Early Prediction of Children’s Disengagement in a Tablet Tutor Using Visual Features

2021 ◽  
pp. 98-103
Author(s):  
Bikram Boote ◽  
Mansi Agarwal ◽  
Jack Mostow
Keyword(s):  
Visual Features ◽  
Early Prediction

Reduced change blindness for task-relevant visual features during a social interaction

2001 ◽  
Author(s):  
Donald A. Varakin ◽  
Sheena Rogers ◽  
Jeffrey T. Andre ◽  
Susan L. Davis
Keyword(s):  
Social Interaction ◽  
Change Blindness ◽  
Visual Features

Early prediction of changes in weight during six weeks of treatment with antidepressants

2004 ◽  
Vol 36 (05) ◽  
Author(s):  
H Himmerich ◽  
A Schuld ◽  
M Haack ◽  
C Kaufmann ◽  
T Pollmächer
Keyword(s):  
Early Prediction

Using Convolutional Neural Networks to measure the contribution of visual features to the representation of object animacy in the brain

2019 ◽  
Author(s):  
Sushrut Thorat
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Temporal Cortex ◽  
Large Degree ◽  
Visual Features ◽  
Visual Feature ◽  
Visual Stream ◽  
Feature Information ◽  
Ventral Visual Stream ◽  
Animal Images

A mediolateral gradation in neural responses for images spanning animals to artificial objects is observed in the ventral temporal cortex (VTC). Which information streams drive this organisation is an ongoing debate. Recently, in Proklova et al. (2016), the visual shape and category (“animacy”) dimensions in a set of stimuli were dissociated using a behavioural measure of visual feature information. fMRI responses revealed a neural cluster (extra-visual animacy cluster - xVAC) which encoded category information unexplained by visual feature information, suggesting extra-visual contributions to the organisation in the ventral visual stream. We reassess these findings using Convolutional Neural Networks (CNNs) as models for the ventral visual stream. The visual features developed in the CNN layers can categorise the shape-matched stimuli from Proklova et al. (2016) in contrast to the behavioural measures used in the study. The category organisations in xVAC and VTC are explained to a large degree by the CNN visual feature differences, casting doubt over the suggestion that visual feature differences cannot account for the animacy organisation. To inform the debate further, we designed a set of stimuli with animal images to dissociate the animacy organisation driven by the CNN visual features from the degree of familiarity and agency (thoughtfulness and feelings). Preliminary results from a new fMRI experiment designed to understand the contribution of these non-visual features are presented.

Working Memory for Low-Level Visual Features: Mechanisms for Detecting the Mismatch between the Current and Stored in Memory Orientation

2018 ◽  
Vol 482 (2) ◽  
pp. 224-227
Author(s):  
E. Mikhaylova ◽  
◽  
N. Gerasimenko ◽  
P. Prokudin ◽  
◽  
...  
Keyword(s):  
Working Memory ◽  
Visual Features ◽  
Low Level
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