Uncovering the core dimensions of visual object representations
COREDIM aims to identify the core dimensions of visual object representations using neuroimaging, behavioral data, and AI, enhancing our understanding of visual processing in the brain.
Projectdetails
Introduction
Our ability to interact with our visual world is a remarkable feat. Despite drastic changes in their visual appearance, we can effortlessly make sense of thousands of objects and carry out meaningful actions on them.
Understanding Visual Representations
To understand the nature of our visual representations that underlie this ability, a central goal of cognitive neuroscience is to determine the properties – or dimensions – that make up our representational space of objects.
Current Limitations
While much progress has been made at identifying the building blocks of visual processing in brain and cognition, our scientific understanding of visual representations remains fundamentally limited by:
- Our ability to capture the complexity and variability of our visual world for determining the dimensions underlying our object representations.
- The difficulty in disentangling visual and semantic contributions to these representations.
COREDIM Program
COREDIM is an ambitious, interdisciplinary program that aims at overcoming these limitations and provide a detailed, interpretable characterization of the core dimensions underlying visual object representations.
Methodology
To reach this goal, COREDIM capitalizes on extensive, targeted sampling of behavioral and neuroimaging data and cutting-edge artificial intelligence methods that allow the identification of interpretable representational dimensions.
Project Goals
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Project 1: Aims at uncovering the core representational dimensions of objects across the ventral visual cortex, using a biologically-inspired neural network model tailored to each individual’s functional neuroanatomy and trained to identify the most informative stimuli.
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Project 2: Will identify the relative role of vision and semantic knowledge in shaping our core representational dimensions, through experimental manipulations at the level of the stimulus, task, and with cross-species comparisons.
Conclusion
Together, COREDIM promises to transform our understanding of visual processing, laying the foundation for a comprehensive characterization of visual cortex function.
Financiële details & Tijdlijn
Financiële details
Subsidiebedrag | € 1.500.000 |
Totale projectbegroting | € 1.500.000 |
Tijdlijn
Startdatum | 1-7-2022 |
Einddatum | 30-6-2027 |
Subsidiejaar | 2022 |
Partners & Locaties
Projectpartners
- JUSTUS-LIEBIG-UNIVERSITAET GIESSENpenvoerder
- MAX-PLANCK-GESELLSCHAFT ZUR FORDERUNG DER WISSENSCHAFTEN EV
Land(en)
Vergelijkbare projecten binnen European Research Council
Project | Regeling | Bedrag | Jaar | Actie |
---|---|---|---|---|
Flexible Dimensionality of Representational Spaces in Category LearningThis project investigates how the brain flexibly adjusts dimensionality in visual learning tasks using multimodal approaches across species to uncover neural mechanisms and enhance educational strategies. | ERC Consolid... | € 2.141.929 | 2025 | Details |
Untangling population representations of objects. A closed loop approach to link neural activity to mouse behavior.This project aims to develop a virtual navigation system for mice to study how visual representations in the brain influence behavior, enhancing our understanding of object recognition in natural environments. | ERC Starting... | € 1.900.000 | 2023 | Details |
Using deep neural networks to understand functional specialization in the human visual cortexThis project aims to uncover the origins of functional specialization in the brain's visual pathway by integrating computational modeling, naturalistic behavior sampling, and neuroimaging. | ERC Starting... | € 1.494.750 | 2024 | Details |
A theory and model of the neural transformations mediating human object perceptionTRANSFORM aims to develop a predictive model and theory of neural transformations for object perception by integrating brain imaging, mathematical analysis, and computational modeling. | ERC Consolid... | € 2.291.855 | 2025 | Details |
Personalized priors: How individual differences in internal models explain idiosyncrasies in natural visionThis project aims to uncover the contents of individual internal models of natural vision through creative drawing methods, enhancing understanding of scene perception and its neural underpinnings. | ERC Starting... | € 1.484.625 | 2023 | Details |
Flexible Dimensionality of Representational Spaces in Category Learning
This project investigates how the brain flexibly adjusts dimensionality in visual learning tasks using multimodal approaches across species to uncover neural mechanisms and enhance educational strategies.
Untangling population representations of objects. A closed loop approach to link neural activity to mouse behavior.
This project aims to develop a virtual navigation system for mice to study how visual representations in the brain influence behavior, enhancing our understanding of object recognition in natural environments.
Using deep neural networks to understand functional specialization in the human visual cortex
This project aims to uncover the origins of functional specialization in the brain's visual pathway by integrating computational modeling, naturalistic behavior sampling, and neuroimaging.
A theory and model of the neural transformations mediating human object perception
TRANSFORM aims to develop a predictive model and theory of neural transformations for object perception by integrating brain imaging, mathematical analysis, and computational modeling.
Personalized priors: How individual differences in internal models explain idiosyncrasies in natural vision
This project aims to uncover the contents of individual internal models of natural vision through creative drawing methods, enhancing understanding of scene perception and its neural underpinnings.