In this paper, we introduce the reader to the field of cognitive network science, that is, the application of network science methods to study human cognition and knowledge structures. Cognitive networks are representations of associative knowledge between concepts in a cognitive system apt at acquiring, storing, processing and producing language, that is, the mental lexicon. In a cognitive network, nodes represent concepts with links expressing relations, such as semantic, syntactic, phonological and visual connections, for example, "canine" and "dog" (nodes) linked by "being synonyms" (link). Hence, cognitive networks represent associative knowledge in mathematical, measurable and quantifiable ways. Can such structure be used to gain insights over cognitive phenomena? We explore this research question by reviewing recent, pioneering key applications and limitations of cognitive networks across visual, auditory, and semantic language processing tasks, either in healthy or clinical populations. We also review applications of cognitive networks modeling language acquisition, reconstructing text content and assessing creativity or personality traits in individuals. Our paper also gently introduces the reader to mathematical notations, definitions and measures about single-layer and multiplex networks as well as hypergraphs. Last but not least, across phonological, semantic and syntactic networks, we guide the reader through relevant psychological frameworks, datasets and software packages that might all aid current and future cognitive network scientists. This article is categorized under: Psychology > Memory Psychology > Theory and Methods Linguistics > Cognitive.

Inner speech decoding is the process of identifying silently generated speech from neural signals. In recent years, this candidate technology has gained momentum as a possible way to support communication in severely impaired populations. Specifically, this approach promises hope for people with a variety of physical or neurological disabilities who need alternative means of verbal expression. This review covers recording modalities that range from the noninvasive EEG to the high-density electrocorticography and discusses how linear discriminant analysis, deep convolutional networks, and hybrid fusion of EEG with fMRI are integrated into machine learning strategies to infer covert speech. This review synthesizes evidence to suggest that small vocabularies, under controlled conditions, can yield relatively reasonable accuracy while further refining the decoding outcome via context-based approaches. The impact of sensor quality, training data size, and domain adaptation is illustrated by focusing on public datasets of imagined or articulated speech. Throughout the article, the methodological standards emerging across laboratories will be discussed, emphasizing that effective inner speech recognition involves high-quality preprocessing, subject calibration, and informed modeling choices balanced against computational power for interpretability. In addition to technical advancements, this review also examines the ethical, societal, and regulatory challenges surrounding inner speech decoding, including brain data privacy, neural rights, informed consent, and user trust. Addressing these interdisciplinary issues is critical for the responsible development and real-world adoption of such technologies. This article is categorized under: Neuroscience > Computation Computer Science and Robotics > Machine Learning.

This primer summarizes the contemporary debate in moral psychology about whether disgust plays a role in moral judgment, and what that role might be. The importance of the debate is explained, then several approaches to studying the issue are reviewed. First, I review experimental studies that induce incidental disgust. Then, I examine other approaches to studying this question, including correlational studies of disgust sensitivity, studies of whether disgust responds to moral content, and research on whether moral transgressions can evoke disgust. I then cast this debate in the philosophical framework of thesis-antithesis-synthesis, and present several possible ways of synthesizing conflicting findings and resolving the debate.

ABSTRACTClimate change (CC) is a global phenomenon characterized by long‐term shifts in temperatures and weather patterns. Aside from natural causes, we have been facing a full‐blown climate crisis primarily driven by human activity, leading to increasingly frequent and extreme weather events that put a strain on people's mental capacities. Addressing CC necessitates a temporal perspective as both causes and potential solutions extend beyond the present. However, despite being a significant challenge for humanity, CC is often considered temporally distant, leading to abstract thinking and reduced urgency for action. Considering the diverse dimensions that concur to define CC, this review will explore the link between CC and time cognition, building on insights from cognitive sciences. Upon considering the tangible effects of the anthropogenic CC (Changing Place), we argue that change in the social construction of time is inherent to CC and drifts to the point of affecting psychological well‐being (Changing Time). Moreover, considering that time is central to cognition and interlinked with several cognitive functions, we will consider the literature investigating the impact of CC‐related eco‐anxiety on cognitive abilities within the framework of time cognition. Furthermore, we assess how eco‐anxiety and time cognition interact, potentially serving as markers of mental well‐being (Changing Thoughts). By framing CC within the realm of time cognition, we offer an interdisciplinary perspective on cognition and well‐being, advocating for the integration of cognitive science into climate adaptation and mitigation efforts to foster more effective, psychologically sustainable long‐term climate strategies (Changing Future).This article is categorized under:Neuroscience > Cognition

