PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 7147730

Plasticity in human blindsight.

B Bridgeman·D Staggs
View original source ↗Export metadata ↓

Abstract

A subject with an unually large cortical scotoma, leaving a 9 degrees hemifield of vision in each eye, showed improvement with practice in pointing to an oscillating target positioned within the scotoma. The practice effect transferred to low-contrast targets and to stationary ones, though oscillating targets were always located more accurately. Following practice, the subject reported improved confidence in visual orientation. Results are interpreted in the context of the contrast between the functions of two visual systems (subcortical and cortical) present in normal humans.

Explore related subjects

Brain DiseasesHematoma, SubduralHumansLearningMaleMotion PerceptionOccipital LobeReaction TimeScotomaVisual Field TestsVisual FieldsVisual PerceptionJournal ArticleResearch Support, U.S. Gov't, Non-P.H.S.

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

B Bridgeman, D Staggs. 1982. Plasticity in human blindsight.. https://doi.org/10.1016/0042-6989(82)90085-2

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2007

A penalized likelihood approach to magnetic resonance image reconstruction.

Currently, images acquired via magnetic resonance imaging (MRI) and functional magnetic resonance imaging (fMRI) technology are reconstructed using the discrete inverse Fourier transform. While computationally convenient, this approach is not able to filter out noise. This is a serious limitation because the amount of noise in MRI and fMRI can be substantial. In this paper, we propose an alternative approach to reconstruction, based on penalized likelihood methodology. In particular, we focus on non-linear shrinkage estimators and show that this approach achieves a great reduction in integrated mean squared error (IMSE) of the estimated image with respect to the currently used estimator. This approach is extremely fast and easy to implement computationally. In addition, it can be combined with various alternative approaches to MR image reconstruction and can be easily adapted to other, non-MRI contexts, in which the observed data and the quantities of interest are related via a linear transform.

Brain Diseases↗