Buying an electric car: A rational choice or a norm-directed behavior?

The diffusion of electric cars can contribute to more sustainability in the transport sector, but diffusion rates in most countries are still low. We investigated motives for electric car adoption in German households from an environmental psychology perspective. The public debate focuses on rational aspects such as the purchase price or new technological demands (e.g., limited range and a new charging system). Psychological research on energy-related investment decisions in households confirms the relevance of rational motives, but additionally points to the importance of norm-directed motives (moral and social norms). We investigated the relevance of different motives in an online questionnaire with n = 220 members of German households interested in buying a new car. The questionnaire included possible rational and norm-related predictors of electric car adoption. We tested three action models to explain adoption intention: An adjusted technology acceptance model (TAM), an adjusted norm activation model (NAM), and an integrative model with predictors from both models. We analyzed the hypothesized models with path analyses. All models explained a substantial share of variance in adoption intention. The explained share of variance in the NAM was higher than in the TAM and comparably high to the integrative model. The results demonstrate the important role of moral and social motives for households’ investment decisions. Additionally, the technology’s perceived usefulness was an important rational motive. We discuss the context dependency of the results, as household members might have little knowledge about the new technology during the early stages of a technology’s diffusion process. The results strongly suggest broadening political support schemes, such as informational and image campaigns, as a way to more effectively foster electric car diffusion. More comprehensive assessments appear to be necessary in future analyses of electric car adoption as well as energy-related investment decisions.     

A multi-modal approach to cognitive training and assistance in minimally invasive surgery

Minimally-invasive surgery (MIS) offers many benefits to patients, but is considerably more difficult to learn and perform than is open surgery. One main reason for the observed difficulty is attributable to the visuo-spatial challenges that arise in MIS, taxing the surgeons’ cognitive skills. In this contribution, we present a new approach that combines training and assistance as well as the visual and the auditory modality to help surgeons to overcome these challenges. To achieve this, our approach assumes two main components: An adaptive, individualized training component as well as a component that conveys spatial information through sound. The training component (a) specifically targets the visuo-spatial processes crucial for successful MIS performance and (b) trains surgeons in the use of the sound component. The second component is an auditory display based on a psychoacoustic sonification, which reduces and avoids some of the commonly experienced MIS challenges. Implementations of both components are described and their integration is discussed. Our approach and both of its components go beyond the current state of the art in important ways. The training component has been explicitly designed to target MIS-specific visuo-spatial skills and to allow for adaptive testing, promoting individualized learning. The auditory display is conveying spatial information in 3-D space. Our approach is the first that encompasses both training for improved mastery and reduction of cognitive challenges in MIS. This promises better tailoring of surgical skills and assistance to the needs and the capabilities of the surgeons and, thus, ultimately, increased patient safety and health.     

Toward ethical cognitive architectures for the development of artificial moral agents

New technologies based on artificial agents promise to change the next generation of autonomous systems and therefore our interaction with them. Systems based on artificial agents such as self-driving cars and social robots are examples of this technology that is seeking to improve the quality of people’s life. Cognitive architectures aim to create some of the most challenging artificial agents commonly known as bio-inspired cognitive agents. This type of artificial agent seeks to embody human-like intelligence in order to operate and solve problems in the real world as humans do. Moreover, some cognitive architectures such as Soar, LIDA, ACT-R, and iCub try to be fundamental architectures for the Artificial General Intelligence model of human cognition. Therefore, researchers in the machine ethics field face ethical questions related to what mechanisms an artificial agent must have for making moral decisions in order to ensure that their actions are always ethically right. This paper aims to identify some challenges that researchers need to solve in order to create ethical cognitive architectures. These cognitive architectures are characterized by the capacity to endow artificial agents with appropriate mechanisms to exhibit explicit ethical behavior. Additionally, we offer some reasons to develop ethical cognitive architectures. We hope that this study can be useful to guide future research on ethical cognitive architectures.     

The TROCA Project: An autonomous transportation robot controlled by a cognitive architecture

Autonomous mobile robots emerged as an important kind of transportation system in warehouses and factories. In this work, we present the use of MECA cognitive architecture in the development of an artificial mind for an autonomous robot responsible for multiple tasks, including transportation of packages along a factory floor, environment exploration, warehouse inventory, its internal energy management, self-monitoring and dealing with human operators and other robots. The present text provides a detailed specification for the architecture and its software implementation. Future work will present the simulation results under different configurations, together with a detailed analysis of the architecture performance and its generalization for autonomous robot control.     

Socially emotional brain-inspired cognitive architecture framework for artificial intelligence

Artificial Intelligence of the next generation needs to interact with users socially, convincing them in its ability to understand human minds, including emotions. For this to happen, an artificial emotional intelligence is needed, capable of adequate, believable behavior in social emotional interactions. Building on previous developments, the present work extends the general framework of emotional Biologically Inspired Cognitive Architecture (eBICA: Samsonovich, 2013, 2018), endowing it with fluents describing, in addition to appraisals, somatic markers, feelings, emotions, moods, emotional reactions and biases. Key building blocks that integrate them are moral schemas and semantic maps. The model describes interaction of three factors: plans and commitments, moral and ethical values, and somatic comfort. Learning in this framework includes self-organization of semantic maps that in turn may provide guidance for active humanlike learning. Implications for empirical studies and practical applications are discussed together with the expected impact.     

