New Ideas in Psychology最新文献_第7页

Artificial intelligence in lie detection: Why do cognitive theories matter? 人工智能测谎：认知理论为何重要？

IF 2.3 3区心理学 Q2 PSYCHOLOGY, EXPERIMENTAL

New Ideas in Psychology

Pub Date : 2024-11-22 DOI: 10.1016/j.newideapsych.2024.101128

Philip Tseng , Tony Cheng

In the field of psychological research on deception detection, the rise of machine learning algorithms, or artificial intelligence (AI), has sparked discussions about its potential benefits and risks. Most researchers argue strongly for the inclusion of good theories in the design, training, and application phases of AI. In this letter we ask an important follow-up question: what makes a good theory? And why do they matter in detecting deception? To this end, we argue that mechanism-driven, and cognitively-informed, theories are the ones AI researchers need to be looking for. This is particularly important in deception detection, where false positives and negatives can result in irreversible legal consequences. Crucially, mechanism-driven theories allow us to know 1) what features are we extracting (e.g., a particular facial expression in time, higher peak in P300, etc.)? And importantly, 2) how are these features related (subordinate or superordinate) to the entity we are inferring (e.g., memory recognition, anxiety, or deception)? Answers to these questions can help forensic experts anticipate where the majority of our AI's mistakes are (i.e., false negatives or false positives), and allow nonexperts such as policymakers to adjust decision-making criterion to compensate for such errors via legal or other means if needed be (e.g., a more liberal criterion for detecting deception during the investigation phase, but later switches to conservative criterion in court). These logical inferences all start from mechanistically and cognitively-informative theories.

在有关欺骗检测的心理学研究领域，机器学习算法或人工智能（AI）的兴起引发了有关其潜在益处和风险的讨论。大多数研究人员强烈主张在人工智能的设计、训练和应用阶段纳入良好的理论。在这封信中，我们提出了一个重要的后续问题：什么是好的理论？为什么它们对检测欺骗行为很重要？为此，我们认为，人工智能研究人员需要寻找的是以机制为驱动、以认知为基础的理论。这一点在欺骗检测中尤为重要，因为在欺骗检测中，假阳性和假阴性都可能导致无法挽回的法律后果。至关重要的是，机制驱动理论能让我们知道：1）我们正在提取哪些特征（例如，特定时间的面部表情、P300 的较高峰值等）？重要的是，2）这些特征与我们正在推断的实体（如记忆识别、焦虑或欺骗）是如何相关的（从属或上级）？这些问题的答案可以帮助法医专家预测人工智能的大部分错误（即假阴性或假阳性），并允许决策者等非专家在必要时通过法律或其他手段调整决策标准以弥补这些错误（例如，在调查阶段采用更宽松的标准来检测欺骗行为，但随后在法庭上改用保守的标准）。这些逻辑推论都是从机械和认知信息理论出发的。

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引用次数: 0

Environments “develop”: Infant motor development can inform the study of physical space 环境 "发展"：婴儿运动发展可为物理空间研究提供启示

IF 2.3 3区心理学 Q2 PSYCHOLOGY, EXPERIMENTAL

New Ideas in Psychology

Pub Date : 2024-10-03 DOI: 10.1016/j.newideapsych.2024.101125

Joshua L. Schneider

Infant development is tied to the physical environment. Indeed, all infant behavior—movement, exploration, object play, social interaction—occurs in the context of a physical space. Nonetheless, researchers know surprisingly little about how the environments that infants inhabit come to be. Researchers in other fields of study (e.g., architectural design, urban planning, classroom curation, playground construction) have focused on space as a construct of interest and gleaned important information about interactions between the composition of spaces and the behaviors of those who occupy them. In particular, the organization of a space (its contents and layout) can both create and constrain opportunities for a host of behaviors for infants and children. This review presents an integrated synthesis of multiple literatures regarding the physical environment and its interactions with human behavior. I argue for a developmental approach for the study of how spaces develop; that is, how the spaces occupied by infants are changed over time by adult caregivers. Using several existing theoretical perspectives, I suggest a conceptual framework anchored to infant motor development—the process by which infants acquire new motor skills over time—as a model system for the study of infants’ developing spaces. Collectively, this review highlights the importance of considering space as part and parcel of infant development.

