scholarly journals Computational Model of Biological Motion Detection: a path toward view-invariant action understanding

2015 ◽  
Vol 15 (12) ◽  
pp. 497
Author(s):  
Giulio Sandini ◽  
Nicoletta Noceti ◽  
Alessia Vignolo ◽  
Alessandra Sciutti ◽  
Francesco Rea ◽  
...  
Keyword(s):  
Computational Model ◽  
Motion Detection ◽  
Biological Motion ◽  
Action Understanding ◽  
Invariant Action

B09 Biological motion detection – a cognitive test for pigs transgenic for the huntington gene?

2018 ◽  
Author(s):  
Tamara Matheis ◽  
Robin Schubert ◽  
Lorena Rieke ◽  
Verena Schuldenzucker ◽  
Jan Motlik ◽  
...  
Keyword(s):  
Motion Detection ◽  
Biological Motion ◽  
Cognitive Test

scholarly journals Feature-based attentional tuning during biological motion detection measured with SSVEP

2017 ◽  
Vol 17 (9) ◽  
pp. 22 ◽  
Author(s):  
Rakibul Hasan ◽  
Ramesh Srinivasan ◽  
Emily D. Grossman
Keyword(s):  
Motion Detection ◽  
Biological Motion ◽  
Feature Based ◽  
Attentional Tuning

Toward a computational model of expression in music performance: The GERM model

2001 ◽  
Vol 5 (1_suppl) ◽  
pp. 63-122 ◽  
Author(s):  
Patrik N. Juslin ◽  
Anders Friberg ◽  
Roberto Bresin
Keyword(s):  
Computational Model ◽  
Emotional Expression ◽  
Biological Motion ◽  
Music Performance ◽  
Future Research ◽  
Listening Test ◽  
Motor Delay ◽  
Preliminary Version ◽  
Generative Structure

This article presents a computational model of expression in music performance: the GERM model. The purpose of the GERM model is to (a) describe the principal sources of variability in music performance, (b) emphasize the need to integrate different aspects of performance in a common model, and (c) provide some preliminaries (germ = a basis from which a thing may develop) for a computational model that simulates the different aspects. Drawing on previous research on performance, we propose that performance expression derives from four main sources of variability: (1) Generative Rules, which function to convey the generative structure in a musical manner (e.g., Clarke, 1988; Sundberg, 1988); (2) Emotional Expression, which is governed by the performer's expressive intention (e.g., Juslin, 1997a); (3) Random Variations, which reflect internal timekeeper variance and motor delay variance (e.g., Gilden, 2001; Wing and Kristofferson, 1973); and (4) Movement Principles, which prescribe that certain features of the performance are shaped in accordance with biological motion (e.g., Shove and Repp, 1995). A preliminary version of the GERM model was implemented by means of computer synthesis. Synthesized performances were evaluated by musically trained participants in a listening test. The results from the test support a decomposition of expression in terms of the GERM model. Implications for future research on music performance are discussed.

scholarly journals Biological motion detection does not involve an automatic "perspective taking"

2011 ◽  
Vol 11 (11) ◽  
pp. 743-743
Author(s):  
A. Christensen ◽  
W. Ilg ◽  
M. A. Giese
Keyword(s):  
Motion Detection ◽  
Perspective Taking ◽  
Biological Motion

scholarly journals Structural Neural Correlates of Biological Motion Detection Ability

2011 ◽  
Vol 11 (11) ◽  
pp. 687-687
Author(s):  
S. Gilaie-Dotan ◽  
R. Kanai ◽  
B. Bahrami ◽  
G. Rees ◽  
A. P. Saygin
Keyword(s):  
Motion Detection ◽  
Biological Motion ◽  
Neural Correlates ◽  
Detection Ability
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