We describe how this model may be applied and developed in future research to identify better the mechanisms that link intervention processes to intervention outcomes.     

Accountability and ideology: When left looks right and right looks left

Managers face hard choices between process and outcome systems of accountability in evaluating employees, but little is known about how managers resolve them. Building on the premise that political ideologies serve as uncertainty-reducing heuristics, two studies of working managers show that: (1) conservatives prefer outcome accountability and liberals prefer process accountability in an unspecified policy domain; (2) this split becomes more pronounced in a controversial domain (public schools) in which the foreground value is educational efficiency but reverses direction in a controversial domain (affirmative action) in which the foreground value is demographic equality; (3) managers who discover employees have subverted their preferred system favor tinkering over switching to an alternative system; (4) but bipartisan consensus arises when managers have clear evidence about employee trustworthiness and the tightness of the causal links between employee effort and success. These findings shed light on ideological and contextual factors that shape preferences for accountability systems.     

Do automatic mental associations detect a flashbulb memory?

Flashbulb memories (FBMs) are defined as detailed memories for the reception context in which people first heard of a public and emotionally relevant event. For many years researchers have been debating whether FBMs can be considered a special class of emotional memories, or whether they suffer the same fate as ordinary autobiographical formations. The debate on the real existence of this special class of memories reflects the difficulty of establishing their accuracy. Three indices have been defined as proxies for FBM accuracy: specificity of recalled details, individuals’ confidence in their memory, and memory consistency over time. However, all approaches to FBM assessment have been based on explicit self-report measures. In two studies we aimed to detect FBMs for two emotional public events, by simultaneously employing explicit traditional FBM measures and implicit measures based on the autobiographical Implicit Association Test (aIAT). Jointly considered, the results from the two studies showed that the implicit measures were able to discriminate a FBM, and appeared significantly associated with explicit traditional measures of FBM Specificity, Confidence, and Consistency. Both explicit and implicit assessments concurred to correctly estimate a FBM. Implications for the FBM debate are discussed.     

Flashbulb memories of the Pope's resignation: Explicit and implicit measures across differing religious groups

In the present paper, we aimed to provide evidence in support of the idea tested in a recent study by Lanciano and colleagues that flashbulb memories (FBMs) are a special class of autobiographical memories that can be assessed through the autobiographical implicit association test (aIAT). FBMs and event memories (EMs) for the news of the resignation of Pope Benedict XVI were evaluated in three samples of churchgoer Catholic, non-churchgoer Catholic and Evangelical Italian participants through the traditional self-report measures (specificity/accuracy, confidence, consistency) and aIAT indices. Results confirmed the strength of the association between FBM and true information. The aIAT effect was stronger for FBMs than for EM details, and for Catholic respondents who were the group most concerned by the original event. Furthermore, the use of implicit measures for memory assessment concurs with the traditional self-report indices of FBMs. The present evidence supports the idea that FBMs are special autobiographical memories which remain detailed, certain and consistent over time.