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.

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.

The expression "repressed memory" was introduced over 100 years ago as a theoretical term purportedly referring to an unobservable psychological entity postulated by Freud's seduction theory. That theory, however, and its hypothesized cognitive architecture, have been thoroughly debunked-yet the term "repressed memory" seems to remain. In this paper, I offer a philosophical evaluation of the meaning of this theoretical term as well as an argument to question its scientific status by comparing it to other cases of theoretical terms that have either survived scientific change-such as "atom" or "gene"-or that have perished, such as "black bile." Ultimately, I argue that "repressed memory" is more like "black bile" than "atom" or "gene" and, thus, recommend its demotion from our scientific vocabulary.

Landscapes are essential to human life: they provide a multitude of material (food, water, pollination) and nonmaterial (beauty, tranquility, recreation) values. Their importance is enshrined in international conventions and treaties, committing signatories to protecting, monitoring, and managing all landscapes. Yet, relatively little is known about how people conceptualize "landscape" and its constituents. There is emerging evidence that conceptualizations of landscape entities may influence landscape management. This in turn raises the question as to how people speaking different languages, and with differing levels of expertise, may differ in conceptualizing landscape domains as a whole. In this paper, we investigated how people conceptualize landscape-related terms in a specific domain-waterbodies-by comparing German and English-speaking experts and nonexperts. We identified commonly used waterbody terms in sustainability discourses in both languages, and used those terms to collect sensory, motor, and affective ratings from participants. Speakers of all groups appear to conceptualize the domain of waterbody terms in comparable ways. Nevertheless, we uncovered subtle differences across languages for nonexperts. For example, there were differences in which waterbodies were associated with calm happiness in each language. In addition, olfaction seemingly plays a role in English speakers' conceptualization of waterbodies, but not German speakers. Taken together, this suggests the ways in which people relate to landscape although shared in many respects may also be shaped in part by their specific language and culture.

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.

Computational feasibility is a widespread concern that guides the framing and modeling of natural and artificial intelligence. The specification of cognitive system capacities is often shaped by unexamined intuitive assumptions about the search space and complexity of a subcomputation. However, a mistaken intuition might make such initial conceptualizations misleading for what empirical questions appear relevant later on. We undertake here computational-level modeling and complexity analyses of segmentation - a widely hypothesized subcomputation that plays a requisite role in explanations of capacities across domains, such as speech recognition, music cognition, active sensing, event memory, action parsing, and statistical learning - as a case study to show how crucial it is to formally assess these assumptions. We mathematically prove two sets of results regarding computational hardness and search space size that may run counter to intuition, and position their implications with respect to existing views on thesubcapacity.

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 navigationdomain.

In the recent debate about the unity and integration of cognitive science (Núñez et al., 2019, 2020; see also commentaries in topiCS 11:4, introduced by Gray, 2019), one focus of the argument rested on the predominance of cognitive psychology and the displacement of smaller disciplines, such as anthropology and philosophy. Much less attention has been paid to the fact that another key player in the genesis of cognitive science has been withdrawing from the joint endeavor: artificial intelligence (AI) and computer science (Forbus, 2010; Goel, 2019). Although not framed in terms of the recent debate, the two topics in the current issue of topiCS are germane to this concern, as they both focus on cognitive modeling—arguably a signature approach of cognitive science and a natural link to AI. The call for rapprochement is clearest in the first topic, Cognition-Inspired Artificial Intelligence, edited by Daniel N. Cassenti (DEVCOM Army Research Laboratory), Vladislav D. Veksler (Caldwell University), and Frank E. Ritter (Pennsylvania State University). To showcase how cognitive science has not just benefitted from advances in AI, but can and should inspire AI development, Cassenti, Veksler, and Ritter bring together contributions from researchers actively using cognitive modeling to tackle a wide range of cognitive phenomena. Incidentally, the other topic in this issue seconds this call for greater attention to and consideration of cognitive models by presenting spearheading work in this very field. For their topic, Terrence C. Stewart (National Research Council Canada) and Joost de Jong (Maastricht University) have assembled revised and expanded versions of the five best papers presented at last year's 19th International Conference on Cognitive Modeling, a conference devoted to computational systems that are aimed at reflecting the internal processes of the mind. In their introduction, Stewart and de Jong point out how these papers, despite their diversity in content, still complement one another in terms of focus and approach: by refining and advancing computational models to better reflect empirical data, or by using such models to better explain data. Congratulations to their authors for their awards––we hope you continue the outstanding work you are doing! topiCS encourages letters and commentaries on all topics, as well as proposals for new topics. Letters are not longer than two published pages (ca. 400–1000 words). Commentaries (between 1000 and 2000 words) are often solicited by Topic Editors prior to the publication of their topic, but they may also be considered after publication. Letters and commentaries typically come without abstracts and with few references, if any. The Executive Editor and the Senior Editorial Board (SEB) are constantly searching for new and exciting topics for topiCS. Feel free to open communications with a short note to the Executive Editor ([email protected]) or a member of the SEB (for a list, see the publisher's homepage for topiCS: http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1756-8765/homepage/EditorialBoard.html).

Cognitive science provides background research that can be leveraged by artificial intelligence (AI) developers. In this special issue, we explore methods of inspiring AI development using the results of cognitive science research.

In this study, we explore the future of work by examining differences in productivity when teams are composed of only humans or both humans and machine agents. Our objective was to characterize the similarities and differences between human and human-machine teams as they work to coordinate across their specialized roles. This form of research is increasingly important given that machine agents are becoming commonplace in sociotechnical systems and playing a more active role in collaborative work. One particular class of machine agents, bots, is being introduced to these systems to facilitate both taskwork and teamwork. We investigated the association between bots and productivity outcomes in open source software development through an analysis of hundreds of project teams. The presence of bots in teams was associated with higher levels of productivity and higher work centralization in addition to greater amounts of observed communication. The adoption of bots in software teams may have tradeoffs, in that doing so may increase productivity, but could also increase workload. We discuss the theoretical and practical implications of these findings for advancing human-machine teaming research.