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Cognitive modeling involves the creation of computer simulations that emulate the internal processes of the mind. This set of papers are the five best representatives of the papers presented at the 17th International Conference on Cognitive Modeling, ICCM 2019. While they represent a diversity of techniques and tasks, they all also share a striking similarity: They make strong statements about the importance of accounting for individual differences., (© 2020 Her Majesty the Queen in Right of Canada Topics in Cognitive Science © 2020 Cognitive Science Society.)

Teams are a fundamental aspect of life-from sports to business, to defense, to science, to education. While the cognitive sciences tend to focus on information processing within individuals, others have argued that teams are also capable of demonstrating cognitive capacities similar to humans, such as skill acquisition and forgetting (cf., Cooke, Gorman, Myers, & Duran, 2013; Fiore et al., 2010). As artificially intelligent and autonomous systems improve in their ability to learn, reason, interact, and coordinate with human teammates combined with the observation that teams can express cognitive capacities typically seen in individuals, a cognitive science of teams is emerging. Consequently, new questions are being asked about teams regarding teamness, trust, the introduction and effects of autonomous systems on teams, and how best to measure team behavior and phenomena. In this topic, four facets of human-autonomy team cognition are introduced with leaders in the field providing in-depth articles associated with one or more of the facets: (1) defining teams; (2) how trust is established, maintained, and repaired when broken; (3) autonomous systems operating as teammates; and (4) metrics for evaluating team cognition across communication, coordination, and performance., (© 2024 Cognitive Science Society LLC.)

Cognitive researchers often carve cognition up into structures and processes. Cognitive processes operate on structures, like vehicles driving over a map. Language alongside semantic and episodic memory are proposed to have structure, as are perceptual systems. Over these structures, processes operate to construct memory and solve problems by retrieving and manipulating information. Network science offers an approach to representing cognitive structures and has made tremendous inroads into understanding the nature of cognitive structure and process. But is the mind a network? If so, what kind? In this article, we briefly review the main metaphors, assumptions, and pitfalls prevalent in cognitive network science (maps and vehicles; one network/process to rule them all), highlight the need for new metaphors that elaborate on the map-and-vehicle framework (wormholes, skyhooks, and generators), and present open questions in studying the mind as a network (the challenge of capturing network change, what should the edges of cognitive networks be made of, and aggregated vs. individual-based networks). One critical lesson of this exercise is that the richness of the mind as network approach makes it a powerful tool in its own right; it has helped to make our assumptions more visible, generating new and fascinating questions, and enriching the prospects for future research. A second lesson is that the mind as a network-though useful-is incomplete. The mind is not a network, but it may contain them., (© 2021 Cognitive Science Society LLC.)

Is cognitive science interdisciplinary or multidisciplinary? We contribute to this debate by examining the authorship structure and topic similarity of contributions to the Cognitive Science Society from 2000 to 2019. Our analysis focuses on graph theoretic features of the co‐authorship network—edge density, transitivity, and maximum subgraph size—as well as clustering within the space of scientific topics. We also combine structural and semantic information with an analysis of how authors choose their collaborators based on their interests and prior collaborations. We compare findings from CogSci to abstracts from the Vision Science Society over the same time frame and validate our approach by predicting new collaborations in the 2020 CogSci proceedings. Our results suggest that collaboration across authors and topics within cognitive science has become increasingly integrated in the last 19 years. More broadly, we argue that a formal quantitative approach which combines structural co‐authorship information and semantic topic analysis provides inroads to questions about the level of interdisciplinary collaboration in a scientific community. DeStefano, Oey, Brockbank, and Vul explore interdisciplinary collaboration using data‐driven measures of research topics and co‐authorship, constructed from a rich dataset of over 11,000 Cogsci conference papers. Findings suggest the cognitive science research community has become increasingly integrated in the last 19 years. [ABSTRACT FROM AUTHOR]

Cognitive science has much to contribute to the general scientific body of knowledge, but it is also a field rife with possibilities for providing background research that can be leveraged by artificial intelligence (AI) developers. In this introduction, we briefly explore the history of AI. We particularly focus on the relationship between AI and cognitive science and introduce this special issue that promotes the method of inspiring AI development with the results of cognitive science research., (© 2022 Cognitive Science Society LLC.)

