Topics in Cognitive Science最新文献_第2页

Adversarial Dynamics in Centralized Versus Decentralized Intelligent Systems. 集中与分散智能系统中的对抗动力学。

IF 2.9 2区心理学

Topics in Cognitive Science Pub Date : 2025-04-01 Epub Date: 2023-10-30 DOI: 10.1111/tops.12705

Levin Brinkmann, Manuel Cebrian, Niccolò Pescetelli

{"title":"Adversarial Dynamics in Centralized Versus Decentralized Intelligent Systems.","authors":"Levin Brinkmann, Manuel Cebrian, Niccolò Pescetelli","doi":"10.1111/tops.12705","DOIUrl":"10.1111/tops.12705","url":null,"abstract":"Artificial intelligence (AI) is often used to predict human behavior, thus potentially posing limitations to individuals' and collectives' freedom to act. AI's most controversial and contested applications range from targeted advertisements to crime prevention, including the suppression of civil disorder. Scholars and civil society watchdogs are discussing the oppressive dangers of AI being used by centralized institutions, like governments or private corporations. Some suggest that AI gives asymmetrical power to governments, compared to their citizens. On the other hand, civil protests often rely on distributed networks of activists without centralized leadership or planning. Civil protests create an adversarial tension between centralized and decentralized intelligence, opening the question of how distributed human networks can collectively adapt and outperform a hostile centralized AI trying to anticipate and control their activities. This paper leverages multi-agent reinforcement learning to simulate dynamics within a human-machine hybrid society. We ask how decentralized intelligent agents can collectively adapt when competing with a centralized predictive algorithm, wherein prediction involves suppressing coordination. In particular, we investigate an adversarial game between a collective of individual learners and a central predictive algorithm, each trained through deep Q-learning. We compare different predictive architectures and showcase conditions in which the adversarial nature of this dynamic pushes each intelligence to increase its behavioral complexity to outperform its counterpart. We further show that a shared predictive algorithm drives decentralized agents to align their behavior. This work sheds light on the totalitarian danger posed by AI and provides evidence that decentrally organized humans can overcome its risks by developing increasingly complex coordination strategies.","PeriodicalId":47822,"journal":{"name":"Topics in Cognitive Science","volume":" ","pages":"374-391"},"PeriodicalIF":2.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12093910/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71414731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Cognitive Models for Machine Theory of Mind. 机器心智理论的认知模型。

IF 2.9 2区心理学

Topics in Cognitive Science Pub Date : 2025-04-01 Epub Date: 2024-12-01 DOI: 10.1111/tops.12773

Christian Lebiere, Peter Pirolli, Matthew Johnson, Michael Martin, Donald Morrison

{"title":"Cognitive Models for Machine Theory of Mind.","authors":"Christian Lebiere, Peter Pirolli, Matthew Johnson, Michael Martin, Donald Morrison","doi":"10.1111/tops.12773","DOIUrl":"10.1111/tops.12773","url":null,"abstract":"Some of the required characteristics for a true machine theory of mind (MToM) include the ability to (1) reproduce the full diversity of human thought and behavior, (2) develop a personalized model of an individual with very limited data, and (3) provide an explanation for behavioral predictions grounded in the cognitive processes of the individual. We propose that a certain class of cognitive models provide an approach that is well suited to meeting those requirements. Being grounded in a mechanistic framework like a cognitive architecture such as ACT-R naturally fulfills the third requirement by mapping behavior to cognitive mechanisms. Exploiting a modeling paradigm such as instance-based learning accounts for the first requirement by reflecting variations in individual experience into a diversity of behavior. Mechanisms such as knowledge tracing and model tracing allow a specific run of the cognitive model to be aligned with a given individual behavior trace, fulfilling the second requirement. We illustrate these principles with a cognitive model of decision-making in a search and rescue task in the Minecraft simulation environment. We demonstrate that cognitive models personalized to individual human players can provide the MToM capability to optimize artificial intelligence agents by diagnosing the underlying causes of observed human behavior, projecting the future effects of potential interventions, and managing the adaptive process of shaping human behavior. Examples of the inputs provided by such analytic cognitive agents include predictions of cognitive load, probability of error, estimates of player self-efficacy, and trust calibration. Finally, we discuss implications for future research and applications to collective human-machine intelligence.","PeriodicalId":47822,"journal":{"name":"Topics in Cognitive Science","volume":" ","pages":"268-290"},"PeriodicalIF":2.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12093916/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142773728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

