Hola,
¿eres nuevo aquí?

Regístrate gratis y conecta tu empresa con financiación pública, partners y proyectos.

Tengo cuenta

Regístrate

Ver video

RewSL

Financiado

Cerrado

Echoes of Experience: How statistical and reward learning guide our decisions

Our visual system constantly encounters a plethora of stimuli, drastically surpassing our processing capabilities for in-depth analysis. It is hence essential to select the element(s) that is most relevant for our objectives and i... ver más

31/12/2026

UGent

176K€

Presupuesto del proyecto: 176K€

Líder del proyecto

UNIVERSITEIT GENT No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2024-03-14

HORIZON-MSCA-2023-PF-01-01: MSCA Postdoctoral Fellowships 2023 ExpectedOutcome:Project results are expected to contribute to the following outcomes:

I+D

Cerrada hace 1 año

0% 100% 100%

Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

1 Participantes

UGent

175.92K€ | Lider

Conecta tu I+D

¿Tienes un proyecto y buscas un partner? Gracias a nuestro motor inteligente podemos recomendarte los mejores socios y ponerte en contacto con ellos. Te lo explicamos en este video

Proyectos interesantes

RELEARN Goal directed learning of the statistical structure of the e... 175K€ Cerrado

DnReLU Decision noise in reward-guided learning amidst option unava... Cerrado

DEVELOP‐LEARNING Developmental trajectories for model free and model based re... 231K€ Cerrado

DyNeRfusion Dynamic Network Reconstruction of Human Perceptual and Rewar... 2M€ Cerrado

SURPRISE How does surprise influence cognition and behavior? 2M€ Cerrado

PREF LEARNHEUR Learning heuristics in preference elicitation tasks insight... 166K€ Cerrado

Duración del proyecto: 33 meses Fecha Inicio: 2024-03-14
Fecha Fin: 2026-12-31

Líder del proyecto

UNIVERSITEIT GENT No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 176K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Our visual system constantly encounters a plethora of stimuli, drastically surpassing our processing capabilities for in-depth analysis. It is hence essential to select the element(s) that is most relevant for our objectives and ignore irrelevant ones. One helpful feature for this is that the world is characterized by numerous regularities making it partially predictable. Our brain can extract these regularities from past experience and use them to efficiently guide target selection. Recently, significant scientific interest has been directed to two forms of learning that focus on two types of regularities: in reward-mediated learning (RL), people learn which stimuli are associated with reward, prioritizing the elements related with high (vs. low) reinforcement. Statistical learning (SL), in contrast, allows people to extract statistical regularities from the environment, such as how often a stimulus occurs in a specific location, optimizing future actions toward the location where the relevant element appears frequently. In everyday life RL and SL coexist and they jointly guide our selection of relevant stimuli. However, they have been mainly addressed separately using divergent tasks, hindering a direct comparison of the results and assessing their combined influence. This project seeks to bridge this gap by systematically comparing RL and SL in a systematic and well-balanced experimental setup to understand how people implicitly learn from their past experience. Using a series of consistent experimental tasks, behavioral, ocular and electroencephalography measures will be recorded from healthy human volunteers to assess the individual and joint effect of RL and SL on the process dynamics from target selection until response execution. Furthermore, employing Markov decision process models, we will compare the observed performance with model performance. Jointly, our results will provide important insights into real-world learning mechanisms.

Conecta tu I+D

Entra hoy

¿Olvidé mi contraseña?

Financiación

Empresas

CTIs/Universidades

Proyectos

Investigadores