Life skills development in physical education: A self-determination theory-based investigation across the school term

ObjectivesGrounded in self-determination theory (SDT), the main aim of this study was to examine the longitudinal associations between teacher autonomy support, students’ basic psychological need satisfaction and life skills development in physical education (PE).DesignThis study employed a two-wave longitudinal research design.MethodStudents (N = 266, Mage = 12.94 years, SD = 0.70) completed measures assessing perceived autonomy-supportive teaching, need satisfaction (autonomy, competence, and relatedness), and life skills development in PE (teamwork, goal setting, social skills, problem solving and decision making, emotional skills, leadership, time management, and interpersonal communication). Data collections took place during week 6 (timepoint 1; T1) and week 15 (timepoint 2; T2) of the autumn school term.ResultsCross-lagged panel analyses showed that T1 teacher autonomy support did not significantly predict students’ three basic psychological needs, total need satisfaction or life skills development at T2. Students’ T1 total need satisfaction positively predicted their development of all eight life skills at T2. Additionally, students’ T1 autonomy satisfaction positively predicted their teamwork, social skills, emotional skills, leadership, and interpersonal communication skills at T2, T1 competence satisfaction positively predicted students’ teamwork skills at T2, and students’ T1 relatedness satisfaction positively predicted their social skills at T2.ConclusionsProviding partial support for SDT, the findings highlighted that satisfaction of students’ three basic psychological needs had some positive effects on students’ life skills development in PE. As such, a climate that satisfies students’ basic psychological needs should help to develop their life skills in PE.     

辱虐管理的应对：基于人际拒绝的多动力模型分析

随着辱虐管理产生破坏性影响的作用机制不断得到丰富和完善, 受辱虐下属选择特定应对方式的原因以及有效性成为未来辱虐管理的研究方向。基于人际拒绝的多动力模型, 梳理了在不同质量LMX的关系情境中受辱虐下属的应对方式, 并提出特定应对方式可能引起辱虐管理的恶性循环的假设模型。建议未来研究通过丰富下属的应对行为变量、增加人格和环境的调节机制以完善受辱虐下属的应对模型, 并以此为基础探究辱虐管理的消极互动机制, 为打破辱虐管理恶性循环提供理论支持。     

面孔识别的自我群体偏向

面孔认知研究表明, 人们识别和再认自己所属群体(如种族、性别、年龄)面孔的成绩显著好于识别其他群体面孔的成绩。近年来围绕面孔识别的这种自我群体偏向进行了实验研究, 研究者提出了知觉经验说和社会认知论两种理论来解释其认知加工机制, 而类别化-个性化模型和双路径模型则是将两种理论分别加以整合提出的新解释。研究者还对面孔识别自我群体偏向的神经机制进行了研究, 探讨了评价者、评价对象和评价任务等因素对自我群体偏向的影响。提升面孔识别自我群体偏向研究的生态效度, 构建整合性理论模型以及加强跨文化和本土研究是未来研究的重要方向。     

时变效应模型及在密集追踪数据分析中的应用

密集追踪数据通常蕴含了心理过程的详细变化信息,反映了某些心理的复杂变化过程。时变效应模型用函数替代恒定的系数,可描述密集追踪数据中随时间推移心理的动态变化过程和时变效应,是分析复杂心理过程的有效方法。在介绍时变效应模型的原理后,通过模拟研究考察模型的表现,结果显示:(1)样本量增加可降低函数估计的误差;(2)惩罚样条法的节点数选择与函数的复杂度有关,函数越复杂,所需节点越多;(3)样本量与节点数对函数估计误差的交互效应不显著。进一步应探讨测量次数、数据分布形态、数据缺失等如何影响模型的表现。     

音乐训练对大学生执行功能的影响

近年来大量研究发现音乐训练对认知能力的具有积极影响,一些理论认为音乐训练的广泛迁移效应是通过执行功能起中介作用,但音乐训练与执行功能的关系至今仍存争议。研究将执行功能分为抑制控制、工作记忆和认知灵活性三个子成分,并将抑制控制进一步细分为注意抑制和反应抑制,同时区分工作记忆中主动性控制和反应性控制两种认知过程。通过对比音乐组和控制组在执行功能各任务（Go/No-go, Stroop, AX-CPT, Task-switching）上的行为表现来系统考察音乐训练与执行功能的关系。结果表明音乐训练对执行功能不同子成分的影响具有差异性,其促进效应主要体现在抑制控制中的注意抑制和工作记忆中的主动性控制,对抑制控制中的反应抑制、工作记忆中的反应性控制和认知灵活性影响较小。