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OBJECTPERMOD

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Explaining object permanence with a deep recurrent neural network model of human...

Explaining object permanence with a deep recurrent neural network model of human cortical visual cognition Visual cognition is our ability to recognize the things we see around us and make inferences about their meaning and relationships. Deep convolutional neuronal network (CNN) models now achieve human-level performance on certain vi... ver más

17/10/2023

UNIVERSITY OF GLAS...

272K€

Presupuesto del proyecto: 272K€

Líder del proyecto

UNIVERSITY OF GLASGOW No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

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Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2023-10-17

MSCA-IF-2018: Individual Fellowships

I+D

Cerrada hace 6 años

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3 Participantes

UNIVERSITY OF GLASGOW

271.73K€ | Lider

TRUSTEES OF COLUMBIA UNIVERS...

N.D. | Participante

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Duración del proyecto: 54 meses Fecha Inicio: 2019-04-12
Fecha Fin: 2023-10-17

Líder del proyecto

UNIVERSITY OF GLASGOW No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 272K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Visual cognition is our ability to recognize the things we see around us and make inferences about their meaning and relationships. Deep convolutional neuronal network (CNN) models now achieve human-level performance on certain visual recognition tasks and currently provide the most powerful models of human visual cognition. A hallmark step in the development of human visual cognition is the acquisition of object permanence (OP). Object permanence is the ability to continue to mentally represent an object that has disappeared from view – for example because it is hidden behind another object. Current deep neural network models of vision lack this fundamental ability, limiting their power as models of human visual cognition and as artificially intelligent systems. In this action, I will study the computational mechanisms necessary for OP using a highly innovative approach that combines four elements: (1) a novel behavioral task that requires OP, (2) development of a deep recurrent neural network models, (3) testing of both human participants and models at the task, and (4) measurement of brain activity with functional magnetic resonance imaging (fMRI) during task performance. The OP task involves viewing a scene of moving objects that occasionally become occluded behind other objects. Models will be trained to represent objects continually, even as they vanish behind an occluder, and selected to match behavioral and cortical-layer- resolved high-field fMRI data of human observers. The hosts, Prof Kriegeskorte at Columbia University and Prof Muckli at University of Glasgow are world-leading experts on deep neural network models of vision and cortical-layer-resolved highfield fMRI, respectively. The outcome of this action, a biologically plausible deep recurrent convolutional model that can explain behavior and brain activity, will significantly enhance our understanding of the computational principles of visual cognition, with implications also for AI technology.

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