Diagnosing synaesthesia with online colour pickers: Maximising sensitivity and specificity

Rothen, Nicolas; Seth, Anil K.; Witzel, Christoph; Ward, Jamie (2013). Diagnosing synaesthesia with online colour pickers: Maximising sensitivity and specificity. Journal of neuroscience methods, 215(1), pp. 156-160. Elsevier 10.1016/j.jneumeth.2013.02.009

[img] Text
1-s2.0-S0165027013000757-main.pdf - Published Version
Restricted to registered users only
Available under License Publisher holds Copyright.

Download (339kB) | Request a copy

The most commonly used method for formally assessing grapheme-colour synaesthesia (i.e., experiencing colours in response to letter and/or number stimuli) involves selecting colours from a large colour palette on several occasions and measuring consistency of the colours selected. However, the ability to diagnose synaesthesia using this method depends on several factors that have not been directly contrasted. These include the type of colour space used (e.g., RGB, HSV, CIELUV, CIELAB) and different measures of consistency (e.g., city block and Euclidean distance in colour space). This study aims to find the most reliable way of diagnosing grapheme-colour synaesthesia based on maximising sensitivity (i.e., ability of a test to identify true synaesthetes) and specificity (i.e., ability of a test to identify true non-synaesthetes). We show, applying ROC (Receiver Operating Characteristics) to binary classification of a large sample of self-declared synaesthetes and non-synaesthetes, that the consistency criterion (i.e., cut-off value) for diagnosing synaesthesia is considerably higher than the current standard in the field. We also show that methods based on perceptual CIELUV and CIELAB colour models (rather than RGB and HSV colour representations) and Euclidean distances offer an even greater sensitivity and specificity than most currently used measures. Together, these findings offer improved heuristics for the behavioural assessment of grapheme-colour synaesthesia.

Item Type: Journal Article (Original Article)
Division/Institute: 07 Faculty of Human Sciences > Institute of Psychology > Experimental Psychology and Neuropsychology
UniBE Contributor: Rothen, Nicolas
Subjects: 100 Philosophy > 150 Psychology
600 Technology > 610 Medicine & health
ISSN: 0165-0270
Publisher: Elsevier
Language: English
Submitter: Nicolas Rothen
Date Deposited: 24 Feb 2016 14:16
Last Modified: 24 Feb 2016 14:16
Publisher DOI: 10.1016/j.jneumeth.2013.02.009
PubMed ID: 23458658
BORIS DOI: 10.7892/boris.75978
URI: http://boris.unibe.ch/id/eprint/75978

Actions (login required)

Edit item Edit item
Provide Feedback