婴儿的发展与物理环境息息相关。事实上，婴儿的所有行为--运动、探索、玩物、社交--都是在物理空间的背景下发生的。然而，研究人员对婴儿居住的环境是如何形成的却知之甚少。其他研究领域（如建筑设计、城市规划、教室设计、操场建设等）的研究人员都将空间作为一种关注的建构物，并收集了有关空间构成与占用者行为之间相互作用的重要信息。特别是，空间的组织（其内容和布局）既能为婴幼儿的一系列行为创造机会，也能限制这些机会。本综述综合了有关物理环境及其与人类行为相互作用的多种文献。我主张用发展的方法来研究空间是如何发展的；也就是说，婴儿所占用的空间是如何随着时间的推移而被成人看护者所改变的。利用现有的几种理论视角，我提出了一个以婴儿运动发展为基础的概念框架--婴儿随着时间的推移获得新运动技能的过程--作为研究婴儿发展空间的模型系统。总之，这篇综述强调了将空间视为婴儿发展的重要组成部分的重要性。

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引用次数: 0

Virtual reality analytics map (VRAM): A conceptual framework for detecting mental disorders using virtual reality data 虚拟现实分析地图（VRAM）：利用虚拟现实数据检测精神障碍的概念框架

IF 2.3 3区心理学 Q2 PSYCHOLOGY, EXPERIMENTAL

New Ideas in Psychology

Pub Date : 2024-10-02 DOI: 10.1016/j.newideapsych.2024.101127

Vibhav Chitale , Julie D. Henry , Hai-Ning Liang , Ben Matthews , Nilufar Baghaei

Virtual reality (VR) is an emerging tool in mental health care yet its potential in diagnostic assessments remains underexplored. Recognizing the growing need of technological advancements that support traditional methods for mental health assessment, this paper introduces the Virtual Reality Analytics Map (VRAM), a novel conceptual framework designed to leverage VR analytics for the detection of symptoms of mental disorders. The VRAM framework integrates psychological constructs with VR technology, systematically mapping and quantifying behavioral domains through specific VR tasks. This approach potentially allows for the precise capture and identification of nuanced behavioral, cognitive, and affective digital biomarkers associated with symptoms of mental disorders. The benefits of the VRAM framework are demonstrated with its example application across various mental disorders ensuring the utility and versatility of the framework. By bridging the gap between psychology and technology, the VRAM framework aims to contribute to the early detection and assessment of mental disorders.

虚拟现实（VR）是一种新兴的心理保健工具，但其在诊断评估方面的潜力仍未得到充分挖掘。认识到对支持传统心理健康评估方法的技术进步的需求日益增长，本文介绍了虚拟现实分析地图（VRAM），这是一个新颖的概念框架，旨在利用虚拟现实分析技术检测精神障碍的症状。VRAM 框架将心理结构与 VR 技术相结合，通过特定的 VR 任务系统地映射和量化行为领域。这种方法可以精确捕捉和识别与精神障碍症状相关的细微行为、认知和情感数字生物标记。VRAM 框架在各种精神障碍中的应用实例展示了该框架的优势，确保了该框架的实用性和多功能性。通过缩小心理学与技术之间的差距，VRAM 框架旨在为精神障碍的早期检测和评估做出贡献。

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引用次数: 0

Minimization, denial, moralization, and exaggeration: A taxonomy of backlash to transgender recognition and rights 最小化、否认、道德化和夸大：对变性人的承认和权利的反弹分类法

IF 2.3 3区心理学 Q2 PSYCHOLOGY, EXPERIMENTAL

New Ideas in Psychology

Pub Date : 2024-09-28 DOI: 10.1016/j.newideapsych.2024.101126

Joseph A. Vandello

Transgender people's efforts for recognition, rights, and equality face backlash, particularly as they gain societal visibility. However, backlash must appear justified (rather than merely prejudiced) to be legitimate and persuasive. I present a taxonomy of 17 backlash strategies people use against transgender groups, organized into four major themes: minimization, denial, moralization, and exaggeration. These strategies range from covert to overt and from seemingly benign to hostile. A model is proposed linking backlash strategies to behavioral tendencies via emotional responses. When targets are perceived as low in power and nonthreatening, people are more likely to favor minimization and denial; As targets are perceived as more threatening, people are more likely to favor moralization and exaggeration. Minimization and denial produce amusement and contempt or reduce or circumvent negative self-focused emotions (guilt or pity). This leads to the weakening of compassion and the suppression of prosocial behaviors. Moralization and exaggeration produce disgust, fear, and anger which can lead to avoidance, punishment, and aggression. By labeling and organizing the many ways that people push back against transgender groups, people may be better able to recognize and respond to backlash. I end by proposing several counter-backlash strategies suggested by the taxonomic structure.

