scholarly journals Perception of Biological Motion and Emotion in Mild Cognitive Impairment and Dementia

2012 ◽  
Vol 18 (5) ◽  
pp. 866-873 ◽  
Author(s):  
Julie D. Henry ◽  
Claire Thompson ◽  
Peter G. Rendell ◽  
Louise H. Phillips ◽  
Jessica Carbert ◽  
...  

AbstractParticipants diagnosed with mild cognitive impairment (MCI), dementia and controls completed measures that required decoding emotions from point-light displays of bodily motion, and static images of facial affect. Both of these measures tap social cognitive processes that are considered critical for social competency. Consistent with prior literature, both clinical groups were impaired on the static measure of facial affect recognition. The dementia (but not the MCI) group additionally showed difficulties interpreting biological motion cues. However, this did not reflect a specific deficit in decoding emotions, but instead a more generalized difficulty in processing visual motion (both to action and to emotion). These results align with earlier studies showing that visual motion processing is disrupted in dementia, but additionally show for the first time that this extends to the recognition of socially relevant biological motion. The absence of any MCI related impairment on the point-light biological emotion measure (coupled with deficits on the measure of facial affect recognition) also point to a potential disconnect between the processes implicated in the perception of emotion cues from static versus dynamic stimuli. For clinical (but not control) participants, performance on all recognition measures was inversely correlated with level of semantic memory impairment. (JINS, 2012, 18, 1–8)

2016 ◽  
Author(s):  
Thomas Guthier

The capability to recognize biological motion, i.e. gestures, human actions or face movements is crucial for social interactions, for predators, prey or artifcial systems interacting in a dynamic environment. In this thesis an artifcial feed-forward neural network for biological motion recognition is proposed. Like its natural counterpart, it consists of multiple layers organized in two streams, one for processing static and one for processing dynamic form information. The key component of the proposed system is a novel unsupervised learning algorithn, called VNMF, that is based on sparsity, non-negativity, inhibition and direction selectivity. In the frst layer of the dorsal stream, the VNMF is modifed to solve the optical flow estimation problem. In the subsequent layer the VNMF algorithm extracts prototypical patterns, such as optical flow patterns shaped e.g. as moving heads or lim parts. For the ventral stream the VNMF algorithm learns distict gradient structures, resembling edg...


2021 ◽  
Vol 25 ◽  
pp. 100196
Author(s):  
Varsha D. Badal ◽  
Colin A. Depp ◽  
Peter F. Hitchcock ◽  
David L. Penn ◽  
Philip D. Harvey ◽  
...  

2015 ◽  
Vol 29 (2) ◽  
pp. 197-204 ◽  
Author(s):  
Eric Fakra ◽  
Elisabeth Jouve ◽  
Fabrice Guillaume ◽  
Jean-Michel Azorin ◽  
Olivier Blin

2010 ◽  
Vol 34 (4) ◽  
pp. 207-221 ◽  
Author(s):  
Noah J. Sasson ◽  
Amy E. Pinkham ◽  
Jan Richard ◽  
Paul Hughett ◽  
Raquel E. Gur ◽  
...  

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