Computational models of memory presented in this issue reflect varied empirical data and levels of representation. From mathematical models to neural and cognitive architectures, all aim to converge on a unified theory of the mind. [ABSTRACT FROM AUTHOR]

Cognitive science has converged in many ways with cognitive psychology, but while also maintaining a distinctive interdisciplinary nature. Here we further characterize this existing state of the field before proposing how it might be reconceptualized toward a broader and more distinct, and thus more stable, position in the realm of sciences., (© 2019 Cognitive Science Society, Inc.)

An introduction is presented in which the editor discusses various reports within the issue including the rationality of human cognition and the papers presented at the Cognitive Science Society Annual conference and the International Conference on Cognitive Modeling.

To introduce our special issue How Minds Work: The Collective in the Individual, we propose "radical CI," a form of collective intelligence, as a new paradigm for cognitive science. Radical CI posits that the representations and processes necessary to perform the cognitive functions that humans perform are collective entities, not encapsulated by any individual. To explain cognitive performance, it appeals to the distribution of cognitive labor on the assumption that the human project runs on countless interactions between locally acting individuals with specialized skills that each retain a small part of the relevant information. Some of the papers in the special issue appeal to radical CI to account for a variety of cognitive phenomena including memory performance, metacognition, belief updating, reasoning, and problem‐solving. Other papers focus on the cultural and institutional practices that make radical CI possible. The notion that cognition is collective has a long pedigree but is enjoying considerable attention in the cognitive sciences as multiple fields of study deliver evidence for the central role of community and culture in cognition. To introduce the special issue of topiCS surveying this groundswell, we propose the concept of "radical collective intelligence," the view that some of the key representations and processes necessary to perform the cognitive functions that humans perform are collective entities, not encapsulated by any individual. This concept clarifies how the volume's contributions either rethink long‐studied cognitive processes (memory, metacognition, reasoning) or contemplate how radical CI can arise. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses articles in the issue on topics including cognitive science, language use, and global change.

Events and event prediction are pivotal concepts across much of cognitive science, as demonstrated by the papers in this special issue. We first discuss how the study of events and the predictive processing framework may fruitfully inform each other. We then briefly point to some links to broader philosophical questions about events. Hohwy, Hebblewhite, and Drummond detail links between the contributions in the special issue and the general predictive processing framework and emphasize the contributions' potential to help answer philosophical questions about the nature on events. [ABSTRACT FROM AUTHOR]

The article offers information on the Best Of topic recognition of the journal "Topics in Cognitive Science" (topiCS). The Best Of topic is designed to offer a cognitive science outlet for the best papers that had been published in nonarchival conference proceedings. The papers are required to undergo two reviews to be invited for the journal's recognition. The winning CogSci2008 papers include "Children's grammars grow more abstract with age -- Evidence from an automatic procedure for identifying the productive units of language," by Gideon Borensztajn, Willem Zuidema and Rens Bod.

The papers in this issue have specific and focused targets that are essential for making incremental progress on the very difficult problem of identifying the coevolutionary interactions of cognition and culture. The purpose of this paper is to discern the shape of a few of the large problems that loom over these more narrowly focussed papers, and to explain and assess the ways these papers contribute to their solution. The background problems described are (a) the character of the selective interactions between the evolution of culture and of cognition; (b) the special features of cumulative cultural evolution; and (c) the place of language in an account of cognition–culture coevolution. The paper ends with some reflections on the extraordinarily difficult challenge of testing scenarios in this field. In his profound discussion, Sterelny draws out common themes in the contributions to this topic: selective drivers in the coevolution of cognition and culture, the role of language in it, characteristics of cumulative cultural evolution, and issues of testability. He highlights the growing body of evidence for positive feedback mechanisms in cultural evolution, but also notes that progress is piecemeal, calling for more cross‐border work between cognitive science and research on cultural evolution. [ABSTRACT FROM AUTHOR]