变性人争取认可、权利和平等的努力面临着反弹，尤其是在他们获得社会关注的时候。然而，反击必须显得有理有据（而不仅仅是偏见），这样才具有合法性和说服力。我将人们针对跨性别群体使用的 17 种反击策略进行了分类，分为四大主题：最小化、否认、道德化和夸大。这些策略的范围从隐蔽到公开，从看似善意到充满敌意。我们提出了一个模型，通过情绪反应将反冲策略与行为倾向联系起来。当目标被认为权力较低且不具威胁性时，人们更倾向于最小化和否认；当目标被认为更具威胁性时，人们更倾向于道德化和夸大。最小化和否认会产生娱乐和蔑视，或减少或规避以自我为中心的负面情绪（内疚或怜悯）。这就会削弱同情心，抑制亲社会行为。道德化和夸大会产生厌恶、恐惧和愤怒，从而导致回避、惩罚和攻击行为。通过对人们反击跨性别群体的多种方式进行标注和整理，人们可以更好地识别和应对反击。最后，我提出了分类结构所建议的几种反击策略。

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引用次数: 0

Modeling the mood state on thermal sensation with a data mining algorithm and testing the accuracy of mood state correction factor 利用数据挖掘算法建立情绪状态对热感觉的影响模型，并测试情绪状态修正系数的准确性

IF 2.3 3区心理学 Q2 PSYCHOLOGY, EXPERIMENTAL

New Ideas in Psychology

Pub Date : 2024-09-25 DOI: 10.1016/j.newideapsych.2024.101124

Fatma Yerlikaya-Özkurt , Mehmet Furkan Özbey , Cihan Turhan

Psychology is proven as an influencing factor on thermal sensation. On the other hand, mood state is one of the significant parameters in psychology field. To this aim, in the literature, mood state correction factor on thermal sensation (Turhan and Özbey coefficients) is derived utilizing with data-driven black-box model. However, novel models which present analytical form of the mood state correction factor should be derived based on the several descriptive variables on thermal sensation. Moreover, the result of this factor should also be checked with analytical model results. Therefore, this study investigates the modelling of mood state correction factor with a data mining algorithm, called Multivariate Adaptive Regression Splines (MARS). Additionally, the mood state is also taken as a thermal sensation parameter besides environmental parameters in this algorithm. The same data, which are collected from a university study hall in a temperate climate zone, are used and the model results are compared with the thermal sensation results based on mood state correction factor which is driven via black-box model. The results show that coefficient of correlation “r” between the MARS and black-box model is found as 0.9426 and 0.9420 for training and testing. Hence, the mood state is also modelled via a data mining algorithm with a high accuracy, besides the black-box model.

事实证明，心理是热感觉的一个影响因素。另一方面，情绪状态是心理学领域的重要参数之一。为此，文献中利用数据驱动的黑箱模型推导出了热感觉的情绪状态修正系数（Turhan 和 Özbey 系数）。然而，应根据热感觉的几个描述性变量，推导出呈现情绪状态修正系数分析形式的新模型。此外，还应将该系数的结果与分析模型的结果进行核对。因此，本研究采用一种名为 "多变量自适应回归样条曲线（MARS）"的数据挖掘算法来研究情绪状态校正因子的建模问题。此外，在该算法中，除环境参数外，情绪状态也被视为热感觉参数。我们使用了从温带气候区一所大学自习室收集的相同数据，并将模型结果与基于情绪状态修正系数的热感结果进行了比较，后者是通过黑盒模型驱动的。结果显示，MARS 与黑盒模型之间的相关系数 "r "在训练和测试中分别为 0.9426 和 0.9420。因此，除黑盒模型外，通过数据挖掘算法建立情绪状态模型也具有很高的准确性。

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引用次数: 0

Me and my brand(s): Exploring the psychological resource exchange in consumer-brand relationships 我和我的品牌探索消费者与品牌关系中的心理资源交换