The Inner Loop of Collective Human-Machine Intelligence. 人机集体智能的内循环。

IF 2.9 2区心理学

Topics in Cognitive Science Pub Date : 2025-04-01 Epub Date: 2023-02-20 DOI: 10.1111/tops.12642

Scott Cheng-Hsin Yang, Tomas Folke, Patrick Shafto

{"title":"The Inner Loop of Collective Human-Machine Intelligence.","authors":"Scott Cheng-Hsin Yang, Tomas Folke, Patrick Shafto","doi":"10.1111/tops.12642","DOIUrl":"10.1111/tops.12642","url":null,"abstract":"With the rise of artificial intelligence (AI) and the desire to ensure that such machines work well with humans, it is essential for AI systems to actively model their human teammates, a capability referred to as Machine Theory of Mind (MToM). In this paper, we introduce the inner loop of human-machine teaming expressed as communication with MToM capability. We present three different approaches to MToM: (1) constructing models of human inference with well-validated psychological theories and empirical measurements; (2) modeling human as a copy of the AI; and (3) incorporating well-documented domain knowledge about human behavior into the above two approaches. We offer a formal language for machine communication and MToM, where each term has a clear mechanistic interpretation. We exemplify the overarching formalism and the specific approaches in two concrete example scenarios. Related work that demonstrates these approaches is highlighted along the way. The formalism, examples, and empirical support provide a holistic picture of the inner loop of human-machine teaming as a foundational building block of collective human-machine intelligence.","PeriodicalId":47822,"journal":{"name":"Topics in Cognitive Science","volume":" ","pages":"248-267"},"PeriodicalIF":2.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12093933/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10748969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

The Role of Adaptation in Collective Human-AI Teaming. 适应性在人与人工智能集体协作中的作用

IF 2.9 2区心理学

Topics in Cognitive Science Pub Date : 2025-04-01 Epub Date: 2022-11-14 DOI: 10.1111/tops.12633

Michelle Zhao, Reid Simmons, Henny Admoni

{"title":"The Role of Adaptation in Collective Human-AI Teaming.","authors":"Michelle Zhao, Reid Simmons, Henny Admoni","doi":"10.1111/tops.12633","DOIUrl":"10.1111/tops.12633","url":null,"abstract":"This paper explores a framework for defining artificial intelligence (AI) that adapts to individuals within a group, and discusses the technical challenges for collaborative AI systems that must work with different human partners. Collaborative AI is not one-size-fits-all, and thus AI systems must tune their output based on each human partner's needs and abilities. For example, when communicating with a partner, an AI should consider how prepared their partner is to receive and correctly interpret the information they are receiving. Forgoing such individual considerations may adversely impact the partner's mental state and proficiency. On the other hand, successfully adapting to each person's (or team member's) behavior and abilities can yield performance benefits for the human-AI team. Under this framework, an AI teammate adapts to human partners by first learning components of the human's decision-making process and then updating its own behaviors to positively influence the ongoing collaboration. This paper explains the role of this AI adaptation formalism in dyadic human-AI interactions and examines its application through a case study in a simulated navigation domain.","PeriodicalId":47822,"journal":{"name":"Topics in Cognitive Science","volume":" ","pages":"291-323"},"PeriodicalIF":2.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12093936/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9339381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Hunting for Paradoxes: A Research Strategy for Cognitive Science. 寻找悖论：认知科学的研究策略。

IF 2.9 2区心理学

Topics in Cognitive Science Pub Date : 2025-04-01 DOI: 10.1111/tops.70004

Nick Chater

引用次数: 0

Fostering Collective Intelligence in Human-AI Collaboration: Laying the Groundwork for COHUMAIN. 在人类-人工智能协作中培养集体智慧：为cohumanin奠定基础。

