A Hierarchical Bayesian Model for Measuring Motion Adaptation

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 A Hierarchical Bayesian Model for Measuring Motion Adaptation (Unpublished).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.

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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).

Item Type:

Conference or Workshop Item (Poster)

Division/Institute:

07 Faculty of Human Sciences > Institute of Psychology > Cognitive Psychology, Perception and Methodology

UniBE Contributor:

Ellis, Andrew William and Mast, Fred

Subjects:

100 Philosophy > 150 Psychology

Funders:

[4] Swiss National Science Foundation

Projects:

[1207] Mental Imagery and Perceptual Learning

Language:

English

Submitter:

Andrew William Ellis

Date Deposited:

19 Jun 2014 10:58

Last Modified:

30 Sep 2019 13:34

BORIS DOI:

10.7892/boris.49470

URI:

https://boris.unibe.ch/id/eprint/49470

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