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Conceptual knowledge is central to human cognition. Neuroimaging studies suggest that conceptual processing involves modality-specific and multimodal brain regions in a task-dependent fashion. However, it remains unclear (1) to what extent conceptual feature representations are also modulated by the task, (2) whether conceptual representations in multimodal regions are indeed cross-modal, and (3) how the conceptual system relates to the large-scale functional brain networks. To address these issues, we conducted multivariate pattern analyses on fMRI data. 40 participants performed three tasks-lexical decision, sound judgment, and action judgment-on written words. We found that (1) conceptual feature representations are strongly modulated by the task, (2) conceptual representations in several multimodal regions are cross-modal, and (3) conceptual feature retrieval involves the default, frontoparietal control, and dorsal attention networks. Conceptual representations in these large-scale networks are task-dependent and cross-modal. Our findings support theories that assume conceptual processing to rely on a flexible, multi-level architecture.
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http://dx.doi.org/10.1016/j.bandl.2023.105313 | DOI Listing |
Patterns (N Y)
July 2025
Cedars-Sinai Medical Center, Los Angeles, CA, USA.
The tree-based pipeline optimization tool (TPOT) is one of the earliest automated machine learning (ML) frameworks developed for optimizing ML pipelines, with an emphasis on addressing the complexities of biomedical research. TPOT uses genetic programming to explore a diverse space of pipeline structures and hyperparameter configurations in search of optimal pipelines. Here, we provide a comparative overview of the conceptual similarities and implementation differences between the previous and latest versions of TPOT, focusing on two key aspects: (1) the representation of ML pipelines and (2) the underlying algorithm driving pipeline optimization.
View Article and Find Full Text PDFPsychol Rev
September 2025
Neural Computation Group, Max-Planck Institute for Human Cognitive and Brain Sciences.
It has been suggested that episodic memory relies on the well-studied machinery of spatial memory. This influential notion faces hurdles that become evident with dynamically changing spatial scenes and an immobile agent. Here I propose a model of episodic memory that can accommodate such episodes via temporal indexing.
View Article and Find Full Text PDFPsychol Sport Exerc
September 2025
Department of Psychology, The University of Texas at San Antonio, San Antonio, TX, USA.
Identity is among the most robust psychological constructs for predicting whether individuals translate physical activity (PA) intentions into action. However, existing identity measures in the PA domain focus narrowly on exercise and largely adopt limited unidimensional conceptualizations. This study aimed to develop and validate the Multidimensional Inventory of Physical Activity Identity (MIPAI-25), a novel instrument grounded in a multidimensional, theoretically integrated framework.
View Article and Find Full Text PDFJ Chem Phys
September 2025
Department of Chemistry, University of Utah, Salt Lake City, Utah 84112, USA.
Chemical data often have complex and nonlinear patterns in how data points relate to one another. Concurrently, there are many situations where chemical data are of high dimensionality (e.g.
View Article and Find Full Text PDFPLoS One
September 2025
Institut für Theoretische Physik, Universität Innsbruck, Innsbruck, Austria.
In the last decade, the free energy principle (FEP) and active inference (AIF) have achieved many successes connecting conceptual models of learning and cognition to mathematical models of perception and action. This effort is driven by a multidisciplinary interest in understanding aspects of self-organizing complex adaptive systems, including elements of agency. Various reinforcement learning (RL) models performing active inference have been proposed and trained on standard RL tasks using deep neural networks.
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