Perspectives on Psychological Science最新文献_第4页

Personality Science in the Digital Age: The Promises and Challenges of Psychological Targeting for Personalized Behavior-Change Interventions at Scale. 数字时代的人格科学：大规模个性化行为改变干预的心理定位承诺与挑战》（Personality Science in the Digital Age: The Promises and Challenges of Psychological Targeting for Personalized Behavior Change Interventions at Scale）。

IF 10.5 1区心理学

Perspectives on Psychological Science Pub Date : 2024-11-01 Epub Date: 2023-08-29 DOI: 10.1177/17456916231191774

Sandra C Matz, Emorie D Beck, Olivia E Atherton, Mike White, John F Rauthmann, Dan K Mroczek, Minhee Kim, Tim Bogg

{"title":"Personality Science in the Digital Age: The Promises and Challenges of Psychological Targeting for Personalized Behavior-Change Interventions at Scale.","authors":"Sandra C Matz, Emorie D Beck, Olivia E Atherton, Mike White, John F Rauthmann, Dan K Mroczek, Minhee Kim, Tim Bogg","doi":"10.1177/17456916231191774","DOIUrl":"10.1177/17456916231191774","url":null,"abstract":"With the rapidly growing availability of scalable psychological assessments, personality science holds great promise for the scientific study and applied use of customized behavior-change interventions. To facilitate this development, we propose a classification system that divides psychological targeting into two approaches that differ in the process by which interventions are designed: audience-to-content matching or content-to-audience matching. This system is both integrative and generative: It allows us to (a) integrate existing research on personalized interventions from different psychological subdisciplines (e.g., political, educational, organizational, consumer, and clinical and health psychology) and to (b) articulate open questions that generate promising new avenues for future research. Our objective is to infuse personality science into intervention research and encourage cross-disciplinary collaborations within and outside of psychology. To ensure the development of personality-customized interventions aligns with the broader interests of individuals (and society at large), we also address important ethical considerations for the use of psychological targeting (e.g., privacy, self-determination, and equity) and offer concrete guidelines for researchers and practitioners.","PeriodicalId":19757,"journal":{"name":"Perspectives on Psychological Science","volume":" ","pages":"1031-1056"},"PeriodicalIF":10.5,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10114215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

How the Complexity of Psychological Processes Reframes the Issue of Reproducibility in Psychological Science. 心理过程的复杂性如何重塑心理科学的可重复性问题》（How the Complexity of Psychological Processes Reframes the Issue of Reproducibility in Psychological Science）。

IF 10.5 1区心理学

Perspectives on Psychological Science Pub Date : 2024-11-01 Epub Date: 2023-08-14 DOI: 10.1177/17456916231187324

Christophe Gernigon, Ruud J R Den Hartigh, Robin R Vallacher, Paul L C van Geert

{"title":"How the Complexity of Psychological Processes Reframes the Issue of Reproducibility in Psychological Science.","authors":"Christophe Gernigon, Ruud J R Den Hartigh, Robin R Vallacher, Paul L C van Geert","doi":"10.1177/17456916231187324","DOIUrl":"10.1177/17456916231187324","url":null,"abstract":"In the past decade, various recommendations have been published to enhance the methodological rigor and publication standards in psychological science. However, adhering to these recommendations may have limited impact on the reproducibility of causal effects as long as psychological phenomena continue to be viewed as decomposable into separate and additive statistical structures of causal relationships. In this article, we show that (a) psychological phenomena are patterns emerging from nondecomposable and nonisolable complex processes that obey idiosyncratic nonlinear dynamics, (b) these processual features jeopardize the chances of standard reproducibility of statistical results, and (c) these features call on researchers to reconsider what can and should be reproduced, that is, the psychological processes per se, and the signatures of their complexity and dynamics. Accordingly, we argue for a greater consideration of process causality of psychological phenomena reflected by key properties of complex dynamical systems (CDSs). This implies developing and testing formal models of psychological dynamics, which can be implemented by computer simulation. The scope of the CDS paradigm and its convergences with other paradigms are discussed regarding the reproducibility issue. Ironically, the CDS approach could account for both reproducibility and nonreproducibility of the statistical effects usually sought in mainstream psychological science.","PeriodicalId":19757,"journal":{"name":"Perspectives on Psychological Science","volume":" ","pages":"952-977"},"PeriodicalIF":10.5,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9993774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Too Good to Be True: Bots and Bad Data From Mechanical Turk. 好得不真实：来自 Mechanical Turk 的机器人和不良数据》（Too Good to Be True: Bots and Bad Data from Mechanical Turk.

