Wiley Interdisciplinary Reviews-Cognitive Science最新文献

Insomnia has become the most prevalent sleep disorder in the world. The hyperarousal model is one of the main theories to explain the pathogenesis of insomnia disorder, and neuroimaging studies have provided important evidence to support this model. Although the findings vary, the overall results indicate that insomnia patients experience a condition of hyperarousal. We reviewed and summarized the related evidence from previous functional and structural neuroimaging studies of hyperarousal, which mainly showed enhanced local activity and interregional functional connectivity, increased metabolism, structural changes in gray matter, and altered white matter connectivity. Future research should further focus on cortical hyperarousal in different insomnia subtypes, unify the treatment criteria based on a sleep-staging interpretation, strictly control for the effects of age and gender, and advance the application of neuroimaging in the diagnosis and prognostic evaluation of insomnia. This article is categorized under: Psychology > Brain Function and Dysfunction Neuroscience > Clinical Neuroscience > Computation.

The public perception of clinical psychology has been heavily influenced by neuroscientific methods over the past several decades. However, we have seldom stopped to consider to what extent neuroscience can contribute to our understanding of how human psychology-including our experience of our psychological self-operates. This article reviews the progress and weaknesses of an extant psychopharmacological approach to psychological disorders. A psychological model is developed, which positions current neuroscientific research as describing symptoms, rather than causes, of mental disorders. This model relates closely to network theories of psychological disorders, with a strong emphasis on the Pattern Theory of the Self, where disruptions to the psychological self are a central etiological factor in mental disorders. In doing so, this article argues that the philosophical underpinnings of clinical psychological and neuroscientific research should be reconsidered if we intend to develop effective interventions for mental disorders. This article is contextualized in the author's experience of psychological and neuroscientific training, as well as subsequent research experience as a neuroscientist. This article is categorized under: Psychology > Theory and Methods Philosophy > Foundations of Cognitive Science Neuroscience > Clinical.

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.

Chanting is an ancient and globally widespread ritualistic practice involving rhythmic vocalization or repetition of words, phrases, or sounds. While previous reviews have considered the neurophysiological impact of meditation and spirituality, chanting has received limited systematic investigation. This review aimed to identify and synthesize neural correlates of chanting, examine methodological variability, and determine consistent neural patterns across chanting studies and styles. PsycINFO and PubMed databases were systematically searched for neuroimaging studies including chanting, mantra, and repetitive prayer. Articles published through October 8, 2024, were included, yielding 899 initial articles. After applying exclusion criteria, 24 studies were included. Study quality was assessed using the adapted Effective Public Health Practice Project (EPHPP) criteria. Findings demonstrate that chanting activates brain regions involved in attention and emotional regulation, including the prefrontal cortex, insula, and cingulate gyrus. Deactivation of default mode network (DMN) areas, particularly the posterior cingulate cortex and hippocampus, was also observed, suggesting reduced self-referential thought. Electroencephalography (EEG) studies revealed increased theta activity, indicating enhanced relaxation during chanting. Although heterogeneity in sample sizes, imaging modalities, participant characteristics, and control conditions preclude a formal meta-analysis, the findings lay a foundation for advancing research into the neural mechanisms of chanting. Chanting engages neural networks associated with attention and emotional regulation. The consistent pattern of prefrontal activation and DMN deactivation suggests mechanisms similar to other contemplative practices.

Climate 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.

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.

While predictive coding offers a powerful framework for investigating schizophrenia, its therapeutic applications remain nascent. To facilitate a "therapy turn" in the field, this review establishes a model-oriented, operationalist, and comprehensive understanding of schizophrenia. We examine predictive coding models across key domains-embodiment, co-occurrence of over- and under-weighting priors, subjective time processing, language production and comprehension, self-other differentiation, and social interaction. Each model is linked to corresponding clinical impairments and manifestations in schizophrenia. Finally, we propose a roadmap for future research, outlining the rationale and methods for leveraging this framework to develop novel interventions. This article is categorized under: Psychology > Prediction Psychology > Brain Function and Dysfunction.

There are few cognitive functions more essential than decision making, as better decisions improve our chances of survival. Cost-benefit decisions as they apply to most scenarios in the developed world can range from relatively mundane to reasonably important; however, particularly risky choices such as speeding on our way to work or consuming suspicious foods can pose a genuine risk of significant harm or illness. How is it that our brains learn and evaluate these risks and rewards to arrive at decisions? Additionally, what drives some of us to continue despite, or avoid because of, potential adverse consequences? This review explores neural mechanisms underlying cost-benefit decision making, focusing on paradigms used in human and particularly rodent studies to model decision making under the risk of explicit punishments, such as pain, discomfort, or loss. The review focuses on several key brain regions (the prefrontal cortex, basolateral amygdala, and striatum), and their roles in the assessment of rewards, punishments (or risk thereof), and motivated behaviors. It also discusses pertinent literature on the role of dopamine arising from the ventral tegmental area, as a neuromodulator critical for learning and reinforcement in the context of risky decision making. This article is categorized under: Neuroscience > Behavior Economics > Individual Decision-Making Psychology > Reasoning and Decision Making.

From birth, our visual system is sensitive to movement. Motion, as defined by any change in spatial position over time, is part of our daily lives and can refer to various visual information from elements of nature (like a tree swaying in the wind), objects (like a moving car), animals (like a running dog) or people (like two people dancing). Atypical motion processing, in particular for social and biological movement cues, could lead to difficulties in social interaction and communication, like those observed in Autism Spectrum Disorder (ASD). Extensive research has focused on coherent and biological motion processing in ASD, showing difficulties for both motion categories. Motion-related differences also emerge in several social contexts like emotion processing, joint attention, language acquisition, and body relationship with the environment. However, it remains unclear whether high-level difficulties stem from low-level processing issues or are specific to interpreting social cues. It appears that critical steps between low-level local cues processing and high-level biological/social contexts have not been studied. Adopting an approach encompassing a motion gradient from low to high levels could help identify when motion-related difficulties arise in ASD and which specific types or attributes of motion are most affected. This would offer a more comprehensive and integrated perspective on motion processing in ASD. This article is categorized under: Neuroscience > Cognition.

As longevity increases, cognitive decline in older adults has become a growing concern. Consequently, an increasing interest in the potential of digital tools (e.g., serious games (SG) and virtual reality (VR)) for early screening of Mild Cognitive Impairment (MCI) is emerging. Traditional cognitive assessments like the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) are widely used but have limitations related to cultural bias and manual scoring, while their digital adaptations, such as MOCA-CC, maintain diagnostic accuracy while offering remote administration and automated scoring. Innovative tools, such as the Virtual Super Market (VSM) test and Panoramix Suite, instead, assess cognitive domains like memory, attention, and executive function while promoting engagement and preserving ecological validity, making assessments more reflective of real-world tasks. Several studies show that these tools exhibit strong diagnostic performance, with sensitivity and specificity often exceeding 80%. However, although digital tools offer advantages in accessibility and user engagement, challenges remain concerning technological literacy, data privacy, and long-term validation. Future research should focus on validating these tools across diverse populations and exploring hybrid models that combine traditional and digital assessments, as digital tools show promise in transforming cognitive screening and enabling earlier interventions for cognitive decline. This article is categorized under: Psychology > Development and Aging Neuroscience > Cognition.