IF 2.9 2区心理学

Topics in Cognitive Science Pub Date : 2025-04-01 Epub Date: 2023-06-29 DOI: 10.1111/tops.12679

Pranav Gupta, Thuy Ngoc Nguyen, Cleotilde Gonzalez, Anita Williams Woolley

{"title":"Fostering Collective Intelligence in Human-AI Collaboration: Laying the Groundwork for COHUMAIN.","authors":"Pranav Gupta, Thuy Ngoc Nguyen, Cleotilde Gonzalez, Anita Williams Woolley","doi":"10.1111/tops.12679","DOIUrl":"10.1111/tops.12679","url":null,"abstract":"Artificial Intelligence (AI) powered machines are increasingly mediating our work and many of our managerial, economic, and cultural interactions. While technology enhances individual capability in many ways, how do we know that the sociotechnical system as a whole, consisting of a complex web of hundreds of human-machine interactions, is exhibiting collective intelligence? Research on human-machine interactions has been conducted within different disciplinary silos, resulting in social science models that underestimate technology and vice versa. Bringing together these different perspectives and methods at this juncture is critical. To truly advance our understanding of this important and quickly evolving area, we need vehicles to help research connect across disciplinary boundaries. This paper advocates for establishing an interdisciplinary research domain-Collective Human-Machine Intelligence (COHUMAIN). It outlines a research agenda for a holistic approach to designing and developing the dynamics of sociotechnical systems. In illustrating the kind of approach, we envision in this domain, we describe recent work on a sociocognitive architecture, the transactive systems model of collective intelligence, that articulates the critical processes underlying the emergence and maintenance of collective intelligence and extend it to human-AI systems. We connect this with synergistic work on a compatible cognitive architecture, instance-based learning theory and apply it to the design of AI agents that collaborate with humans. We present this work as a call to researchers working on related questions to not only engage with our proposal but also develop their own sociocognitive architectures and unlock the real potential of human-machine intelligence.","PeriodicalId":47822,"journal":{"name":"Topics in Cognitive Science","volume":" ","pages":"189-216"},"PeriodicalIF":2.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12093911/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9697847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Shifting Between Models of Mind: New Insights Into How Human Minds Give Rise to Experiences of Spiritual Presence and Alternative Realities. 在思维模式之间转换：人类思维如何产生精神存在和替代现实体验的新见解。

IF 2.9 2区心理学

Topics in Cognitive Science Pub Date : 2025-04-01 Epub Date: 2025-03-09 DOI: 10.1111/tops.70002

Kara Weisman, Tanya Marie Luhrmann

{"title":"Shifting Between Models of Mind: New Insights Into How Human Minds Give Rise to Experiences of Spiritual Presence and Alternative Realities.","authors":"Kara Weisman, Tanya Marie Luhrmann","doi":"10.1111/tops.70002","DOIUrl":"10.1111/tops.70002","url":null,"abstract":"Phenomenal experiences of immaterial spiritual beings-hearing the voice of God, seeing the spirit of an ancestor-are a valuable and largely untapped resource for the field of cognitive science. Such experiences, we argue, are experiences of the mind, tied to mental models and cognitive-epistemic attitudes about the mind, and thus provide a striking example of how, with the right combination of mental models and cognitive-epistemic attitudes, one's own thoughts and inner sensations can be experienced as coming from somewhere or someone else. In this paper, we present results from a large-scale study of U.S. adults (N = 1779) that provides new support for our theory that spiritual experiences are facilitated by a dynamic interaction between mental models and cognitive-epistemic attitudes: A person is more likely to hear God speak if they have the epistemic flexibility and cultural support to shift, temporarily, away from a mundane model of mind into a more \"porous\" way of thinking and being. This, in turn, lays the foundation for a meditation on how mental models and cognitive-epistemic attitudes might also interact to facilitate other phenomena of interest to cognitive science, such as fiction writing and scientific discovery.","PeriodicalId":47822,"journal":{"name":"Topics in Cognitive Science","volume":" ","pages":"144-179"},"PeriodicalIF":2.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143587711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Introduction to topiCS Volume 17, Issue 2. 主题导论第17卷，第2期。