The article offers information on the journal "Topics in Cognitive Science" (topiCS) of the Cognitive Science Society. The journal aims to present a growth in the range and scope of cognitive science by a forum of three categories of work which are New and Emerging, Integrative and Reflective, and Great Debates. The role of the publication's Associate Editor includes defining a topic for the journal, looking for strong papers, and managing the process of reviews. The journal also indicates policy for the acceptance of paper to be published.

Drawing on original ethnobotanical and anthropological research among Indigenous peoples across the Amazon, we examine synergies and dissonances between Indigenous and Western scientific knowledge about the environment, resource use, and sustainability. By focusing on the sensory dimension of Indigenous engagements with the environment—an approach we have described as "sensory ecology" and explored through the method of "phytoethnography"—we promote a symmetrical dialogue between Indigenous and scientific understandings around such phenomena as animal–plant mutualisms, phytochemical toxicity, sustainable forest management in "multinatural" landscapes, and the emergence of new diseases like the novel coronavirus SARS‐CoV‐2 (COVID‐19). Drawing examples from our own and other published works, we explore the possibilities and limitations of a "parallax view" attempting to hold Indigenous and scientific knowledge in focus simultaneously. As the concept of "bioeconomy" emerges as a key alternative for sustainable development of the Amazon, we encourage a critical and urgent engagement between dominant Western conceptions and Indigenous Amazonian knowledge, practices, and cultural values. Cognitive science, which has long contributed to studies of Indigenous categorization and conceptualization of the natural world, continues to play an important role in building bridges of mutual communication and respect between Indigenous and scientific approaches to sustainability and biodiversity conservation. This paper explores the possibilities and limitations of a "parallax view" attempting to hold Indigenous and scientific knowledge in focus simultaneously in order to promote interdisciplinary and intercultural dialogue, sustainability, and biodiversity conservation. [ABSTRACT FROM AUTHOR]

This is the Editor's introduction to the Special Issue of TopiCS in honor of Lila R. Gleitman's receipt of the 2017 David E. Rumelhart Prize. The introduction gives an overview of Gleitman's intellectual history and scientific contributions, and it briefly reviews each of the contributions to the issue. Landau introduces the volume with a selective review of Lila R. Gleitman's intellectual history, emphasizing the theoretical roots of her research. These include influences of Zellig Harris and Noam Chomsky, her creation of "The Great Verb Game" (which paved the way for the theory of syntactic bootstrapping), the importance of natural "deprivation" experiments, and how they shed light on understanding what the data for learning really might be, and her life as an empiricist, driven by data to nativist conclusions. The introduction also provides brief summaries of each contributed paper. [ABSTRACT FROM AUTHOR]

Thirty years after the publication of Marr's seminal book Vision (Marr, 1982) the papers in this topic consider the contemporary status of his influential conception of three distinct levels of analysis for information-processing systems, and in particular the role of the algorithmic and representational level with its cognitive-level concepts. This level has (either implicitly or explicitly) been downplayed or eliminated both by reductionist neuroscience approaches from below that seek to account for behavior from the implementation level and by Bayesian approaches from above that seek to account for behavior in purely computational-level terms. [ABSTRACT FROM AUTHOR]