IF 2.3 3区心理学 Q2 PSYCHOLOGY, EXPERIMENTAL

New Ideas in Psychology

Pub Date : 2024-09-25 DOI: 10.1016/j.newideapsych.2024.101123

Wiktor Razmus

Researchers have shown a growing interest in the effects of consumer brand engagement—a psychological construct that describes the type of relationship between the consumer and the brands. Although it is widely acknowledged that consumer brand engagement plays a significant role in shaping company and brand outcomes, the existing literature lacks overarching theoretical underpinnings to explain the psychological outcomes of brand engagement for consumers. The following questions arise: What personal benefits do consumers derive from such engagement, and how does this process unfold? This paper presents a model of consumer brand engagement as a resource. The new model offers a framework that integrates the theoretical foundations of consumer-brand relationships and the concept of resource exchange, providing novel and relevant insights into elucidating the psychological consumer-based consequences of brand engagement. Finally, a research agenda is provided, outlining directions for future studies that will contribute to advancing understanding of the psychological relationship between the consumer and brands.

研究人员对消费者品牌参与的影响表现出越来越浓厚的兴趣，这种心理结构描述了消费者与品牌之间的关系类型。尽管人们普遍认为消费者品牌参与在塑造公司和品牌结果方面发挥着重要作用，但现有文献缺乏总体理论基础来解释消费者品牌参与的心理结果。由此产生了以下问题：消费者从这种参与中获得了哪些个人利益，这一过程又是如何展开的？本文提出了一个消费者品牌参与资源模型。新模型提供了一个整合消费者-品牌关系理论基础和资源交换概念的框架，为阐明基于消费者心理的品牌参与后果提供了新颖而相关的见解。最后，还提供了一个研究议程，概述了未来研究的方向，这将有助于加深对消费者与品牌之间心理关系的理解。

引用次数: 0

Can autoethnography enhance research in health psychology? 自述能促进健康心理学研究吗？

IF 2.3 3区心理学 Q2 PSYCHOLOGY, EXPERIMENTAL

New Ideas in Psychology

Pub Date : 2024-09-23 DOI: 10.1016/j.newideapsych.2024.101122

David Chun Yin Li

While autoethnography can offer unique insights into subjective experiences, some researchers view it as lacking generalizability and scientific rigor. This essay argues that autoethnography is an epistemologically defensible method from a theoretical standpoint. Ontologically, it is valuable and capable of enriching health psychological research by providing accounts of palliative and curative treatments and illness experiences for patients, carers, and practitioners that may not be fully accessible by other means.

虽然自述民族志可以提供对主观经验的独特见解，但一些研究人员认为它缺乏普遍性和科学严谨性。本文认为，从理论角度看，自述民族志是一种在认识论上站得住脚的方法。从本体论的角度来看，它是有价值的，能够丰富健康心理学研究，为患者、护理人员和从业人员提供其他方法可能无法完全获得的有关姑息治疗和治疗方法以及疾病经历的描述。

引用次数: 0

Partnering with AI for instrument development: Possibilities and pitfalls 与人工智能合作开发仪器：可能性与陷阱

IF 2.3 3区心理学 Q2 PSYCHOLOGY, EXPERIMENTAL

New Ideas in Psychology

Pub Date : 2024-09-17 DOI: 10.1016/j.newideapsych.2024.101121

Ronald A. Beghetto , Wendy Ross , Maciej Karwowski , Vlad P. Glăveanu

Recent advances in generative artificial intelligence (AI), specifically large language models (LLMs), provide new possibilities for researchers to partner with AI when developing and refining psychological instruments. In this paper we demonstrate how LLMs, such as OpenAI's ChatGPT 4 model, might be used to support the development of new psychometric scales. Partnering with AI for the purpose of developing and refining instruments, however, comes with its share of potential pitfalls. We thereby discuss throughout the paper that instrument development and refinement start and end with human judgment and expertise. We open with two use-cases that describe how we used LLMs in the development and refinement of two new psychological instruments. Next, we discuss possibilities for where and how researchers can use LLMs in the process of instrument development more broadly, including considerations for maximizing the benefits of LLMs and addressing the potential hazards when working with LLMs. Finally, we close by offering initial suggestions for psychology researchers interested in partnering with LLMs in this capacity.

