Review of risk perception research by cognitive neuroscience technologies in high-risk industry.

Neural correlates of implicit emotion processing and regulation in the SEAT paradigm: A meta-analysis of fMRI studies.

Brain lexicon representation of high-familiarity Japanese loanwords in Chinese-English-Japanese trilinguals: evidence from cognitive neuroscience

The predictive brain: Neural correlates of word expectancy align with large language model prediction probabilities.

Nonlinear shift along the sensorimotor-association-axis in brain responses to task performance.

Futrell and Mahowald argue that neural networks have learned non-trivial aspects of language. We argue that these systems have not in fact demonstrated "mastery" of syntax, marshalling recent evidence, and that they further obscure explanatory insights with respect to topics in the cognitive neuroscience of language.

The visual system is attuned to the statistical regularities of the visual world, enabling rapid, almost reflexive, and highly accurate recognition of object identities and categories. This article presents recent advances across psychophysics, computational modeling, and cognitive neuroscience, which together suggest that the visual system is just as attuned to the laws of physics governing our physical world. We review psychophysical work showing that vision incorporates intuitive physics as a rapid, spontaneous, and stimulus-driven process. We review the computational framework of physics-based analysis by synthesis, which suggests that the mind's and brain's intuitive physics is in a structure-preserving relationship with entities and their force relations in the physical world. We end with an outline of an integrative program of computational modeling work, along with neuroscientific and psychophysical studies, toward psychologically and neurally refined mechanistic accounts of the visual perception of intuitive physics.

Clinical psychological research stands to benefit greatly from collaborations that incorporate perspectives from other disciplines. However, the challenges of such collaborative efforts are immense. This commentary provides the perspectives of five frequent collaborators from distinct disciplines (clinical psychology, linguistics, affective and developmental science, vision science, and cognitive neuroscience). We outline three overarching challenges we have encountered during our collaborations: disciplinary bias (i.e., implicit and explicit beliefs about the best ways to gain knowledge), lack of multidisciplinary fluency (i.e., difficulty effectively communicating across disciplines), and inherent risk (i.e., the speculative nature and uncertain career prospects of multidisciplinary work). We then discuss potential solutions to these challenges. We hope that by sharing our experiences and insights, we can promote more productive and impactful clinical science.

Characterizing brain activities underlying text availability in an ecologically valid context and identifying whether aspects of these activities are influenced by one's state of cognitive absorption (CA) are important yet unexplored lines of research in cognitive neuroscience. This study investigated the oculometric behavior and electroencephalographic (EEG) activities during periods with vs. without surtitle presentation in 25 human subjects while they viewed a live theatrical stage performance, and further explored relationships with self-reported CA. Behaviorally, subjects anticipated the appearance of text, and oriented their gaze toward them when they were present regardless of their level of CA . Neurophysiologically, text presence/absence was differentiated primarily by EEG theta activities and beta connectivities in or between visuospatial processing areas. Importantly, text presence-associated increases in theta activity in the frontal eye field and beta connectivity between the precuneus and the primary visual cortex did not significantly vary with self-reported CA, suggesting that these processes may support the automatic allocation of attention to available text.

Cognitive neuroscience research often relies on convenience sampling of participants, which can result in biased sample demographics and an under-representation of older adults. There is a need to identify more effective routes to widen participation among older adults and to explore age-related differences in the motivators and barriers to research involvement. This mixed methods study combined qualitative data from two focus groups, conducted with N = 11 healthy older adults aged 55-73, and an online questionnaire completed by N = 336 adults aged 18-88. Analysis of the focus group discussions identified 3 main themes that were most important to older adults: a) The importance of receiving transparent information about the aims, procedures and safety of the study, b) Distinguishing between medical and non-medical research, and c) Contributing to the "collective good". The questionnaire echoed that altruism, and the prospect of scientific discovery, are increasingly important motivators with advancing age, whereas financial incentives become less important. Older adults have more free time to participate, are less deterred by the prospect of pain, and express more trust in researchers than younger people. Attitudes towards different imaging methods (MRI, EEG, NIBS and Eye tracking) varied, with fewest negative emotions for eye-tracking and most for non-invasive brain stimulation, but positive attitudes generally increased and negative attitudes reduced with age. These findings can inform age-tailored recruitment strategies to improve diversity in neuroimaging research. Improving communication, addressing practical barriers, and framing studies in a meaningful context may help increase participation among groups who are traditionally underrepresented in neuroimaging research.

