SubsidieMeestersCognitive and Neural Computations of Semantics
CONNECTS aims to resolve the paradox of semantic congruity's effects on cognition by developing a unified framework that integrates behavioral, neural, and computational methods.
Projectdetails
Introduction
In our everyday lives, we rely on existing relations among elements in our environment (i.e., semantic information) to interact efficiently with the world. This information can either be used to facilitate understanding by exploiting redundant (congruent) evidence or to signal out salient stimuli by highlighting unexpected (incongruent) elements.
Cognitive Puzzle
This duality raises fundamental questions about how and when our brains utilize stored semantic knowledge, as its influence seems to vary depending on the specific cognitive domain. This seemingly paradoxical state represents a cognitive puzzle that questions whether the presence of (in)congruent contextual information in a given situation has a positive or negative impact on how we perceive, process, and remember information.
Project Overview
CONNECTS seeks to address the paradoxical effects of semantic congruity across various cognitive domains by providing a unified framework. The proposed framework builds on the Transfer Appropriate Processing principle and brings it to a neural representational level.
Neural Representations
By examining the transformations of neural representations, it is possible to quantify the degree of overlap in cognitive computations as a measure of appropriate transfer. Thus, CONNECTS dissolves the paradox by proposing that performance would be optimal when the required cognitive computation is oriented towards the same stimulus properties emphasized by the semantic (in)congruity of the stimulus.
Integrative Aim
This proposal not only reconciles conflicting evidence on specific domains but also provides a domain-agnostic framing of the conundrum that ensures its integrative aim.
Methodology
CONNECTS combines a solid theoretical foundation with cutting-edge neuroscientific techniques. The project's multi-method approach includes:
- Behavioural data
- Neural data (fMRI and EEG)
- Computational data from artificial neural networks
This approach offers a comprehensive exploration of the phenomenon, which is a core requirement for a unifying framework.
Financiële details & Tijdlijn
Financiële details
| Subsidiebedrag | € 1.496.563 |
| Totale projectbegroting | € 1.496.563 |
Tijdlijn
| Startdatum | 1-1-2025 |
| Einddatum | 31-12-2029 |
| Subsidiejaar | 2025 |
Partners & Locaties
Projectpartners
- UNIVERSIDAD DE GRANADApenvoerder
Land(en)
Vergelijkbare projecten binnen European Research Council
| Project | Regeling | Bedrag | Jaar | Actie |
|---|---|---|---|---|
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The Flexible Brain: (Re-)shaping Adaptation in Semantic CognitionThis project investigates how domain-general networks can compensate for semantic network disruptions in the brain, using neurostimulation to enhance understanding of cognitive flexibility and rehabilitation. | ERC Consolid... | € 1.999.750 | 2023 | Details |
It's about time: Towards a dynamic account of natural vision.TIME aims to revolutionize vision research by integrating semantic understanding and active information sampling through advanced brain imaging and bio-inspired deep learning, enhancing insights into visual cognition. | ERC Starting... | € 1.499.455 | 2022 | Details |
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Making sense of the senses: Causal Inference in a complex dynamic multisensory world
This project aims to uncover how the brain approximates causal inference in complex multisensory environments using interdisciplinary methods, potentially informing AI and addressing perceptual challenges in clinical populations.
The Flexible Brain: (Re-)shaping Adaptation in Semantic Cognition
This project investigates how domain-general networks can compensate for semantic network disruptions in the brain, using neurostimulation to enhance understanding of cognitive flexibility and rehabilitation.
It's about time: Towards a dynamic account of natural vision.
TIME aims to revolutionize vision research by integrating semantic understanding and active information sampling through advanced brain imaging and bio-inspired deep learning, enhancing insights into visual cognition.
Causal Roles of Intrinsic Coupling Modes: an Integrated Multiscale Framework for Cognitive Network Dynamics
This project aims to establish causal evidence for intrinsic coupling modes in brain networks by manipulating and analyzing their effects on cognition and behavior using advanced neurophysiological techniques.
Connectome cost conservation model of skill learning
This project aims to model brain connectomes before and after skill learning to predict neuroplasticity and behavioral outcomes, bridging neuropsychology and neurobiology.