Though the scientific study of surprise dates back to Darwin (), there was an upsurge in interest beginning in the 1960s and 70s, and this has continued to the present. Recent developments have shed much light on the cognitive mechanisms and consequences of surprise, but research has often been siloed within sub‐areas of Cognitive Science. A central challenge for research on surprise is, therefore, to connect various research programs around their overlapping foci. This issue has its roots in a symposium on surprise, entitled "Triangulating Surprise: Expectations, Uncertainty, and Making Sense," at the 36th Annual Conference of the Cognitive Science Society (Quebec City, July 2014). Building on the interdisciplinary conversations that started at the symposium, this issue aims to draw attention to some promising empirical and modeling results and their theoretical implications. The present paper sets the stage for the issue by presenting a historical summary, discussing contrasting definitions of surprise, and then by tracing major threads that run through both this issue and the larger literature on surprise. Our aim is to develop broader, shared understandings of the main insights, theories, and findings regarding surprise, with a view to supporting future integration and progress. This special issue presents developments in research on the cognitive mechanisms and consequences of surprise. Amidst much progress, surprise research has often been siloed, so, as editors, we have sought to juxtapose insights, theories, and findings, to support cross‐fertilization in future research. The present paper sets the stage by presenting a historical summary, highlighting contrasts in definitions, and tracing major threads running through this issue and the larger surprise literature. [ABSTRACT FROM AUTHOR]

The last two decades have produced unprecedented successes in the fields of artificial intelligence and machine learning (ML), due almost entirely to advances in deep neural networks (DNNs). Deep hierarchical memory networks are not a novel concept in cognitive science and can be traced back more than a half century to Simon's early work on discrimination nets for simulating human expertise. The major difference between DNNs and the deep memory nets meant for explaining human cognition is that the latter are symbolic networks meant to model the dynamics of human memory and learning. Cognition‐inspired symbolic deep networks (SDNs) address several known issues with DNNs, including (1) learning efficiency, where a much larger number of training examples are required for DNNs than would be expected for a human; (2) catastrophic interference, where what is learned by a DNN gets unlearned when a new problem is presented; and (3) explainability, where there is no way to explain what is learned by a DNN. This paper explores whether SDNs can achieve similar classification accuracy performance to DNNs across several popular ML datasets and discusses the strengths and weaknesses of each approach. Simulations reveal that (1) SDNs provide similar accuracy to DNNs in most cases, (2) SDNs are far more efficient than DNNs, (3) SDNs are as robust as DNNs to irrelevant/noisy attributes in the data, and (4) SDNs are far more robust to catastrophic interference than DNNs. We conclude that SDNs offer a promising path toward human‐level accuracy and efficiency in category learning. More generally, ML frameworks could stand to benefit from cognitively inspired approaches, borrowing more features and functionality from models meant to simulate and explain human learning. Deep Neural Networks (DNNs) are popular for classifying large noisy analogue data. However, DNNs suffer from several known issues, including explainability, efficiency, catastrophic interference, and a need for high‐end computational resources. Our simulations reveal that psychologically‐inspired symbolic deep networks (SDNs) achieve similar accuracy and robustness to noise as DNNs on common ML problem sets, while addressing these issues. [ABSTRACT FROM AUTHOR]

Debate about cognitive science explanations has been formulated in terms of identifying the proper level(s) of explanation. Views range from reductionist, favoring only neuroscience explanations, to mechanist, favoring the integration of multiple levels, to pluralist, favoring the preservation of even the most general, high‐level explanations, such as those provided by embodied or dynamical approaches. In this paper, we challenge this framing. We suggest that these are not different levels of explanation at all but, rather, different styles of explanation that capture different, cross‐cutting patterns in cognitive phenomena. Which pattern is explanatory depends on both the cognitive phenomenon under investigation and the research interests occasioning the explanation. This reframing changes how we should answer the basic questions of which cognitive science approaches explain and how these explanations relate to one another. On this view, we should expect different approaches to offer independent explanations in terms of their different focal patterns and the value of those explanations to partly derive from the broad patterns they feature. Patterns in cognitive phenomena and pluralism of explanatory styles This paper focuses on three general approaches to explanation in cognitive science, namely: reductionist, mechanist and pluralist approaches to explanation. On the basis of various case studies from cognitive psychology, Potochnik and Sanches De Oliveira argue that these three explanatory approaches are best understood as different styles of explanation. Each of these styles would be best suited to explain different, cross‐cutting patterns in cognitive phenomena. [ABSTRACT FROM AUTHOR]

