de.mpg.escidoc.pubman.appbase.FacesBean
Deutsch
 
Hilfe Wegweiser Impressum Kontakt Einloggen
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Zeitschriftenartikel

Texture and haptic cues in slant discrimination: Reliability-based cue weighting without statistically optimal cue combination

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84925

Rosas,  P
Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83906

Wagemans J, Ernst,  MO
Research Group Multisensory Perception and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84314

Wichmann,  FA
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

Externe Ressourcen
Es sind keine Externen Ressourcen verfügbar
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Rosas, P., Wagemans J, Ernst, M., & Wichmann, F. (2005). Texture and haptic cues in slant discrimination: Reliability-based cue weighting without statistically optimal cue combination. Journal of the Optical Society of America A, 22(5), 801-809. Retrieved from http://josaa.osa.org/DirectPDFAccess/2E1BCF03-BDB9-137E-C541C6B14AA1F5A7_83573.pdf?da=1id=83573seq=0CFID=44254280CFTOKEN=96368927.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-D595-E
Zusammenfassung
A number of models of depth cue combination suggest that the final depth percept results from a weighted average of independent depth estimates based on the different cues available. The weight of each cue in such an average is thought to depend on the reliability of each cue. In principle, such a depth estimation could be statistically optimal in the sense of producing the minimum variance unbiased estimator that can be constructed from the available information. Here we test such models using visual and haptic depth information. Different texture types produce differences in slant discrimination performance, providing a means for testing a reliability-sensitive cue combination model using texture as one of the cues to slant. Our results show that the weights for the cues were generally sensitive to their reliability, but fell short of statistically optimal combination—we find reliability-based re-weighting, but not statistically optimal cue combination.