Spatial navigation is more difficult and effortful for older than younger individuals, a shift which occurs for a variety of neurological, physical, and cognitive reasons associated with aging. Despite a large body of evidence documenting age-related deficits in spatial navigation, comparatively less research addresses how to facilitate more effective navigation behavior for older adults. Since navigation challenges arise for a variety of reasons in old age, a one-size-fits-all solution is unlikely to work. Here, we introduce a framework for the variety of spatial navigation challenges faced in aging, which we call LOST-Location, Orientation, Spatial mapping, and Transit. The LOST framework builds on evidence from the cognitive neuroscience of spatial navigation, which reveals distinct components underpinning human wayfinding. We evaluate research on navigational aids-devices and depictions-which help people find their way around; and we reflect on how navigation aids solve (or fail to solve) specific wayfinding difficulties faced by older adults. In summary, we emphasize a bespoke approach to improving spatial navigation in aging, which focuses on tailoring navigation solutions to specific navigation challenges. Our hope is that by providing precise support to older navigators, navigation opportunities can facilitate independence and exploration, while minimizing the danger of becoming lost. We conclude by delineating critical knowledge gaps in how to improve older adults' spatial navigation capacities that the novel LOST framework could guide to address. This article is categorized under: Psychology > Development and Aging Neuroscience > Cognition Neuroscience > Behavior.

Imagination is a cognitive process used to generate new ideas from old, not just in the service of creativity and fantasy, but also in our ordinary thoughts about alternatives to current reality. In this article, I argue for the central function of imagination in the development of social cognition in infancy and childhood. In Section 1, I review a work showing that even in the first year of life, social cognition can be viewed through a nascent ability to imagine the physical possibilities and physical limits on action. In Section 2, I discuss how imagination of what should happen is appropriately constrained by what can happen, and how this influences children's moral evaluations. In the final section, I suggest developmental changes in imagination—especially the ability to imagine improbable events—may have implications for social inference, leading children to learn that inner motives can conflict. These examples point to a flexible and domain‐general process that operates on knowledge to make social meaning.This article is categorized under:Psychology > Development and AgingCognitive Biology > Cognitive DevelopmentPhilosophy > Knowledge and Belief

Strategic interactions, where an individual's payoff depends on the decisions of multiple intelligent agents, are ubiquitous among social animals. They span a variety of important social behaviors such as competition, cooperation, coordination, and communication, and often involve complex, intertwining cognitive operations ranging from basic reward processing to higher‐order mentalization. Here, we review the progress and challenges in probing the neural and cognitive mechanisms of strategic behavior of interacting individuals, drawing an analogy to recent developments in studies of reward‐seeking behavior, in particular, how research focuses in the field of strategic behavior have been expanded from adaptive behavior based on trial‐and‐error to flexible decisions based on limited prior experience. We highlight two important research questions in the field of strategic behavior: (i) How does the brain exploit past experience for learning to behave strategically? and (ii) How does the brain decide what to do in novel strategic situations in the absence of direct experience? For the former, we discuss the utility of learning models that have effectively connected various types of neural data with strategic learning behavior and helped elucidate the interplay among multiple learning processes. For the latter, we review the recent evidence and propose a neural generative mechanism by which the brain makes novel strategic choices through simulating others' goal‐directed actions according to rational or bounded‐rational principles obtained through indirect social knowledge.This article is categorized under:Economics > Interactive Decision‐MakingPsychology > Reasoning and Decision MakingNeuroscience > Cognition

Memories affect nearly every aspect of our mental life. They allow us to both resolve uncertainty in the present and to construct plans for the future. Recently, renewed interest in the role memory plays in adaptive behavior has led to new theoretical advances and empirical observations. We review key findings, with particular emphasis on how the retrieval of many kinds of memories affects deliberative action selection. These results are interpreted in a sequential inference framework, in which reinstatements from memory serve as "samples" of potential action outcomes. The resulting model suggests a central role for the dynamics of memory reactivation in determining the influence of different kinds of memory in decisions. We propose that representation-specific dynamics can implement a bottom-up "product of experts" rule that integrates multiple sets of action-outcome predictions weighted based on their uncertainty. We close by reviewing related findings and identifying areas for further research. This article is categorized under: Psychology > Reasoning and Decision Making Neuroscience > Cognition Neuroscience > Computation.