Reliably identifying relevant brain areas implicated by the simulated activity from cognitive models is still an unsolved problem for cognitive modeling, particularly when matching model output with human electroencephalography (EEG) data. We propose a new method involving postprocessing of ACT-R module activity and clustered EEG component activity, and performing generalized least squares analysis to find matching patterns between predicted and observed data, thereby inferring neural substrates of distinct cognitive processes. This approach holds several advantages over other methods by controlling for autocorrelation and unequal variances. To exemplify its application, we used a cognitive model and EEG data from a mental spatial transformation study to show how this method finds areas involved in representational and transformational spatial processing. Parietal areas involved with spatial activity were identified, in line with prior studies on spatial cognition. In addition, previously established associations between ACT-R and brain areas were confirmed. Finally, we discuss limitations and possibilities of the approach.

It has been argued that fungi have cognitive capacities, and even conscious experiences. While these arguments risk ushering in unproductive disputes about how words like "mind," "cognitive," "sentient," and "conscious" should be used, paying close attention to key properties of fungal life can also be uncontroversially productive for cognitive science. Attention to fungal life can, for example, inspire new, potentially fruitful directions of research in cognitive science. Here, I introduce a concept of cognitive symbiosis whose significance for cognitive science becomes salient when we consider the centrality of symbioses in the life of fungi. Like fungi, virtually all cognitive systems live in close association with other kinds of cognitive systems, and this living together can have substantive psychological consequences. Expanding the scope of cognitive science to study a wide variety of cognitive symbioses underwrites the importance of biology and evolution in understanding minds.

This summer, the Cognitive Science Society is celebrating the 25th Anniversary of the David E. Rumelhart Prize. Since its establishment and generous endowment in 2000 by the Robert J. Glushko and Pamela Samuelson Fund, this equivalent of a Nobel Prize for Cognitive Science has been awarded annually to scholars making a significant contemporary contribution to the theoretical foundations of human cognition. To honor this legacy, CogSci 2025 is hosting a special symposium prior to this year's conference, “featuring a lineup of visionary thinkers whose work exemplifies the Rumelhart Prize's spirit” (https://cognitivesciencesociety.org/rumelhart-25th-anniversary-event/). And the recurring highlight of CogSci proper will be the talk by the current laureate, Nora S. Newcombe, followed by the announcement of the 26th recipient of the prize. topiCS is marking the celebrations by publishing a very special issue honoring the 2021 recipient Susan Goldin-Meadow and the 2023 recipient Nick Chater in a unique twin format—the former introduced by Alibali and Cook (IN PRESS [this issue]), the latter by Christiansen and Oaksford (IN PRESS [this issue]). Finally, we are especially honored to have been provided with reflections on David Rumelhart and the Prize in his name by one of Rumelhart's closest collaborators, the first chair of the Prize committee, and himself the 2010 recipient of the prize, James L. (“Jay”) McClelland (IN PRESS [this issue]). 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 an 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 ([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).

The cumulative evolution of technology has proven central to our species' ecological success, allowing for cultural rather than biological adaptation to environmental challenges. While cumulative improvement explains how specific technological traditions can get increasingly better at solving pre-existing adaptive problems, it remains fundamentally an optimization process, one which halts when an optimal solution is found. Yet, humans are also capable of open-ended or evolvable technological change, that is, we have the capacity for generating novel and useful technological solutions for an ever-expanding set of increasingly complex problems. How novel problems of increasing complexity are accessed, however, remains an open issue. Here, I argue that human open-ended technological evolution emerges from the cultural evolutionary bootstrapping of our inventive capabilities through cognitive technologies. By inventing technologies that enhance our cognitive capabilities, we become able to invent technologies that would have been impossible to design using only our core (noncultural) cognitive abilities. These inventions include further empowering cognitive technologies, creating a feedback loop through which inventors become increasingly capable of making themselves even more capable inventors. I propose a model for how the cultural evolution of increasingly sophisticated cognitive technologies enables access to previously unreachable invention problems, driving open-ended technological change. This process differs from cumulative optimization as it involves expanding the range of problems that can be solved (evolvability) rather than optimizing solutions to existing problems (adaptation). This paper contributes to our understanding of human technological uniqueness by identifying a mechanism enabling open-ended cultural evolution.