What would it mean to explain the mind in neural terms? Neural accounts of the mind are often sought in a reductionistic spirit in which neural mechanisms explain cognition. Because an individual's thoughts and behaviors are not reproducible without careful control of task, stimulus, and behavioral history, laws of the mind are the currency of psychology. Reduction may thus have to take the form familiar from physics: deriving macroscopic laws from microscopic laws. I argue that the metaphor of reduction from non‐equilibrium physics may be the most appropriate. Macroscopic patterns of neural activity, which cause behavior and thought, are slow dynamical variables that dominate the fast microscopic dynamics of individual neurons and synapses. I outline a theoretical framework in which strongly recurrent neural networks, described by neural dynamics, generate neural representations as attractor states that are embedded in low‐dimensional feature spaces. Instabilities of these states are instrumental in decision‐making and the generation of sequences of mental states that are the basis for higher cognition. Networks of such neural population dynamics form neural cognitive architectures that capture the laws of the mind. The dynamics of neural populations capture the laws of the mind This paper focuses on the level of neural networks. Examining the case of recurrent neural networks, the paper argues that the dynamics of neural populations form a privileged level of explanation in cognitive science. According to Schöner, this level is privileged, because it enables cognitive scientists to discover the laws governing organisms' cognition and behaviour. [ABSTRACT FROM AUTHOR]

The article presents an introduction to Volume 9, Issue 1 of the journal that contains a collection of papers on cognitive science that were recruited, edited, and assembled by Berit Brogaard of University of Miami and director of Brogaard Lab for Multisensory Research.

Human languages all have a grammar, that is, rules that determine how symbols in a language can be combined to create complex meaningful expressions. Despite decades of research, the evolutionary, developmental, cognitive, and computational bases of grammatical abilities are still not fully understood. "Artificial Grammar Learning" (AGL) studies provide important insights into how rules and structured sequences are learned, the relevance of these processes to language in humans, and whether the cognitive systems involved are shared with other animals. AGL tasks can be used to study how human adults, infants, animals, or machines learn artificial grammars of various sorts, consisting of rules defined typically over syllables, sounds, or visual items. In this introduction, we distill some lessons from the nine other papers in this special issue, which review the advances made from this growing body of literature. We provide a critical synthesis, identify the questions that remain open, and recognize the challenges that lie ahead. A key observation across the disciplines is that the limits of human, animal, and machine capabilities have yet to be found. Thus, this interdisciplinary area of research firmly rooted in the cognitive sciences has unearthed exciting new questions and venues for research, along the way fostering impactful collaborations between traditionally disconnected disciplines that are breaking scientific ground. [ABSTRACT FROM AUTHOR]

The article introduces the latest issue of Topics in Cognitive Science, which is dedicated to excellent and award-winning research. The first topic focuses on the Fellows of the Cognitive Science Society, featuring articles by various researchers. Barbara Malt from Lehigh University has joined this exclusive group and provides an overview of her research on the relationship between language, thought, and representation. The second topic includes revised papers from the Annual Meeting of the Cognitive Science Society, and the final topic showcases the best papers from the International Conference on Cognitive Modeling. The article encourages letters, commentaries, and proposals for new topics. [Extracted from the article]

An introduction is presented in which the editor discusses various reports within the issue on topics including views of cognitive scientists on climate science, cognitive science and human cognition.

Cognitive science is a multidisciplinary field. Whereas debates on whether this is beneficial continue to spring up, this multidisciplinarity comes with at least one obvious challenge, namely, safeguarding an increasing integration across its subfields. The new and open‐ended topic preluded here attempts to address this challenge by pursuing a multilayered agenda: to introduce the Fellows of the Cognitive Science Society and earn them the recognition and profile they deserve; to furnish a platform for reflection on cognitive science from a bird's eye view; and to present role models for younger generations. To achieve these goals, this topic provides all Fellows with an opportunity to showcase their scientific work, to outline what they consider to be its major contributions to cognitive science, and to elaborate on their visions for greater integration of the field. [ABSTRACT FROM AUTHOR]