Detecting concealed information is a critical challenge in forensic investigations, security screening, and cognitive neuroscience. Conventional approaches using the concealed information test primarily rely on binary classification, distinguishing between recognized (concealed) and unrecognized (neutral) stimuli. This limits interpretability and fails to reveal the nature of the concealed knowledge. In this pilot study, we present a novel multimodal framework that moves beyond binary detection by decoding the category of concealed information into object, person, or place based on neurophysiological signals recorded during a concealed information test. High density electroencephalography and physiological signals, including skin temperature, galvanic skin response, and plethysmogram, were simultaneously recorded from ten subjects as they viewed visual stimuli representing these three categories. Temporal and spectral features were extracted from both modalities, followed by machine learning based multimodal fusion for classification. The proposed framework achieved an overall accuracy of 94.2%, significantly outperforming unimodal EEG (73%) and physiological (54.2%) baselines. Further analysis showed that similar decoding performance can be achieved using as few as eight strategically selected EEG electrodes, supporting the feasibility of lightweight, wearable implementations. The most informative electrodes were located over prefrontal and frontocentral regions, aligning with cognitive processes related to attention, recognition, and deception. These findings demonstrate that neurophysiological signals enables not only the detection of concealed knowledge but also the identification of the type of hidden information. The integration of EEG and physiological signals enhances both sensitivity and interpretability by capturing complementary aspects of cognitive and affective processing during recognition. By enhancing the CIT paradigm from binary recognition toward semantic decoding, this pilot study advances the development of interpretable deception detection systems and bridges laboratory neuroscience with real world forensic applications.

The Default Mode Network was a key finding for cognitive neuroscience, but being the result of a data-driven analysis of resting-state fMRI data, its psychological and clinical implications have been difficult to elucidate. This is because in the resting-state paradigm we cannot directly correlate an observable specific task with specific brain connectivity patterns, and therefore inferences about the relationship between particular cognitive domains and the resting-state networks are limited. A similar problem arises when trying to link the network with personality traits: the DMN, as other intrinsic networks, is not a simple metric to compare with the results of a psychological test, but a complex composite of spatio-temporal features. Although over the last two decades several research works have provided insights about these relationships, we still lack a consensus on the methodology that best captures these interactions. In this context, we propose an alternative method to model the psycho-physiological relationships of the resting state components with behavioral data, based on the dimensionality reduction of an extensive psychological evaluation and the spatial dimension of the intrinsic connectivity components. Our results show that the connectivity networks are low to moderately related with behavioral and personality traits, or at least this relation is not in a direct way. This integration of neuroimaging and psychological assessment data creates valuable pathways for cognitive neuroscience, potentially revealing with precision how intrinsic brain network organization relates to individual variations in cognitive functioning and personality dimensions.

A central question in cognitive neuroscience is how the brain implements abstract computations that must generalize across superficially different inputs. Language provides a strong test case: the same grammatical operation, such as pluralization, can be realized through distinct rules and forms across languages. Whether such transformations rely on language-specific neural systems or on abstract mechanisms that generalize across linguistic contexts remains unresolved. Crucially, these transformations must be computed online and integrated into speech planning within a tightly constrained time window. Using magnetoencephalography (MEG), we tracked the millisecond dynamics of grammatical word-form transformations during semi-naturalistic phrase completion in humans of both sexes. Highly proficient Spanish-English bilinguals produced singular and plural noun forms in both languages in a design that fully orthogonalized semantic number, phonological changes, grammatical inflection and produced language. Adjusting words to fit their grammatical context engaged a left-lateralized fronto-temporal network beginning ∼100 ms after cue onset. Multivariate decoding revealed that the neural patterns supporting this computation generalized across languages, across different surface plural forms, and to pseudowords, demonstrating that abstractly equivalent operations are instantiated in the same neural substrates despite differences in linguistic form. Together, these findings provide time-resolved neural evidence for a language-general computational mechanism, showing that the brain implements grammatical transformations as abstract, generative operations. More broadly, they show how bilingualism can be used to probe general principles of neural organization, revealing how abstract computations may be shared and reused across representational systems.Significance Statement Human language relies on the ability to modify words to convey information like number and tense, but languages vary widely in how these transformations are implemented. This variation raises a fundamental question in cognitive neuroscience: do such transformations depend on language-specific neural systems, or are they processed by abstract neural mechanisms that generalize across languages? We demonstrate that Spanish-English bilinguals engage a shared left frontal-temporal network when producing grammatically appropriate forms in both languages. This common neural signature emerges early during speech planning and even generalizes to novel words. These findings indicate that the brain builds abstract, reusable neural mechanisms, consistent with models where language is organized by computational principles rather than by language-specific systems.

