Elsevier

Cortex

Volume 99, February 2018, Pages 375-389
Cortex

Research report
Why is the synesthete's “A” red? Using a five-language dataset to disentangle the effects of shape, sound, semantics, and ordinality on inducer–concurrent relationships in grapheme-color synesthesia

https://doi.org/10.1016/j.cortex.2017.12.003Get rights and content

Abstract

Grapheme-color synesthesia is a neurological phenomenon in which viewing a grapheme elicits an additional, automatic, and consistent sensation of color. Color-to-letter associations in synesthesia are interesting in their own right, but also offer an opportunity to examine relationships between visual, acoustic, and semantic aspects of language. Research using large populations of synesthetes has indeed found that grapheme-color pairings can be influenced by numerous properties of graphemes, but the contributions made by each of these explanatory factors are often confounded in a monolingual dataset (i.e., only English-speaking synesthetes). Here, we report the first demonstration of how a multilingual dataset can reveal potentially-universal influences on synesthetic associations, and disentangle previously-confounded hypotheses about the relationship between properties of synesthetic color and properties of the grapheme that induces it. Numerous studies have reported that for English-speaking synesthetes, “A” tends to be colored red more often than predicted by chance, and several explanatory factors have been proposed that could explain this association. Using a five-language dataset (native English, Dutch, Spanish, Japanese, and Korean speakers), we compare the predictions made by each explanatory factor, and show that only an ordinal explanation makes consistent predictions across all five languages, suggesting that the English “A” is red because the first grapheme of a synesthete's alphabet or syllabary tends to be associated with red. We propose that the relationship between the first grapheme and the color red is an association between an unusually-distinct ordinal position (“first”) and an unusually-distinct color (red). We test the predictions made by this theory, and demonstrate that the first grapheme is unusually distinct (has a color that is distant in color space from the other letters' colors). Our results demonstrate the importance of considering cross-linguistic similarities and differences in synesthesia, and suggest that some influences on grapheme-color associations in synesthesia might be universal.

Introduction

Grapheme-color synesthesia is a neurological phenomenon in which a percept (the synesthetic inducer) elicits an additional, automatic, and consistent sensation (the synesthetic concurrent). In grapheme-color synesthetes – one of the most commonly-studied forms – a grapheme will elicit the perception of a color. Strikingly, the relationship between a specific inducer and concurrent is highly consistent within a single synesthete: when asked to choose (using a color-picker) the color elicited by a grapheme, grapheme-color synesthetes will consistently choose the same color, even when testing periods are separated by months or years (e.g., Asher, Aitken, Farooqi, Kurmani, & Baron-Cohen, 2006). On the other hand, the relationship between a specific inducer and concurrent is often inconsistent across synesthetes; in other words, while one synesthete might consistently experience a yellow “C”, another might consistently experience a green “C”.

The heterogeneity of associations between pairs of synesthetes suggests that the relationship between inducer and concurrent is idiosyncratic; indeed, some theories of synesthesia consider between-subject idiosyncrasy to be a defining feature (e.g., Grossenbacher and Lovelace, 2001, Spence and Deroy, 2013). However, a growing number of studies using large samples of synesthetes have demonstrated that some letters are associated with a particular color more often than would be expected by chance (Day, 2004, Rich et al., 2005, Simner et al., 2005). Consistent associations between letters and colors even exist in non-synesthetes. Simner et al. (2005) demonstrated that non-synesthetes associated some letters with a particular color more often than chance; some of these trends were shared with synesthetes (e.g., both synesthetes and nonsynesthetes associated “A” with red), and some were not (e.g., synesthetes associate “O” with white, whereas non-synesthetes associate “O” with orange). In a cross-linguistic study, English-, Dutch-, and Hindi-speaking non-synesthetes were shown to have consistent color preferences (Rouw, Case, Gosavi, & Ramachandran, 2014). Some of these preferences were found to be similar across languages, and also similar between self-identified synesthetes and non-synesthetes (e.g., again, both synesthetes and nonsynesthetes associated “A” with red). These findings raise the question: if at least some inducer concurrent relationships are not random, are inducer–concurrent relationships in synesthesia driven by universal biases (across languages, and shared between synesthetes and non-synesthetes)? The underlying notion that specific inducer–concurrent relationships might be caused by specific properties of the inducing grapheme is still much debated, but in the past decade a number of studies have demonstrated that several properties can influence inducer–concurrent relationships in both synesthetes and non-synesthetes.