IF 10.5 1区心理学

Perspectives on Psychological Science Pub Date : 2024-11-01 Epub Date: 2022-11-07 DOI: 10.1177/17456916221120027

Margaret A Webb, June P Tangney

引用次数: 0

Why DON'T We "Say Her Name"? An Intersectional Model of the Invisibility of Police Violence Against Black Women and Girls. 为什么我们不 "说出她的名字"？警察暴力侵害黑人妇女和女孩行为隐匿性的交叉模型》。

IF 10.5 1区心理学

Perspectives on Psychological Science Pub Date : 2024-10-09 DOI: 10.1177/17456916241277554

Aerielle M Allen, Alexis Drain, Chardée A Galán, Azaadeh Goharzad, Irene Tung, Beza M Bekele

{"title":"Why DON'T We \"Say Her Name\"? An Intersectional Model of the Invisibility of Police Violence Against Black Women and Girls.","authors":"Aerielle M Allen, Alexis Drain, Chardée A Galán, Azaadeh Goharzad, Irene Tung, Beza M Bekele","doi":"10.1177/17456916241277554","DOIUrl":"https://doi.org/10.1177/17456916241277554","url":null,"abstract":"Racialized police violence is a profound form of systemic oppression affecting Black Americans, yet the narratives surrounding police brutality have disproportionately centered on Black men and boys, overshadowing the victimization of Black women and girls. In 2014, the #SayHerName campaign emerged to bring attention to the often-overlooked instances of police brutality against Black women and girls, including incidents of both nonsexual and sexual violence. Despite these efforts, mainstream discourse and psychological scholarship on police violence continue to marginalize the experiences of Black women and girls. This raises a critical question: Why DON'T we \"Say Her Name\"? This article employs intersectional frameworks to demonstrate how the historic and systemic factors that render Black women and girls particularly vulnerable to police violence also deny their legitimacy as victims, perpetuate their invisibility, and increase their susceptibility to state-sanctioned violence. We extend models of intersectional invisibility by arguing that ideologies related to age, in addition to racial and gender identities, contribute to their marginalization. Finally, we reflect on how psychological researchers can play a pivotal role in dismantling the invisibility of Black women and girls through scientific efforts and advocacy.","PeriodicalId":19757,"journal":{"name":"Perspectives on Psychological Science","volume":" ","pages":"17456916241277554"},"PeriodicalIF":10.5,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142392320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Challenges in Understanding Human-Algorithm Entanglement During Online Information Consumption. 理解在线信息消费过程中人与算法纠缠的挑战。

IF 10.5 1区心理学

Perspectives on Psychological Science Pub Date : 2024-09-01 Epub Date: 2023-07-10 DOI: 10.1177/17456916231180809

