A Hierarchical Bayesian Model for Measuring Motion Adaptation


Autoria(s): Ellis, Andrew William; Mast, Fred
Data(s)

24/01/2014

Resumo

Previous research has shown that motion imagery draws on the same neural circuits that are involved in perception of motion, thus leading to a motion aftereffect (Winawer et al., 2010). Imagined stimuli can induce a similar shift in participants’ psychometric functions as neural adaptation due to a perceived stimulus. However, these studies have been criticized on the grounds that they fail to exclude the possibility that the subjects might have guessed the experimental hypothesis, and behaved accordingly (Morgan et al., 2012). In particular, the authors claim that participants can adopt arbitrary response criteria, which results in similar changes of the central tendency μ of psychometric curves as those shown by Winawer et al. (2010).

Formato

application/pdf

Identificador

http://boris.unibe.ch/49470/1/poster_ssn_201401.pdf

Ellis, Andrew William; Mast, Fred (24 January 2014). A Hierarchical Bayesian Model for Measuring Motion Adaptation (Unpublished). In: Swiss Society for Neuroscience Annual Meeting 2014. Bern, Switzerland. 24.-25.01.2014.

doi:10.7892/boris.49470

Idioma(s)

eng

Relação

http://boris.unibe.ch/49470/

Direitos

info:eu-repo/semantics/openAccess

Fonte

Ellis, Andrew William; Mast, Fred (24 January 2014). A Hierarchical Bayesian Model for Measuring Motion Adaptation (Unpublished). In: Swiss Society for Neuroscience Annual Meeting 2014. Bern, Switzerland. 24.-25.01.2014.

Palavras-Chave #150 Psychology
Tipo

info:eu-repo/semantics/conferenceObject

info:eu-repo/semantics/draft

NonPeerReviewed