During the early postnatal years, most infants rapidly learn to understand two naturally-evolved communication systems: language and emotion. While these two domains include different types of content knowledge, it is possible that similar learning processes subserve their acquisition. In this review, we compare the learnable statistical regularities in language and emotion input. We then consider how domain-general learning abilities may underly the acquisition of language and emotion, and how this process may be constrained in each domain. This comparative developmental approach can advance our understanding of how humans learn to communicate with others.

Consideration of collectives raises important questions about human rationality. This has long been known for questions about preferences, but it holds also with respect to beliefs. For one, there are contexts (such as voting) where we might care as much, or more, about the rationality of a collective than the rationality of the individuals it comprises. Here, a given standard may yield competing assessments at the individual and the collective level, thus giving rise to important normative questions. At the same time, seemingly rational strategies of individuals may have surprising consequences, or even fail, when exercised by individuals within collectives. This paper will illustrate these considerations with examples, provide an overview of different formal frameworks for understanding and assessing the beliefs of collectives, and it will illustrate how such frameworks can combine with simulations in order to elucidate epistemicnorms.

The ease with which humans usually perform everyday activities masks their inherit complexity. Tasks such as setting a table prior to a meal or preparing a hot beverage require the coordination of several cognitive abilities. At the same time, many everyday activities are simple enough to afford investigation in controlled lab settings. One main goal of this issue is to raise awareness of everyday activities as a topic and a field of study in its own right, which allows investigating (a) selected cognitive abilities with high ecological validity and (b) the interplay and integration of key cognitive abilities. To this end, this topic consists of eight papers that span different aspects of everyday activities, ranging from neuroscience through philosophical considerations and implications to lessons fromrobotics.

The ability to anticipate team members' actions enables joint action towards a common goal. Task knowledge and mental simulation allow for anticipating other agents' actions and for making inferences about their underlying mental representations. In human-AI teams, providing AI agents with anticipatory mechanisms can facilitate collaboration and successful execution of joint action. This paper presents a computational cognitive model demonstrating mental simulation of operators' mental models of a situation and anticipation of their behavior. The work proposes two successive steps: (1) A hierarchical cluster algorithm is applied to recognize patterns of behavior among pilots. These behavioral clusters are used to derive commonalities in situation models from empirical data (N = 13 pilots). (2) An ACT-R (adaptive control of thought - rational) cognitive model is implemented to mentally simulate different possible outcomes of action decisions and timing of a pilot. model tracing of ACT-R allows following up on operators' individual actions. Two models are implemented using the symbolic representations of ACT-R: one simulating normative behavior and the other by simulating individual differences and using subsymbolic learning. Model performance is analyzed by a comparison of both models. Results indicate the improved performance of the individual differences over the normative model and are discussed regarding implications for cognitive assistance capable of anticipating operatorbehavior.

This year's first issue of Topics in Cognitive Science comprises two very distinct topics: one devoted to a new type of papers authored by a special set of scholars, the Fellows of the Cognitive Science Society, the other being a special issue on an exciting topic, namely an approach to modeling the mind based on network science. Election as Fellow of the Society recognizes a scholar's sustained excellence and impact on the Cognitive Science community. The Fellows Topic was initiated in 2021 by the incoming Executive Editor to provide these scholars an opportunity for showcasing their scientific work and outlining what they consider to be its major contributions to the field. Intended as an on-going and open-ended forum for all Fellows, the topic is launched here with a small, but fine selection of papers. In the preface to the Fellows Topic (Bender, 2022), the editor depicts its multilayered agenda and introduces the two Fellows who inaugurate the topic: Cleotilde ‘Coty’ Gonzalez from Carnegie Mellon University and Steven Sloman from Brown University, renowned for their work on, respectively, how people make decisions (summarized here in Gonzalez, 2022) and how they think (Sloman, 2022). The two not only do a great job outlining the answers that their research provides to fundamental questions in cognitive science, but they also provide a bird's eye view that allows for reflection on the field more broadly. The topiCS team wishes to take this opportunity to thank Coty and Steve for their wonderful papers; to congratulate all Fellows of the Cognitive Science Society—and especially the newly elected recipients—for their awards and for the outstanding work that earned them these awards, and to extend an invitation to submit to this topic to all Fellows. The second topic in this issue, edited by Yoed N. Kenett (Technion, Israel Institute of Technology, Haifa) and Thomas Hills (Department of Psychology, University of Warwick, UK), presents network science as a powerful approach to studying cognition. As pointed out in the Editors’ Introduction to their topic (Kenett & Hills, 2022), network science terminology was already used in classic cognitive theory half a century ago, to describe how concepts are stored and organized in semantic memory. Spanning a broad range of themes, the collection of papers recruited for “Networks of the Mind” illustrates how much more network science can help to achieve: from modeling individual information encoding to large-scale processes of culture change, and from investigating a wide range of applications to advancing theory-building in cognitive science more broadly, thereby opening up promising and exciting avenues for future developments. 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 abstract 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 (Andrea.Bender@uib.no) 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).

