Conceptual representations in the default, control and attention networks are task-dependent and cross-modal.

Brain Lang

Wilhelm Wundt Institute for Psychology, Leipzig University, Germany; Lise Meitner Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.

Published: September 2023


Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

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.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.bandl.2023.105313DOI Listing

Publication Analysis

Top Keywords

conceptual representations
16
cross-modal conceptual
12
conceptual feature
12
conceptual
11
attention networks
8
networks task-dependent
8
task-dependent cross-modal
8
conceptual processing
8
feature representations
8
representations modulated
8

Similar Publications

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 PDF

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 PDF

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 PDF

Understanding the shape of chemistry data-Applications with persistent homology.

J 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 PDF

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.

View Article and Find Full Text PDF