IF 2.9 2区心理学

Topics in Cognitive Science Pub Date : 2025-04-01 Epub Date: 2025-03-24 DOI: 10.1111/tops.70006

Andrea Bender

引用次数: 0

Human Performance in Competitive and Collaborative Human-Machine Teams. 竞争和协作人机团队中的人的表现。

IF 2.9 2区心理学

Topics in Cognitive Science Pub Date : 2025-04-01 Epub Date: 2023-07-13 DOI: 10.1111/tops.12683

Murray S Bennett, Laiton Hedley, Jonathon Love, Joseph W Houpt, Scott D Brown, Ami Eidels

{"title":"Human Performance in Competitive and Collaborative Human-Machine Teams.","authors":"Murray S Bennett, Laiton Hedley, Jonathon Love, Joseph W Houpt, Scott D Brown, Ami Eidels","doi":"10.1111/tops.12683","DOIUrl":"10.1111/tops.12683","url":null,"abstract":"In the modern world, many important tasks have become too complex for a single unaided individual to manage. Teams conduct some safety-critical tasks to improve task performance and minimize the risk of error. These teams have traditionally consisted of human operators, yet, nowadays, artificial intelligence and machine systems are incorporated into team environments to improve performance and capacity. We used a computerized task modeled after a classic arcade game to investigate the performance of human-machine and human-human teams. We manipulated the group conditions between team members; sometimes, they were instructed to collaborate, compete, or work separately. We evaluated players' performance in the main task (gameplay) and, in post hoc analyses, participant behavioral patterns to inform group strategies. We compared game performance between team types (human-human vs. human-machine) and group conditions (competitive, collaborative, independent). Adapting workload capacity analysis to human-machine teams, we found performance under both team types and all group conditions suffered a performance efficiency cost. However, we observed a reduced cost in collaborative over competitive teams within human-human pairings, but this effect was diminished when playing with a machine partner. The implications of workload capacity analysis as a powerful tool for human-machine team performance measurement are discussed.","PeriodicalId":47822,"journal":{"name":"Topics in Cognitive Science","volume":" ","pages":"324-348"},"PeriodicalIF":2.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12093930/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9770733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Self-beliefs, Transactive Memory Systems, and Collective Identification in Teams: Articulating the Socio-Cognitive Underpinnings of COHUMAIN. 团队中的自我信念、交互记忆系统和集体认同：阐明共同认知的社会认知基础。

IF 2.9 2区心理学

Topics in Cognitive Science Pub Date : 2025-04-01 Epub Date: 2023-07-04 DOI: 10.1111/tops.12681

Ishani Aggarwal, Gabriela Cuconato, Nüfer Yasin Ateş, Nicoleta Meslec

{"title":"Self-beliefs, Transactive Memory Systems, and Collective Identification in Teams: Articulating the Socio-Cognitive Underpinnings of COHUMAIN.","authors":"Ishani Aggarwal, Gabriela Cuconato, Nüfer Yasin Ateş, Nicoleta Meslec","doi":"10.1111/tops.12681","DOIUrl":"10.1111/tops.12681","url":null,"abstract":"Socio-cognitive theory conceptualizes individual contributors as both enactors of cognitive processes and targets of a social context's determinative influences. The present research investigates how contributors' metacognition or self-beliefs, combine with others' views of themselves to inform collective team states related to learning about other agents (i.e., transactive memory systems) and forming social attachments with other agents (i.e., collective team identification), both important teamwork states that have implications for team collective intelligence. We test the predictions in a longitudinal study with 78 teams. Additionally, we provide interview data from industry experts in human-artificial intelligence teams. Our findings contribute to an emerging socio-cognitive architecture for COllective HUman-MAchine INtelligence (i.e., COHUMAIN) by articulating its underpinnings in individual and collective cognition and metacognition. Our resulting model has implications for the critical inputs necessary to design and enable a higher level of integration of human and machine teammates.","PeriodicalId":47822,"journal":{"name":"Topics in Cognitive Science","volume":" ","pages":"217-247"},"PeriodicalIF":2.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12093922/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9758826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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