In reflexively autocatalytic foodset (RAF)‐generated networks, nodes are not only passive transmitters of activation, but they also actively galvanize, or "catalyze" the synthesis of novel ("foodset‐derived") nodes from existing ones (the "foodset"). Thus, RAFs are uniquely suited to modeling how new structure grows out of currently available structure, and analyzing phase transitions in potentially very large networks. RAFs have been used to model the origins of evolutionary processes, both biological (the origin of life) and cultural (the origin of cumulative innovation), and may potentially provide an overarching framework that integrates evolutionary and developmental approaches to cognition. Applied to cognition, the foodset consists of information obtained through social learning or individual learning of pre‐existing information, and foodset‐derived items arise through mental operations resulting in new information. Thus, mental representations are not only propagators of spreading activation, but they also trigger the derivation of new mental representations. To illustrate the application of RAF networks in cognitive science, we develop a step‐by‐step process model of conceptual change (i.e., the process by which a child becomes an active participant in cultural evolution), focusing on childrens' mental models of the shape of the Earth. Using results from (Vosniadou & Brewer, 1992), we model different trajectories from the flat Earth model to the spherical Earth model, as well as the impact of other factors, such as pretend play, on cognitive development. As RAFs increase in size and number, they begin to merge, bridging previously compartmentalized knowledge, and get subsumed by a giant RAF (the maxRAF) that constrains and enables the scaffolding of new conceptual structure. At this point, the cognitive network becomes self‐sustaining and self‐organizing. The child can reliably frame new knowledge and experiences in terms of previous ones, and engage in recursive representational redescription and abstract thought. We suggest that individual differences in the reactivity of mental representations, that is, their proclivity to trigger conceptual change, culminate in different cognitive networks and concomitant learning trajectories. This paper introduces a new kind of cognitive network in which nodes are not just passive transmitters of activation; they actively galvanize, or "catalyze" the synthesis of novel ("foodset‐derived") nodes from existing ones (the "foodset"). This makes the approach uniquely suited to modeling how new structure grows out of currently available structure, resulting in phase transitions in the network as a whole. To illustrate the approach, we develop a step‐by‐step process model of conceptual change in childrens' mental models of the shape of the earth, incorporating individual differences, and the impact of other factors, such as pretend play, on cognitive development. [ABSTRACT FROM AUTHOR]

There is a rich tradition of building computational models in cognitive science, but modeling, theoretical, and experimental research are not as tightly integrated as they could be. In this paper, we show that computational techniques-even simple ones that are straightforward to use-can greatly facilitate designing, implementing, and analyzing experiments, and generally help lift research to a new level. We focus on the domain of artificial grammar learning, and we give five concrete examples in this domain for (a) formalizing and clarifying theories, (b) generating stimuli, (c) visualization, (d) model selection, and (e) exploring the hypothesis space., (© 2019 The Authors. Topics in Cognitive Science published by Wiley Periodicals, Inc. on behalf of Cognitive Science Society.)

Sharing information and memories is a key feature of social interactions, making social contexts important for developing and transmitting accurate memories and also false memories. False memory transmission can have wide-ranging effects, including shaping personal memories of individuals as well as collective memories of a network of people. This paper reviews a collection of key findings and explanations in cognitive research on the transmission of false memories in small groups. It also reviews the emerging experimental work on larger networks and collective false memories. Given the reconstructive nature of memory, the abundance of misinformation in everyday life, and the variety of social structures in which people interact, an understanding of transmission of false memories has both scientific and societal implications., (© 2018 Cognitive Science Society, Inc.)

