Sensitivity to statistical covariation of visual features is feature-specific
نویسندگان
چکیده
منابع مشابه
Statistical Learning of Visual Feature Hierarchiespdfsubject
We propose an unsupervised, probabilistic method for learning visual feature hierarchies. Starting from local, low-level features computed at interest point locations, the method combines these primitives into high-level abstractions. Our appearance-based learning method uses local statistical analysis between features and ExpectationMaximization (EM) to identify and code spatial correlations. ...
متن کاملFeature-specific effects of selective visual attention
Four experiments were conducted to quantify the effect of performing a foveal discrimination task on sensitivity for a peripheral grating. The observer's primary task was to discriminate either the spatial frequency or orientation of successive foveal Gabor patches. On a third of the trials they also performed a secondary task to detect the presence of a near-threshold grating in the periphery....
متن کاملCortical Sensitivity to Visual Features in Natural Scenes
A central hypothesis concerning sensory processing is that the neuronal circuits are specifically adapted to represent natural stimuli efficiently. Here we show a novel effect in cortical coding of natural images. Using spike-triggered average or spike-triggered covariance analyses, we first identified the visual features selectively represented by each cortical neuron from its responses to nat...
متن کاملStatistical learning of new visual feature combinations by infants.
The ability of humans to recognize a nearly unlimited number of unique visual objects must be based on a robust and efficient learning mechanism that extracts complex visual features from the environment. To determine whether statistically optimal representations of scenes are formed during early development, we used a habituation paradigm with 9-month-old infants and found that, by mere observ...
متن کاملEncoding multielement scenes: statistical learning of visual feature hierarchies.
The authors investigated how human adults encode and remember parts of multielement scenes composed of recursively embedded visual shape combinations. The authors found that shape combinations that are parts of larger configurations are less well remembered than shape combinations of the same kind that are not embedded. Combined with basic mechanisms of statistical learning, this embeddedness c...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Vision
سال: 2016
ISSN: 1534-7362
DOI: 10.1167/16.12.1174