Multidimensional scaling methods for absolute identification data

Title: Multidimensional scaling methods for absolute identification data
Creator: Dodds, Pennie; Donkin, Chris; Brown, Scott; Heathcote, Andrew
Relation: 32nd Annual Conference of the Cognitive Science Society (COGSCI 2010). Cognition in Flux. Proceedings of the 32nd Annual Conference of the Cognitive Science Society (Portland, OR 11-14 August, 2010) p. 2804-2809
Relation: http://mindmodeling.org/cogsci2010/papers/0664/index.html
Publisher: Cognitive Science Society
Resource Type: conference paper
Date: 2010
Description: Absolute identification exposes a fundamental limit in human information processing. Recent studies have shown that this limit might be extended if participants are given sufficient opportunity to practice. An alternative explanation is that the stimuli used – which vary on only one physical dimension – may elicit psychological representations that vary on two (or more) dimensions. Participants may learn to take advantage of this characteristic during practice, thus improving performance. We use multi-dimensional scaling to examine this question, and conclude that despite some evidence towards the existence of two dimensions, a one dimensional account cannot be excluded.
Subject: absolute identification; unidimensional stimuli; multidimensional scaling; MDS; learning
Identifier: http://hdl.handle.net/1959.13/935515
Identifier: uon:12075
Language: eng
Reviewed

		Thumbnail	File	Description	Size	Format