Computational modeling of sketch understanding is interesting both scientifically and for creating systems that interact with people more naturally. Scientifically, understanding sketches requires modeling aspects of visual processing, spatial representations, and conceptual knowledge in an integrated way. Software that can understand sketches is starting to be used in classrooms, and it could have a potentially revolutionary impact as the models and technologies become more advanced. This paper looks at one such effort, Sketch Worksheets, which have been used in multiple classroom experiments already, with students ranging from elementary school to college. Sketch Worksheets are a software equivalent of pencil and paper worksheets commonly found in classrooms, but they provide on-the-spot feedback based on what students draw. They are built on the CogSketch platform, which provides qualitative visual and spatial representations and analogical processing based on computational models of human cognition. This paper explores three issues. First, we examine how research from cognitive science and artificial intelligence, combined with the constraints of creating new kinds of educational software, led to the representations and processing in CogSketch. Second, we examine how these capabilities have been used in Sketch Worksheets, drawing upon experiments with fifth-grade students in biology and college students in engineering design and in geoscience. Finally, we examine some open issues in sketch understanding that need to be addressed to better model high-level aspects of vision, and for sketch understanding systems to reach their full potential for supporting education. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including formal learning theory, mathematical aspects and data sharing policy in cognitive science.

An introduction is presented in which the editor discusses various reports within the issue on topics including computational methods to extract meaning from a text, theories of human cognition and cognitive science.

An introduction to the journal is presented in which the editor discusses articles within the issue including "Thirty Years After Marr's Vision" and topics related to three levels of cognitive science and papers that won awards for excellence at the Cognitive Science Conference in 2014.

An introduction to the periodical is presented on topics such as research on sociolinguistic variation and expansion of cognitive science.

Unlike behaviorism, cognitive psychology relies on mental concepts to explain behavior. Yet mental processes are not directly observable and multiple explanations are possible, which poses a challenge for finding a useful framework. In this article, I distinguish three new frameworks for explanations that emerged after the cognitive revolution. The first is called tools‐to‐theories: Psychologists' new tools for data analysis, such as computers and statistics, are turned into theories of mind. The second proposes as‐if theories: Expected utility theory and Bayesian statistics are turned into theories of mind, describing an optimal solution of a problem but not its psychological process. The third studies the adaptive toolbox (formal models of heuristics) that describes mental processes in situations of uncertainty where an optimal solution is unknown. Depending on which framework researchers choose, they will model behavior in either situations of risk or of uncertainty, and construct models of cognitive processes or not. The frameworks also determine what questions are asked and what kind of data are generated. What all three frameworks have in common, however, is a clear preference for formal models rather than explanations by general dichotomies or mere verbal concepts. The frameworks have considerable potential to inform each other and to generate points of integration. How to Explain Behavior? This paper focuses on the cognitive/behavioural level. It describes three general frameworks for explanation in cognitive science, namely: a "tools‐to‐theories", an "as‐if", and an "adaptive toolbox of heuristics" framework. While discussing the virtues and limitations of these three frameworks, Gigerenzer argues that the adaptive toolbox of heuristics is the most promising for providing process‐oriented explanations of human rationality. [ABSTRACT FROM AUTHOR]

Cognitive science began as a multidisciplinary endeavor to understand how the mind works. Since the beginning, cognitive scientists have been asking questions about the right methodologies and levels of explanation to pursue this goal, and make cognitive science a coherent science of the mind. Key questions include: Is there a privileged level of explanation in cognitive science? How do different levels of explanation fit together, or relate to one another? How should explanations at one level inform or constrain explanations at some other level? Can the different approaches to the mind, brain, and culture be unified? The aim of this issue of topiCS is to provide a platform for discussing different answers to such questions and to facilitate a better understanding between the different strands of thinking about the right levels of explanation in cognitive science. Introduction to "Levels of Explanation in Cognitive Science: From Molecules to Culture" This paper introduces the topic "Levels of Explanation in Cognitive Science: From Molecules to Culture", puts into focus some key questions, and provides an overview of the contributions in this topic. [ABSTRACT FROM AUTHOR]