Improving the accuracy of medical image interpretation can improve the diagnosis of numerous diseases. We compared different approaches to aggregating repeated decisions about medical images to improve the accuracy of a single decision maker. We tested our algorithms on data from both novices (undergraduates) and experts (medical professionals). Participants viewed images of white blood cells and made decisions about whether the cells were cancerous or not. Each image was shown twice to the participants and their corresponding confidence judgments were collected. The maximum confidence slating (MCS) algorithm leverages metacognitive abilities to consider the more confident response in the pair of responses as the more accurate "final response" (Koriat, 2012), and it has previously been shown to improve accuracy on our task for both novices and experts (Hasan etal., 2021). We compared MCS to similarity-based aggregation (SBA) algorithms where the responses made by the same participant on similar images are pooled together to generate the "final response." We determined similarity by using two different neural networks where one of the networks had been trained on white blood cells and the other had not. We show that SBA improves performance for novices even when the neural network had no specific training on white blood cell images. Using an informative representation (i.e., network trained on white blood cells) allowed one to aggregate over more neighbors and further boosted the performance of novices. However, SBA failed to improve the performance for experts even with the informative representation. This difference in efficacy of the SBA suggests different decision mechanisms for novices andexperts.

Traditional statistics instruction emphasizes a .05 significance level for hypothesis tests. Here, we investigate the consequences of this training for researchers' mental representations of probabilities - whether .05 becomes a boundary, that is, a discontinuity of the mental number line, and alters their reasoning about p-values. Graduate students with statistical training (n = 25) viewed pairs of p-values and judged whether they were "similar" or "different." After controlling for several covariates, participants were more likely and faster to judge p-values as "different" when they crossed the .05 boundary (e.g., .046 vs. .052) compared to when they did not (e.g., .026 vs. .032). This result suggests a categorical perception-like effect for the processing of p-values. It may be a consequence of traditional statistical instruction creating a psychologically real divide between so-called statistical "significance" and "nonsignificance." Such a distortion is undesirable given modern approaches to statistical reasoning that de-emphasize dichotomizing the p-value continuum.

Tasks, tools, and techniques that we perform, use, and acquire, define the elements of expertise which we value as the hallmarks of goal-driven behavior. Somehow, the creation of tools enables us to define new tasks, or is it that the envisioning of new tasks drives us to invent new tools? Or maybe it is that new tools engender new techniques which then result in new tasks? This jumble of issues will be explored and discussed in this diverse collection of papers. Individually, few of the papers are related to each other by topic or by techniques of analysis. Collectively, all focus on tasks performed using tools and discuss the techniques of tool use which enable differences in performance and expertise across individuals, societies, and (even) species.

Acquiring expertise in a task is often thought of as an automatic process that follows inevitably with practice according to the log-log law (aka: power law) of learning. However, as Ericsson, Chase, and Faloon (1980) showed, this is not true for digit-span experts and, as we show, it is certainly not true for Tetris players at any level of expertise. Although some people may simply "twitch" faster than others, the limit to Tetris expertise is not raw keypress time but the techniques acquired by players that allow them to use the tools provided by the hardware and software to compensate for the game's relentlessly increasing drop speed. Unfortunately, these increases in drop speed between Tetris levels make performance plateaus very short and quickly followed by game death. Hence, a player's success at discovering, exploring, and practicing new techniques for the tasks of board preparation, board maintenance, optimal placement discovery, zoid rotation, lateral movement of zoids, and other tasks important to expertise in Tetris is limited. In this paper, we analyze data collected from 492 Tetris players to reveal the challenges they confronted while constructing expertise via the discovery of new techniques for gameplay at increasingly difficult levels ofTetris.