Confidence through consensus: A neural mechanism for uncertainty monitoring
Models that integrate sensory evidence to a threshold can explain task accuracy, response times and confidence, yet it is still unclear how confidence is encoded in the brain. Classic models assume that confidence is encoded in some form of balance between the evidence integrated in favor and agains...
| Autores: | , , , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2016 |
| País: | Argentina |
| Recursos: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/74907 |
| Acesso em linha: | http://hdl.handle.net/11336/74907 |
| Access Level: | acceso abierto |
| Palavra-chave: | Computational neuroscience Consciousness Decision Neural circuits https://purl.org/becyt/ford/1.6 https://purl.org/becyt/ford/1 |
| Resumo: | Models that integrate sensory evidence to a threshold can explain task accuracy, response times and confidence, yet it is still unclear how confidence is encoded in the brain. Classic models assume that confidence is encoded in some form of balance between the evidence integrated in favor and against the selected option. However, recent experiments that measure the sensory evidence's influence on choice and confidence contradict these classic models. We propose that the decision is taken by many loosely coupled modules each of which represent a stochastic sample of the sensory evidence integral. Confidence is then encoded in the dispersion between modules. We show that our proposal can account for the well established relations between confidence, and stimuli discriminability and reaction times, as well as the fluctuations influence on choice and confidence. |
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