生成式人工智能（AI），特别是大型语言模型（LLMs）的最新进展，为研究人员在开发和完善心理测量工具时与 AI 合作提供了新的可能性。在本文中，我们将展示如何利用 LLM（如 OpenAI 的 ChatGPT 4 模型）来支持新心理测量量表的开发。然而，与人工智能合作开发和完善工具也有其潜在的隐患。因此，我们将在本文中讨论，工具的开发和完善始于人类的判断和专业知识，也终于人类的判断和专业知识。我们以两个使用案例开篇，描述了我们如何在两个新的心理工具的开发和完善过程中使用 LLM。接下来，我们讨论了研究人员在工具开发过程中在哪些方面以及如何更广泛地使用 LLM 的可能性，包括如何最大限度地发挥 LLM 的优势，以及在使用 LLM 时如何应对潜在的危险。最后，我们为有兴趣以这种身份与法律硕士合作的心理学研究人员提供了初步建议。

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引用次数: 0

Person-environment fit and social camouflaging in autism 自闭症患者的人与环境适应性和社交伪装

IF 2.3 3区心理学 Q2 PSYCHOLOGY, EXPERIMENTAL

New Ideas in Psychology

Pub Date : 2024-09-08 DOI: 10.1016/j.newideapsych.2024.101112

Jessica Klein, Sarah J. Macoun

Social camouflaging is a set of behaviours used by autistic people to conceal social differences. This paper provides an analysis of social camouflaging within the developmental context of autistic persons. We suggest that autistic people achieve person-environment fit with their social environment by using social camouflaging as an inauthentic form of trait expression whereby autistic traits are masked and neurotypical traits are displayed. The resulting consequences for autistic individuals may be interpersonally beneficial, but conversely intrapersonally detrimental, when considering existing theories or models of person-environment fit throughout development. The current paper explores this dichotomy and suggests implications for future social camouflaging research in autism, such as considering a broader developmental context through which to study the consequences of camouflaging. Clinical implications include an increased focus on reciprocity between autistic individuals and their social environment.

社会伪装是自闭症患者用来掩盖社会差异的一系列行为。本文分析了自闭症患者在成长过程中的社会伪装行为。我们认为，自闭症患者通过使用社会伪装这种不真实的特质表达方式，掩盖自闭症特质并展示神经典型特质，从而实现个人-环境与社会环境的契合。考虑到现有的关于人与环境在整个发展过程中的契合度的理论或模型，自闭症患者由此产生的后果可能在人际关系上是有益的，但反之则在人际关系上是有害的。本文探讨了这种二分法，并提出了对未来自闭症社交伪装研究的启示，例如考虑在更广泛的发展背景下研究伪装的后果。对临床的影响包括更加关注自闭症患者与其社会环境之间的互惠关系。

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引用次数: 0

Growth Mindset Scale: Aspects of reliability and validity of a new 8-item scale assessing growth mindset 成长心态量表：新的 8 项成长心态评估量表的信度和效度

IF 2.3 3区心理学 Q2 PSYCHOLOGY, EXPERIMENTAL

New Ideas in Psychology

Pub Date : 2024-08-22 DOI: 10.1016/j.newideapsych.2024.101111

Hermundur Sigmundsson , Monika Haga

In this article, the psychometric properties of a new scale aimed at quantifying Growth Mindset are explored. Growth Mindset Scale is a quantitative measure which is context independent and simple to administer.

Growth Mindset Scale was tested on 723 participants between 16 and 85 years of age (mean age = 28.56, SD = 12.14), which allowed for the exploration of feasibility, internal consistency, and construct validity.

The results indicate that the growth mindset scale is applicable for the age studied (16–85). All individual item scores showed a positive correlation with the total score and ranged between 0.45 and 0.63. The Cronbach's alpha value was 0.83 for the standardized items. Pearson's correlation coefficient between the total score of the Growth Mindset Scale and the total score of Theories of intelligence scale was r = 0.168 (p < 0.001).

These encouraging results assure additional improvement of the growth mindset scale, involving normalization based on a larger, representative sample.

本文探讨了一种旨在量化 "成长心态 "的新量表的心理测量特性。对 723 名年龄在 16-85 岁之间的参与者（平均年龄为 28.56 岁，标准差为 12.14 岁）进行了测试，从而探讨了该量表的可行性、内部一致性和建构效度。结果表明，成长心态量表适用于所研究的年龄段（16-85 岁）。所有单项得分均与总分呈正相关，相关范围在 0.45 至 0.63 之间。标准化项目的 Cronbach's alpha 值为 0.83。成长心态量表的总分与智力理论量表的总分之间的皮尔逊相关系数为 r = 0.168 (p < 0.001)。这些令人鼓舞的结果保证了成长心态量表的进一步改进，包括在更大的代表性样本基础上进行标准化。

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引用次数: 0