Robust averaging protects decisions from noise in neural computations.
PLoS Comput Biol
; 13(8): e1005723, 2017 Aug.
Article
em En
| MEDLINE
| ID: mdl-28841644
ABSTRACT
An ideal observer will give equivalent weight to sources of information that are equally reliable. However, when averaging visual information, human observers tend to downweight or discount features that are relatively outlying or deviant ('robust averaging'). Why humans adopt an integration policy that discards important decision information remains unknown. Here, observers were asked to judge the average tilt in a circular array of high-contrast gratings, relative to an orientation boundary defined by a central reference grating. Observers showed robust averaging of orientation, but the extent to which they did so was a positive predictor of their overall performance. Using computational simulations, we show that although robust averaging is suboptimal for a perfect integrator, it paradoxically enhances performance in the presence of "late" noise, i.e. which corrupts decisions during integration. In other words, robust decision strategies increase the brain's resilience to noise arising in neural computations during decision-making.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Simulação por Computador
/
Encéfalo
/
Biologia Computacional
/
Tomada de Decisões
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Modelos Neurológicos
Tipo de estudo:
Prognostic_studies
Limite:
Adult
/
Female
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Humans
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Male
Idioma:
En
Revista:
PLoS Comput Biol
Assunto da revista:
BIOLOGIA
/
INFORMATICA MEDICA
Ano de publicação:
2017
Tipo de documento:
Article
País de afiliação:
Reino Unido