scholarly journals Limited Plasticity of Prismatic Visuomotor Adaptation

i-Perception ◽  
2017 ◽  
Vol 8 (2) ◽  
pp. 204166951770145 ◽  
Author(s):  
Karoline Spang ◽  
Sven Wischhusen ◽  
Manfred Fahle
2012 ◽  
Vol 22 (2) ◽  
pp. 144-154 ◽  
Author(s):  
Genevieve Albouy ◽  
Gilles Vandewalle ◽  
Virginie Sterpenich ◽  
Geraldine Rauchs ◽  
Martin Desseilles ◽  
...  

Author(s):  
Erin K. Cressman ◽  
Danielle Salomonczyk ◽  
Alina Constantin ◽  
Janis Miyasaki ◽  
Elena Moro ◽  
...  

10.1167/1.2.3 ◽  
2001 ◽  
Vol 1 (2) ◽  
pp. 3 ◽  
Author(s):  
Douglas W. Cunningham ◽  
Astros Chatziastros ◽  
Markus von der Heyde ◽  
Heinrich H. Bülthoff

2014 ◽  
Vol 14 (9) ◽  
pp. 4-4 ◽  
Author(s):  
B. Barton ◽  
A. Treister ◽  
M. Humphrey ◽  
G. Abedi ◽  
S. C. Cramer ◽  
...  

Author(s):  
Eugene Poh ◽  
Naser Al-Fawakari ◽  
Rachel Tam ◽  
Jordan A. Taylor ◽  
Samuel D. McDougle

ABSTRACTTo generate adaptive movements, we must generalize what we have previously learned to novel situations. The generalization of learned movements has typically been framed as a consequence of neural tuning functions that overlap for similar movement kinematics. However, as is true in many domains of human behavior, situations that require generalization can also be framed as inference problems. Here, we attempt to broaden the scope of theories about motor generalization, hypothesizing that part of the typical motor generalization function can be characterized as a consequence of top-down decisions about different movement contexts. We tested this proposal by having participants make explicit similarity ratings over traditional contextual dimensions (movement directions) and abstract contextual dimensions (target shape), and perform a visuomotor adaptation generalization task where trials varied over those dimensions. We found support for our predictions across five experiments, which revealed a tight link between subjective similarity and motor generalization. Our findings suggest that the generalization of learned motor behaviors is influenced by both low-level kinematic features and high-level inferences.


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