Below, we review a number of properties that have been shown to influence inducer–concurrent relationships, which we term Explanatory Factors (EFs). These EFs can exert influences on first-order associations, causing a grapheme with a particular property to be associated with a particular color, or on second-order associations, causing graphemes with similar properties to be associated with similar colors.

Perhaps the most prominent and intuitive explanation of first-order inducer–concurrent relationships invokes semantic associations between a grapheme and a word that begins with that grapheme. Color names appear to strongly influence inducer–concurrent relationships: “R” is typically red, “Y” is typically yellow, and so on (Rich et al., 2005, Simner et al., 2005).

Semantic associations could also influence inducer–concurrent relationships if a grapheme is commonly associated with a word that has a prototypical color; for example, “D” could be brown because “D is for dog”, and dogs are often brown. To formally test these hypotheses, Mankin and Simner (2017) used data from a word-generation experiment on non-synesthetic subjects to determine the most common letter-word semantic associations (which they term index words), and used data from a separate group of subjects to determine the most prototypical color associated with those index words. They then demonstrated that the prototypical color of index words correctly predicted the most commonly-associated color for 15/26 graphemes, far more than would be expected by chance.

Visual features can influence first-order inducer–concurrent relationships. Hubbard, Ambrosio, Azoulai, and Ramachandran (2005) first suggested that synesthetes might associate letters that have curved versus sharp features with “warm” versus “cool” colors, though this observation was not quantified. Spector and Maurer (2011) propose that the common synesthetic associations between “O” and white, and between “X” and “Z” and black result from tendencies to associate smooth versus jagged shapes with white versus black colors. To avoid the potential confound of semantic associations, they measured the strength of this effect in non-synesthetic, pre-literate children, and demonstrate that these children still associate “O” with white and “X” and “Z” with black significantly more often than expected by chance. The influence of visual shape is also not limited to letters: a study with bi- and trilingual synesthetes showed that the synesthetic colors induced in the non-native language were predicted by visual similarity to words in the native language (Barnett, Feeney, Gormley, & Newell, 2009).

Visual shape has also been shown to induce a second-order effect on inducer–concurrent relationships: letters that share visual features (such as symmetry, curvature, or repeating elements) are associated with similar colors in English-speaking synesthetes (Brang et al., 2011, Watson et al., 2012) and German synesthetes (Jürgens & Nikolić, 2012); furthermore, the effect transfers to newly learned graphemes (Jürgens & Nikolić, 2012). Asano and Yokosawa (2013) found that this effect is stronger in English-speaking synesthetes than in Japanese-speaking synesthetes.

Marks (1975) found that synesthetes with heterogeneous linguistic backgrounds (many of whom were French and German) have consistent associations between vowel inducers and their concurrent colors He tabulated the results from three large scale and 35 small studies, and showed that for each of these datasets the vowel a tended to be red and blue, e and i tended to be yellow and white, o tended to be red and black, u was usually blue, brown, or black, and ou (in French) was brown. By ranking the vowels in acoustic “brightness” (pitch), he showed how the findings could be explained as a generalization of the correlation between visual brightness and visual pitch.

Guillamón (2014) examined, in non-synesthetes, associations between particular sounds and particular colors across different languages. Properties of the vowel spectrum were shown to be associated with certain colors (e.g., the front-mid spectrum is associated with green). Interestingly, the front-open spectrum, where the /a/ or /ɑ/ sounds are located, was found to be associated with red, in Japanese (Miyahara, Amemiya, & Sekiguchi, 2006), Polish and English (Wrembel, 2007), and Arabic (Guillamón, 2014). By using synesthetic vowel sounds manipulated in the two dimensions of a position of an articulatory organ tongue body, Kim, Nam, & Kim (in press) found that low vowels such as [a] are associated with more reddish colors in non-synesthetes.

There is also mixed evidence that acoustic similarity exerts second-order effects: similarly-pronounced letters are associated with similar colors in Japanese (Asano & Yokosawa, 2011) and Korean (Kang, Kim, Shin, & Kim, 2017), but not in English (Watson et al., 2012).