Anxiolytic and sedative drugs: Sleep modulation and memory implications.

Prediction in action: Toward an empirical science of active inference.

Background:This study presents a bibliometric and scientometric analysis of research trends in schizophrenia genetics over nearly 7 decades (1957–2025). The field has evolved from early heritability studies to genome-wide association studies and multi-omics approaches. The objective was to map the historical trajectory, thematic evolution, key contributors, and global collaboration patterns in relation to clinically relevant psychological constructs.Methods:An integrated bibliometric and scientometric approach was applied. A total of 5679 publications were included after systematic retrieval and PRISMA-guided screening from Scopus, PubMed, and Web of Science (initial n = 6193; final n = 5679). Data preprocessing included deduplication, keyword normalization, and metadata verification. VOSviewer (version 1.6.20) was used to construct co-authorship, keyword co-occurrence, and source co-citation networks. Thresholds were defined empirically to balance network interpretability and coverage.Results:The findings suggest a gradual increase in research activity beginning in the 1990s, followed by a marked acceleration after 2010. Four thematic clusters were identified: neurobiological mechanisms and endophenotypes; basic genetic foundations; pharmacological treatments; and clinical comorbidities. Temporal patterns indicate a shift between 2012 and 2016 from genetic association studies toward functional genomics and cognitive neuroscience. Prominent contributors include J. van Os, R.E. Gur, and M.T. Tsuang. The United States may play a prominent role within the global collaboration network. However, clinically relevant psychological constructs may be less centrally integrated within the network structure.Conclusion:This study provides a structured bibliometric mapping of schizophrenia genetics research from 1957 to 2025. The findings may suggest increasing movement toward large-scale, consortium-based research models and a possible partial separation between clinical and basic science domains. Importantly, the results highlight a relative underrepresentation of clinically interpretable psychological dimensions. These findings may indicate the need for stronger integration between genetic discoveries and clinical psychological frameworks to enhance translational relevance.

Sound hypersensitivity phenotypes and sound hypersensitivity disorder.

Computational models are central to cognitive neuroscience, but their rigorous application to experimental datasets is often constrained to a narrow set of canonical models that afford tractable analytical computations. We introduce theHSSM(Hierarchical Sequential Sampling Model) ecosystem, a Python toolbox that democratizes access to a broad, extensible array of neurocognitive process models through hierarchical Bayesian inference. Naturally leveraging simulation-based inference via likelihood surrogates,HSSMenables fast parameter estimation for models lacking closed-form likelihoods. Built atop PyMC and Bambi,HSSMprovides a user-friendly formula syntax for specifying hierarchical mixed-effects regressions on model parameters, incorporating trial-by-trial neural or physiological covariates. The ecosystem allows fast model simulation and training data generation, as well as the neural network training utilities to deploy surrogate likelihood networks via HuggingFace. Contributions are designed to benefit not only the single researcher working on a problem, but organically, the entire research community. Together, the tools in theHSSMecosystem bridge the interests of computational theorists as well as experimentalists, accelerating the cycle from model development to rigorous empirical testing.

Brain entangled quantum states in radical pairs: a possible link to consciousness.

A database of digital line drawıngs that depict expected and unexpected action-place relationships.