Stephan Lewandowsky, Ronald E Robertson, Renee DiResta

{"title":"Challenges in Understanding Human-Algorithm Entanglement During Online Information Consumption.","authors":"Stephan Lewandowsky, Ronald E Robertson, Renee DiResta","doi":"10.1177/17456916231180809","DOIUrl":"10.1177/17456916231180809","url":null,"abstract":"Most content consumed online is curated by proprietary algorithms deployed by social media platforms and search engines. In this article, we explore the interplay between these algorithms and human agency. Specifically, we consider the extent of entanglement or coupling between humans and algorithms along a continuum from implicit to explicit demand. We emphasize that the interactions people have with algorithms not only shape users' experiences in that moment but because of the mutually shaping nature of such systems can also have longer-term effects through modifications of the underlying social-network structure. Understanding these mutually shaping systems is challenging given that researchers presently lack access to relevant platform data. We argue that increased transparency, more data sharing, and greater protections for external researchers examining the algorithms are required to help researchers better understand the entanglement between humans and algorithms. This better understanding is essential to support the development of algorithms with greater benefits and fewer risks to the public.","PeriodicalId":19757,"journal":{"name":"Perspectives on Psychological Science","volume":" ","pages":"758-766"},"PeriodicalIF":10.5,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11373152/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9765071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Psychological AI: Designing Algorithms Informed by Human Psychology. 心理人工智能：根据人类心理学设计算法》（Psychological AI: Designing Algorithms Informed by Human Psychology）。

IF 10.5 1区心理学

Perspectives on Psychological Science Pub Date : 2024-09-01 Epub Date: 2023-07-31 DOI: 10.1177/17456916231180597

Gerd Gigerenzer

引用次数: 0

The Sins of the Parents Are to Be Laid Upon the Children: Biased Humans, Biased Data, Biased Models. 父母的罪过要加诸于子女：有偏见的人类、有偏见的数据、有偏见的模型。

IF 10.5 1区心理学

Perspectives on Psychological Science Pub Date : 2024-09-01 Epub Date: 2023-07-18 DOI: 10.1177/17456916231180099

Merrick R Osborne, Ali Omrani, Morteza Dehghani

{"title":"The Sins of the Parents Are to Be Laid Upon the Children: Biased Humans, Biased Data, Biased Models.","authors":"Merrick R Osborne, Ali Omrani, Morteza Dehghani","doi":"10.1177/17456916231180099","DOIUrl":"10.1177/17456916231180099","url":null,"abstract":"Technological innovations have become a key driver of societal advancements. Nowhere is this more evident than in the field of machine learning (ML), which has developed algorithmic models that shape our decisions, behaviors, and outcomes. These tools have widespread use, in part, because they can synthesize massive amounts of data to make seemingly objective recommendations. Yet, in the past few years, the ML community has been drawing attention to the need for caution when interpreting and using these models. This is because these models are created by humans, from data generated by humans, whose psychology allows for various biases that impact how the models are developed, trained, tested, and interpreted. As psychologists, we thus face a fork in the road: Down the first path, we can continue to use these models without examining and addressing these critical flaws and rely on computer scientists to try to mitigate them. Down the second path, we can turn our expertise in bias toward this growing field, collaborating with computer scientists to reduce the models' deleterious outcomes. This article serves to light the way down the second path by identifying how extant psychological research can help examine and curtail bias in ML models.","PeriodicalId":19757,"journal":{"name":"Perspectives on Psychological Science","volume":" ","pages":"796-807"},"PeriodicalIF":10.5,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10185871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transmission Versus Truth, Imitation Versus Innovation: What Children Can Do That Large Language and Language-and-Vision Models Cannot (Yet). 传播与真理，模仿与创新：孩子能做什么，大型语言和语言视觉模型（目前）做不到。

IF 10.5 1区心理学

Perspectives on Psychological Science Pub Date : 2024-09-01 Epub Date: 2023-10-26 DOI: 10.1177/17456916231201401