One property of many languages is that their graphemes have a defined order (alphabet, syllabary, etc.), leading to the possibility that position in the alphabet could affect synesthetic color. Overall, position in the alphabet does not appear to affect synesthetic color (Simner et al., 2005). However, it is possible that ordinal position only influences color for particularly-salient ordinal positions, such as the first or last grapheme in the alphabet. Indeed, Rouw et al. (2014) found that not only was the first grapheme typically red for American, Dutch, and Hindi synesthetes (and also non-synesthetes), Monday (the first day of the workweek in all three cultures) was also associated with red in Dutch, English, and Hindi calendar-color synesthetes (and also non-synesthetes), suggesting that the property of “first” is associated with the color red.

Eagleman (2010) reported that letters in the beginning of the alphabet are associated with colors that are distinct from each other, and letters at the end of the alphabet are associated with colors that are similar to each other; in other words, a second-order relationship between ordinal position and color distinctness. Using a second-order similarity mappings similar to Watson et al. (2012), Asano and Yokosawa (2013) examined determinants of synesthetic colors to Hiragana (a phonetic script in Japanese language). Color distance (and luminance, saturation, hue distance) was predicted most strongly by differences in ordinality (position in grapheme sequence), followed by phonological similarity, and weakest by visual shape similarity and grapheme familiarity.

Simner et al. (2005) showed in English-speaking synesthetes that grapheme frequency was positively correlated with frequency of color names. Beeli, Esslen, and Jäncke (2007) found, in German-speaking synesthetes, a positive correlation between letter frequency and saturation (though see Simner & Ward, 2008), a finding replicated in Korean by Kim and Kim (2014). Grapheme frequency is related to color luminance, and this effect is also present (though weaker) in non-synesthetes (Smilek et al., 2007, Watson et al., 2012).

Grapheme-color relations are also influenced by the ease of generation of the color name or of the color category. Simner et al. (2005) found that non-synesthetes were more likely to associate letters earlier in the alphabet with colors that are easier to generate. Van Leeuwen, Dingemanse, Todil, Agameya, and Majid (2016) further showed that higher-frequency letters are more likely to be associated with colors earlier in the Berlin–Kay color sequence (Berlin & Kay, 1991). The sequence of colors in the Berlin–Kay hierarchy reflects the order in which colors are introduced into languages (Malt & Majid, 2013). They represent a psychological, rather than optical or electromagnetic, view on colors (though see Regier, Kay, & Khetarpal, 2007): the 11 “basic” Berlin–Kay colors are the 11 monomorphemic, monolexemic color categories into which people tend to categorize other colors (e.g., “crimson” a shade of “red”, but “red” is not a shade of another color).

Note that the explanations provided above are neither exhaustive nor mutually exclusive. Hung, Simner, Shillcock, and Eagleman (2014) studied the relationship between synesthetic colors and different constituent morphological units of Chinese characters (radicals), showing that hue was determined by the semantic component while luminance was determined by the phonetic component. The effects may also interact: Bargary, Barnett, Mitchell, and Newell (2009) used a multisensory illusion, the McGurk effect, to show that for phoneme-color synesthetes the colors induced by spoken words are influenced by a combination of audio and visual input, and not by auditory or visual input alone.

Furthermore, inducer–concurrent relationships may depend on specific patterns of learning during development. For example, Japanese speakers typically learn the Hiragana script before the Katakana or Kanji scripts; synesthetic colors of Kanji and Katakana graphemes are influenced by phonological similarity to the Hiragana script, rather than by orthographic properties (visual shape, ordinality, etc.) of the Katakana/Kanji script (Asano & Yokosawa, 2012).

These findings support the notion that the concept of “letter” is not represented in isolation, but is connected to perceptual representational systems, and that some (but not necessarily all) of these connections might be shared across different languages and cultures. What causes these conscious and unconscious cross-domain connections? The answer to this question is not only interesting in its own right. As elegantly pointed out by Simner (2007), synesthesia is often studied as a sensory phenomenon, but it should also be considered as a psycholinguistic phenomenon. We know that synesthetic associations develop during early childhood (Simner & Bain, 2013), but little is known about how these associations relate to childhood learning mechanisms. The color-to-letter associations obtained in the synesthesia literature offer an extraordinary opportunity to examine relationships between linguistic processes and visual, acoustic, and semantic aspects of language learning.

