Distinct colours in the ‘synaesthetic colour palette’

Abstract

In grapheme-colour synaesthesia, particular linguistic elements evoke particular colour sensations. Interestingly, when asked, non-synaesthetes can also associate colours to letters, and previous studies show that specific letter-to-colour associations have similar biases to those of synaesthetes. However, it is an open question whether the colours reported by synaesthetes and non-synaesthetes differ overall : is there a ‘synaesthetic colour palette’? In this study, we visualize the overall distribution in colour space of colour concurrents in grapheme-colour synaesthetes, and colour associations in non-synaesthetic controls. We confirm the existence of a synaesthetic colour palette: colour concurrents in synaesthetes are different from colour associations in non-synaesthetes. We quantify three factors that distinguish the colour palette of synaesthetes and non-synaesthetes: synaesthetes have an increased over-representation of ‘pure’ (unmixed) hues, an increased presence of ‘warm’ (yellow, orange, brown) colours, and an increased presence of achromatic (grey, white, black) colours. Furthermore, we demonstrate that differences in the synaesthetic colour palette can be used to train a machine learning algorithm to reliably classify single subjects as synaesthetes versus non-synaesthetes without using test–retest consistency data. As far as we know, this is the first time an individual could be ‘diagnosed’ as a synaesthete, based only on his or her colours evoked by letters. This article is part of a discussion meeting issue ‘Bridging senses: novel insights from synaesthesia’.

Crossmodal Correspondence

“…To do so, the CIELab values were converted into Berlin-Kay color categories (Berlin and Kay, 1969) using the online application Colournamer (Mylonas et al, 2010(Mylonas et al, , 2013, a computation model trained by over 3,000 participants, which returns for color inputs the most likely color name in several languages and its Berlin-Kay color category in English. This method of converting 3D color space coordinates into Berlin-Kay color categories has been used in the recent studies assessing typical grapheme-color associations (Root et al, 2017(Root et al, , 2019Rouw and Root, 2019). Upon visual inspection, the algorithm appears less precise for colors close to the grayscale axis (at a saturation below 1).…”

Bridging senses: novel insights from synaesthesia

“…Together, these four articles [10][11][12][13] provide a snapshot of how investigations of grapheme-colour (and related forms of ) synaesthesia are being used to address deep-seated questions about cognitive development, language acquisition, and the interplay of genes and environment in our learning processes.…”

“…The authors propose a model in which environmental factors evoke associations between graphemes and colours in all people (children and adults; synaesthetes and non-synaesthetes), but that children with a synaesthetic predisposition 'lock in' particular associations during development, yielding the stable associations in adulthood that are hallmarks of the condition [12]. In a related paper, Rouw & Root [13] note that while synaesthetes and non-synaesthetes may show similar biases in the specific letter-colour associations that they report, it has not previously been determined whether the colours themselves differ in their properties. The authors find that, compared with colour associations reported by non-synaesthetes, the colour concurrents of synaesthetes have an over-representation of unmixed hues, with the increased presence of warm (yellow-orange/brown) and achromatic (grey, white and black) colours [13].…”

“…In a related paper, Rouw & Root [13] note that while synaesthetes and non-synaesthetes may show similar biases in the specific letter-colour associations that they report, it has not previously been determined whether the colours themselves differ in their properties. The authors find that, compared with colour associations reported by non-synaesthetes, the colour concurrents of synaesthetes have an over-representation of unmixed hues, with the increased presence of warm (yellow-orange/brown) and achromatic (grey, white and black) colours [13]. Based on the existence of this 'synaesthetic colour palette', they develop a machine-learning approach for identifying people with synaesthesia without relying on more traditional test-retest consistency methods.…”

“…Spiller et al [16] investigated 70 adult individuals who were recruited to their study without reference to either synaesthesia or imagery, to ensure that they were naive to the goals of the research. Regardless of whether or not they were synaesthetic, all participants performed letter and number colour-picking consistency tests (see also [12,13] in this issue), and their mental imagery abilities were evaluated via self-report (Vividness of the Visual Imagery Questionnaire and Spontaneous Use of the Imagery Scale) and behavioural tests (Animal Tails and Mental Rotations). Colour-picking consistency was correlated with performance on the Animal Tails visual imagery test, but not with the Mental Rotations task or either self-report measure [16].…”