Peer-assisted learning has the potential to improve learning in academic settings and beyond. However, the cognitive and motivational mechanisms of learning through interaction with other learners are not fully understood. Here, we present an empirical study in which we compare a peer-assisted learning condition with two individual learning conditions. The empirical findings suggest that both positive and negative peer effects occurred. On the positive side, learners placed in a peer-assisted learning condition allocated more time to practice and they benefited from selectively interacting with the more knowledgeable peers. On the negative side, error exposure and increased cognitive load may have hindered learning in the peer-assisted learning condition. A computational cognitive model developed in the ACT-R cognitive architecture is presented and used to explain the mechanisms of knowledge spillover, trust, and error exposure. This research has implications for designing collaborative learning protocols to increase human collective intelligence and designing artificial intelligence systems that can support human-machine teaming.

This Topics in Cognitive Science issue starts the new year off with two collections entirely devoted to excellent and award-winning research: two additions to the (ongoing) topic honoring the Fellows of our Society and a handful of award-winning papers from our annual conference, CogSci, last year. The first topic aims to introduce the fellows of the Cognitive Science Society to a broad and diverse audience beyond the Fellows’ own field and, in so doing, to help bridge the disciplines that together constitute cognitive science (Bender, 2022). In the 3 years since its inception, the Fellows Topic has had the honor to host such overarching and integrative contributions from six outstanding scholars, who are joined now by two great thinkers: Nancy J. Nersessian (Georgia Institute of Technology, US) and Peter Gärdenfors (Lund University, Sweden). The honor of kicking this off belongs to Nancy J. Nersessian, grande dame of the philosophy of (cognitive) science. Her commitment to the Cognitive Science Society had her chair its Fellows Inaugural Committee (2001/02) and serve as president of the Society (2003/04), and in 2009, she was elected a Fellow herself. Having started off as a natural scientist and culminating as a cognitive scientist, she has helped to create—and uniquely shaped—an entire research field in her own way, the cognitive science of science. In “How do scientists think?” (Nersessian, in press), she presents this lifelong research program by interweaving it with her academic biography, thereby illustrating not just “how scientists think” more generally, but also how this applies to this particular scientist. Her Fellows paper is a passionate account of research into the nature of scientific thinking, to which she hopes (as she confided to me) to recruit more young researchers. The second contribution to this topic is different both in style and content. Its author, Peter Gärdenfors, is Sweden's likely most eminent philosopher and professor emeritus of cognitive science in one of the oldest universities in Northern Europe. He has been elected to several scientific academies, was a member of the Nobel Prize Committee for economics for 6 years, and became a Fellow of the Cognitive Science Society in 2016. His academic work, too, is characterized by crossing disciplinary boundaries, from philosophy to cognitive science and deep into questions in linguistics. For his Fellows contribution, “The geometry and dynamics of meaning” (Gärdenfors, in press), he chose a less biographical approach and instead provides a highly educational introduction to his influential theory of conceptual space, aimed at modeling why and how even very young minds are able to map words onto meaning when learning a language. 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 ([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).

Abstract Predictive processing is an influential theoretical framework for understanding human and animal cognition. In the context of predictive processing, learning is often reduced to optimizing the parameters of a generative model with a predefined structure. This is known as Bayesian parameter learning. However, to provide a comprehensive account of learning, one must also explain how the brain learns the structure of its generative model. This second kind of learning is known as structure learning. Structure learning would involve true structural changes in generative models. The purpose of the current paper is to describe the processes involved upstream of these structural changes. To do this, we first highlight the remarkable compatibility between predictive processing and the processing fluency theory. More precisely, we argue that predictive processing is able to account for all the main theoretical constructs associated with the notion of processing fluency (i.e., the fluency heuristic, naïve theory, the discrepancy‐attribution hypothesis, absolute fluency, expected fluency, and relative fluency). We then use this predictive processing account of processing fluency to show how the brain could infer whether it needs a structural change for learning the causal regularities at play in the environment. Finally, we speculate on how this inference might indirectly trigger structural changes when necessary.

The 1998 article by van Gelder proposed a Dynamical Hypothesis (DH) in cognitive science consisting of Nature (cognitive agents are dynamical systems) and Knowledge (cognitive agents should be understood dynamically) hypotheses in contrast to the Computational Hypothesis (CH) that cognitive agents are computers. My commentary focuses on the contributions of Paxton and Necaise etal. in interpersonal motor coordination and radicalization across social media. I do not think that either contribution supports the Nature hypothesis but does conform with the Knowledge hypothesis. I conclude by describing cognitive agents as living systems (or nonliving systems that mimic aspects of living systems) that can be alternately viewed to support the DH or CH or both at the same time.