Some Explanatory Factors might exert more influences in languages with particular properties. For example, the Acoustic EF correctly predicts inducer–concurrent relationships in Japanese Hiragana characters (Asano & Yokosawa, 2011) and Korean Hangul characters (Kang et al., 2017), but not in English letters (Watson et al., 2012). Asano and Yokosawa (2013) propose a model for this discrepancy that invokes the linguistic property of orthographic depth – the degree to which graphemes' pronunciation is consistent and predictable. In their model, the feature (they consider acoustic, ordinal, and visual features) of a grapheme that ultimately determines its color is the feature of that grapheme which is most discriminatory or salient during language acquisition. In this framework, the Acoustic EF exerts a stronger influence in Japanese than in English because in Japanese (unlike English), the relationship between a grapheme and its pronunciation is highly consistent, and the syllabary (syllable alphabet) is arranged by sound similarity, both of which are features that would ensure that acoustic properties of graphemes are more salient during the language learning process. More generally, Asano and Yokosawa (2013) propose that, for each grapheme, its most distinctive feature (whether it be ordinal, acoustic, visual, etc.) is the feature that will ultimately influence the grapheme-color association for that particular grapheme.

From the results reviewed in Section 1.1, it is clear that even within a single language (e.g., English), multiple Explanatory Factors can predict a subset of inducer–concurrent relationships. How do Explanatory Factors interact? Notably, EFs can make both congruent and incongruent predictions about the expected concurrent color of a grapheme. For example, the Semantic EF might predict “V” to be purple (via violet) and “X” to be black (via x-ray), but the Visual Shape EF might predict “V” and “X” (which share many visual features, such as symmetry, diagonal elements, and no curvature) to share similar colors – i.e., incongruent predictions. On the other hand, the Semantic EF might predict “P” to be pink and “R” to be red, and the Visual Shape EF would predict “P” and “R” to share similar colors – i.e., congruent predictions.

When Explanatory Factors make incongruent predictions, their contributions to a given inducer–concurrent relationship are straightforward to determine. In Mankin and Simner's (2017) data, for example, synesthetes usually experience a purple “V” and a black “X”, suggesting that for these particular graphemes the Semantic EF “beats” the Visual Shape EF in a “winner-takes-all” effect. We propose that this is consistent with a within-language application of the model of Asano and Yokosawa (2013): whichever property of a grapheme is most salient is ultimately the property that influences its color. In this framework, the semantic association of “V” with “violet” is more salient than the visual similarity between “V” and “X”, and so the Semantic EF influences the color of V.

When Explanatory Factors make congruent predictions, their contributions to a given inducer–concurrent relationship are confounded. For example, one particularly-consistent finding – perhaps the strongest association reported in synesthesia literature – is that English-speaking synesthetes experience the letter “A” as colored red far more often than expected by chance (e.g., Barnett et al., 2009; Day, 2004, Ramachandran and Hubbard, 2001, Rich et al., 2005, Simner et al., 2005). The Semantic (Mankin & Simner, 2017), Acoustic (Kim, Nam, and Kim (in press); Marks, 1975), and Ordinal (Rouw et al., 2014) EFs each predict that “A” should be red, so in a monolingual English dataset it is not possible to determine which EF is responsible for this association (or whether they combine additively – a cooperative interaction). For English, each EF offers different, yet equally plausible explanations for the finding that “A” is red. In the present study, we demonstrate that a multilingual dataset allows us to disentangle and contrast different Explanatory Factors of inducer–concurrent relationships. Further, we demonstrate that a language-independent Explanatory Factor best explains our data, suggesting that universal (cross-language) inducer–concurrent relationships do exist in synesthesia.

Most studies of synesthesia as a psycholinguistic phenomenon have examined synesthetes in only one language (typically English); indeed, the few studies that have tested synesthetes in multiple languages have either used non-native speakers (Asano and Yokosawa, 2013, Shin and Kim, 2014), or have only tested for broad correlations between languages (Rouw et al., 2014). Studying inducer–concurrent relationships with native speakers of different languages might enable researchers to disentangle the effects of EFs that make congruent predictions about an inducer–concurrent relationship. For example, while the Semantic and Ordinal EFs both predict English “A” to be red, in Spanish, the Semantic EF would predict “A” to be blue (via azul), whereas the Ordinal EF would still predict “A” to be red.

To illustrate the methodological advantages of multilingual synesthesia research, we combine previously-collected synesthetic associations from native speakers of five different languages into a single dataset, and derive and test predictions that four different Explanatory Factors make about the color of graphemes in Spanish-, Dutch-, Japanese-, and Korean-speaking synesthetes. Specifically, we attempt to discover which Explanatory Factor(s) causes the English “A” to be associated with the color red, by comparing the predictions that each of these EFs make about associations in the other languages in our dataset.