Eunice Yiu, Eliza Kosoy, Alison Gopnik

{"title":"Transmission Versus Truth, Imitation Versus Innovation: What Children Can Do That Large Language and Language-and-Vision Models Cannot (Yet).","authors":"Eunice Yiu, Eliza Kosoy, Alison Gopnik","doi":"10.1177/17456916231201401","DOIUrl":"10.1177/17456916231201401","url":null,"abstract":"Much discussion about large language models and language-and-vision models has focused on whether these models are intelligent agents. We present an alternative perspective. First, we argue that these artificial intelligence (AI) models are cultural technologies that enhance cultural transmission and are efficient and powerful imitation engines. Second, we explore what AI models can tell us about imitation and innovation by testing whether they can be used to discover new tools and novel causal structures and contrasting their responses with those of human children. Our work serves as a first step in determining which particular representations and competences, as well as which kinds of knowledge or skill, can be derived from particular learning techniques and data. In particular, we explore which kinds of cognitive capacities can be enabled by statistical analysis of large-scale linguistic data. Critically, our findings suggest that machines may need more than large-scale language and image data to allow the kinds of innovation that a small child can produce.","PeriodicalId":19757,"journal":{"name":"Perspectives on Psychological Science","volume":" ","pages":"874-883"},"PeriodicalIF":10.5,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11373165/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54230419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Three Challenges for AI-Assisted Decision-Making. 人工智能辅助决策面临的三大挑战。

IF 10.5 1区心理学

Perspectives on Psychological Science Pub Date : 2024-09-01 Epub Date: 2023-07-13 DOI: 10.1177/17456916231181102

Mark Steyvers, Aakriti Kumar

{"title":"Three Challenges for AI-Assisted Decision-Making.","authors":"Mark Steyvers, Aakriti Kumar","doi":"10.1177/17456916231181102","DOIUrl":"10.1177/17456916231181102","url":null,"abstract":"Artificial intelligence (AI) has the potential to improve human decision-making by providing decision recommendations and problem-relevant information to assist human decision-makers. However, the full realization of the potential of human-AI collaboration continues to face several challenges. First, the conditions that support complementarity (i.e., situations in which the performance of a human with AI assistance exceeds the performance of an unassisted human or the AI in isolation) must be understood. This task requires humans to be able to recognize situations in which the AI should be leveraged and to develop new AI systems that can learn to complement the human decision-maker. Second, human mental models of the AI, which contain both expectations of the AI and reliance strategies, must be accurately assessed. Third, the effects of different design choices for human-AI interaction must be understood, including both the timing of AI assistance and the amount of model information that should be presented to the human decision-maker to avoid cognitive overload and ineffective reliance strategies. In response to each of these three challenges, we present an interdisciplinary perspective based on recent empirical and theoretical findings and discuss new research directions.","PeriodicalId":19757,"journal":{"name":"Perspectives on Psychological Science","volume":" ","pages":"722-734"},"PeriodicalIF":10.5,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11373149/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9770751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Social Drivers and Algorithmic Mechanisms on Digital Media. 数字媒体的社会驱动力和算法机制。

IF 10.5 1区心理学

Perspectives on Psychological Science Pub Date : 2024-09-01 Epub Date: 2023-07-19 DOI: 10.1177/17456916231185057

Hannah Metzler, David Garcia

{"title":"Social Drivers and Algorithmic Mechanisms on Digital Media.","authors":"Hannah Metzler, David Garcia","doi":"10.1177/17456916231185057","DOIUrl":"10.1177/17456916231185057","url":null,"abstract":"On digital media, algorithms that process data and recommend content have become ubiquitous. Their fast and barely regulated adoption has raised concerns about their role in well-being both at the individual and collective levels. Algorithmic mechanisms on digital media are powered by social drivers, creating a feedback loop that complicates research to disentangle the role of algorithms and already existing social phenomena. Our brief overview of the current evidence on how algorithms affect well-being, misinformation, and polarization suggests that the role of algorithms in these phenomena is far from straightforward and that substantial further empirical research is needed. Existing evidence suggests that algorithms mostly reinforce existing social drivers, a finding that stresses the importance of reflecting on algorithms in the larger societal context that encompasses individualism, populist politics, and climate change. We present concrete ideas and research questions to improve algorithms on digital platforms and to investigate their role in current problems and potential solutions. Finally, we discuss how the current shift from social media to more algorithmically curated media brings both risks and opportunities if algorithms are designed for individual and societal flourishing rather than short-term profit.","PeriodicalId":19757,"journal":{"name":"Perspectives on Psychological Science","volume":" ","pages":"735-748"},"PeriodicalIF":10.5,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11373151/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9822531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0