Since the emergence of our species at least, natural selection based on genetic variation has been replaced by culture as the major driving force in human evolution. It has made us what we are today, by ratcheting up cultural innovations, promoting new cognitive skills, rewiring brain networks, and even shifting gene distributions. Adopting an evolutionary perspective can therefore be highly informative for cognitive science in several ways: It encourages us to ask grand questions about the origins and ramifications of our cognitive abilities; it equips us with the means to investigate, explain, and understand key dimensions of cognition; and it allows us to recognize the continued and ubiquitous workings of culture and evolution in everyday instances of cognitive behavior. Taking advantage of this reorientation presupposes a shift in focus, though, from human cognition as a general, homogenous phenomenon to the appreciation of cultural diversity in cognition as an invaluable source of data. The Role of Culture and Evolution for Human Cognition This paper focuses on the level of cultural evolution. It examines a number of cognitive abilities, including the abilities to write, to represent numbers and to reason about causal relationships, to argue that cultural evolution has distinctive causal influences at several levels of explanation in cognitive science. Bender argues that cultural evolution causally influences gene selection in a population, neural connectivity and dynamics, cognitive functioning, and culture itself. [ABSTRACT FROM AUTHOR]

Back when researchers thought about the various forms that color vision could take, the focus was primarily on the retinal mechanisms. Since that time, research on human color vision has shifted from an interest in retinal mechanisms to cortical color processing. This has allowed color research to provide insight into questions that are not limited to early vision but extend to cognition. Direct cortical connections from higher-level areas to lower-level areas have been found throughout the brain. One of the classic questions in cognitive science is whether perception is influenced, and if so to what extent, by cognition and whether a clear distinction can be drawn between perception and cognition. Since perception is seen as providing justification for our beliefs about properties in the external world, these questions also have metaphysical and epistemological significance. The aim of this paper is to highlight some of the areas where research on color perception can shed new light on questions in the cognitive sciences. A further aim of the paper is to raise some questions about color research that are in dire need of further reflection and investigation. [ABSTRACT FROM AUTHOR]

One challenge to creating realistic cognitive models of memory is the inability to account for the vast common-sense knowledge of human participants. Large computational knowledge bases such as WordNet and DBpedia may offer a solution to this problem but may pose other challenges. This paper explores some of these difficulties through a semantic network spreading activation model of the Deese-Roediger-McDermott false memory task. In three experiments, we show that these knowledge bases only capture a subset of human associations, while irrelevant information introduces noise and makes efficient modeling difficult. We conclude that the contents of these knowledge bases must be augmented and, more important, that the algorithms must be refined and optimized, before large knowledge bases can be widely used for cognitive modeling. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The aim of this issue is to take stock of cognitive science of human variation in the field of spatial navigation, an important domain in which debates have often assumed an invariant human mind. Addressing the challenge of individual differences requires cognitive scientists to change their practices in several ways. First, we need to consider how to design measures and paradigms that have adequate psychometric characteristics. Second, using reliable, efficient, and valid measures, we need to examine how people vary from time to time, both in the short run due to emotions, such as stress or time pressure, and in the longer run, due to training or living in physical environments that require wayfinding skills. Third, we need to study people different from the traditional college participants, including variations in age, gender, education, culture, physical environment, and possible interactions among these variables. This issue assesses how human spatial navigation differs: within individuals across short‐term variations in mood or stress, and between individuals across variations in age, gender, education, culture, and physical environment. [ABSTRACT FROM AUTHOR]

Implicit learning and statistical learning are two contemporary approaches to the long-standing question in psychology and cognitive science of how organisms pick up on patterned regularities in their environment. Although both approaches focus on the learner's ability to use distributional properties to discover patterns in the input, the relevant research has largely been published in separate literatures and with surprisingly little cross-pollination between them. This has resulted in apparently opposing perspectives on the computations involved in learning, pitting chunk-based learning against probabilistic learning. In this paper, I trace the nearly century-long historical pedigree of the two approaches to learning and argue for their integration under the heading of "implicit statistical learning." Building on basic insights from the memory literature, I sketch a framework for statistically based chunking that aims to provide a unified basis for understanding implicit statistical learning., (Copyright © 2018 Cognitive Science Society, Inc.)