Our choice of languages was driven by two factors: data availability, and the idiosyncratic properties of each language. While we expected semantic associations to differ between most languages, the influence of some EFs can only be disentangled with certain languages. Dutch is closely related to English (it is also part of the Germanic branch of the Indo-European language family), and shares many linguistic properties with English. However, including Dutch in this study allows us to contrast two types of acoustic EF: the phoneme of the English letter “A” is [aː] (in IPA), similar to Dutch; however, the name of the letter A is very different in the two languages: the letter is called [aː] in Dutch, but [eɪ] in English (furthermore, the sound [eɪ] is in Dutch the name of the letter “E”). Spanish shares the same alphabet as Dutch and English, but is otherwise quite different. Spanish has a shallow, transparent orthography (Bravo-Valdivieso and Escobar, 2014, Seymour et al., 2003) and a smaller vowel inventory (Bradlow, 1995), so the Acoustic EF might be expected to play a larger role in determining inducer–concurrent relationships. Finally, Spanish is a Romance language rather than a Germanic language, so we might expect semantic associations to differ more than between English and Dutch; indeed, recent research suggests that different linguistic backgrounds (Spanish vs English) lead to language-dependent cross-modal associations in non-synesthetes (Fernandez-Prieto, Spence, Pons, & Navarra, 2017). Korean is one of the few commonly-spoken languages in which the grapheme encoding “A” is not the first letter of the alphabet (the first letter of the Korean Hangul alphabet roughly corresponds to [g∼k]), enabling us to disentangle the ordinal EF. However, in Korean the Visual and Acoustic EFs are confounded, because Hangul is a featural alphabet – similar-shaped graphemes encode similar-sounding phonemes. The last language in our dataset, Japanese, allows for us to completely disentangle the visual EF: the Japanese Hiragana syllabary is visually quite different from the Roman alphabet, and in Japanese there is no relationship between the visual form of a grapheme and its pronunciation.

Section snippets

Experiment 1: replicating the result that “A is often red”

The propensity for English-speaking grapheme-color synesthetes to associate “A” with the color red has been formally tested for British (Simner et al., 2005) and Australian (Rich et al., 2005) synesthetes, but not for American synesthetes (however, see Day, 2004 for a descriptive report). We first sought to replicate these results in our American sample.

Experiment 2: why is “A” red?

Why are English-speaking synesthetes likely to associate “A” with red? The letter “A” has numerous properties, including its shape, its sound, its ordinal position in the alphabet, and its semantic associations. Each of these potential Explanatory Factors likely explains some subset of grapheme-color associations (as noted in Section 1.3 in the introduction), but in a monolingual English dataset it is not possible to determine which EF accounts for the propensity for the red “A”. However, these

Experiment 3: semantic associations

Mankin and Simner (2017) suggest that the color of letters might be influenced by an index word (a commonly-generated word beginning with the grapheme) that has a prototypical color. In other words, for English speakers, “A” could be red more often than chance because “A” is often associated with the word “apple”, and apples are prototypically red. Our result from Experiment 2 could be confounded if index words for the first grapheme in the other languages in our dataset were all

Experiment 4: distinctness of the first grapheme

The Ordinal EF (which our data supports as the most likely explanation for why “A” is red) explains why the first grapheme is a consistent color, but not why the first grapheme is red. Why red, and not some other color?

The color red has several properties that might cause it to be considered “distinct”, or “special”. First, red is typically the most basic color term acquired by a culture, after “dark” and “light” (Berlin & Kay, 1991). Second, red may have been an important signal color in our

General discussion

We replicated the finding that, for English-speaking synesthetes, “A” is red much more often than would be expected by chance. Using a five-language dataset, we tested a number of different hypotheses that sought to explain why the English “A” is red using different Explanatory Factors (visual shape, acoustic, semantic, ordinal). Only the Ordinal EF (“the first grapheme in the alphabet/syllabary is often red”) was strongly supported in all five languages in our dataset, and all other hypotheses

Acknowledgments

We thank David Brang, Jessica Gang and Sarah Rainsdon for help with American data collection and sharing. We thank Yeseul Kim and Yuna Kwak for their help with Korean data collection and sharing. We thank Soichiro Takahashi, Takuya Tsushiro, and Lisa Tobayama for their help with Japanese data collection and sharing. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF-2016R1A2B4011267) awarded to C-YK, and by Japan

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