scholarly journals Summary statistical representation of temporal frequency

2017 ◽  
Vol 17 (10) ◽  
pp. 1375
Author(s):  
Shoko Kanaya ◽  
Masamichi Hayashi ◽  
David Whitney
Keyword(s):  
Temporal Frequency ◽  
Statistical Representation ◽  
Summary Statistical Representation

scholarly journals Individual representations in visual working memory inherit ensemble properties

2019 ◽  
Author(s):  
Igor Utochkin ◽  
Timothy F. Brady
Keyword(s):  
Working Memory ◽  
Visual Working Memory ◽  
Uncertain Information ◽  
Individual Item ◽  
Statistical Representation ◽  
The Mean ◽  
Summary Statistical Representation ◽  
Source Of Information

Prevailing theories of visual working memory assume that each encoded item is stored or forgotten as a separate unit independent from other items. Here, we show that items are not independent and that the recalled orientation of an individual item is strongly influenced by the summary statistical representation of all items (ensemble representation). We find that not only is memory for an individual orientation substantially biased toward the mean orientation, but the precision of memory for an individual item also closely tracks the precision with which people store the mean orientation (which is, in turn, correlated with the physical range of orientations). Thus, individual items are reported more precisely when items on a trial are more similar. Moreover, the narrower the range of orientations present on a trial, the more participants appear to rely on the mean orientation as representative of all individuals. This can be observed not only when the range is carefully controlled, but also shown even in randomly generated, unstructured displays, and after accounting for the possibility of location-based ‘swap’ errors. Our results suggest that the information about a set of items is represented hierarchically, and that ensemble information can be an important source of information to constrain uncertain information about individuals.

Spatial and Temporal Frequency Tuning of Motion-in-Depth Aftereffect

2008 ◽  
Vol 128 (7) ◽  
pp. 1015-1022
Author(s):  
Sheng Ge ◽  
Makoto Ichikawa ◽  
Atsushi Osa ◽  
Keiji Iramina ◽  
Hidetoshi Miike
Keyword(s):  
Frequency Tuning ◽  
Temporal Frequency ◽  
Motion In Depth

scholarly journals Growing Stock Volume Retrieval from Single and Multi-Frequency Radar Backscatter

Forests ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 944
Author(s):  
Mihai A. Tanase ◽  
Ignacio Borlaf-Mena ◽  
Maurizio Santoro ◽  
Cristina Aponte ◽  
Gheorghe Marin ◽  
...  
Keyword(s):  
Learning Algorithm ◽  
Temporal Frequency ◽  
National Forest Inventory ◽  
Easy Access ◽  
Estimation Errors ◽  
Growing Stock ◽  
L Band ◽  
Stock Volume ◽  
Global Datasets ◽  
Relative Root

While products generated at global levels provide easy access to information on forest growing stock volume (GSV), their use at regional to national levels is limited by temporal frequency, spatial resolution, or unknown local errors that may be overcome through locally calibrated products. This study assessed the need, and utility, of developing locally calibrated GSV products for the Romanian forests. To this end, we used national forest inventory (NFI) permanent sampling plots with largely concurrent SAR datasets acquired at C- and L-bands to train and validate a machine learning algorithm. Different configurations of independent variables were evaluated to assess potential synergies between C- and L-band. The results show that GSV estimation errors at C- and L-band were rather similar, relative root mean squared errors (RelRMSE) around 55% for forests averaging over 450 m3 ha−1, while synergies between the two wavelengths were limited. Locally calibrated models improved GSV estimation by 14% when compared to values obtained from global datasets. However, even the locally calibrated models showed particularly large errors over low GSV intervals. Aggregating the results over larger areas considerably reduced (down to 25%) the relative estimation errors.

Sign in / Sign up

Export Citation Format

Share Document