Languages and Cultures: Emotional Sapir-Whorf Hypothesis (ESWH)

An emotional version of Sapir-Whorf hypothesis suggests that differences in language emotionalities influence differences among cultures no less than conceptual differences.

An emotional version of Sapir-Whorf hypothesis suggests that differences in language emotionalities influence differences among cultures no less than conceptual differences.

via Languages and Cultures: Emotional Sapir-Whorf Hypothesis (ESWH).

Sapir Whorf Hypothesis (SWH)

Benjamin Whorf (Whorf, 1956) and Edward Sapir (Sapir, 1985) in a series of publications in the 1930s researched an idea that the way people think is influenced by the language they speak. Although there was a long predating linguistic and philosophical tradition, which emphasized influence of language on cognition (Bhartrihari, IVCE/1971; Humboldt, 1836/1967; Nietzsche, 1876/1983), this is often referenced as Sapir-Whorf hypothesis (SWH). Linguistic evidence in support of this hypothesis concentrated on conceptual contents of languages; for example, words for colors influence color perception (Roberson, Davidoff, & Braisbyb, 1999; Winawer, Witthoft, Frank, Wu, Wade, & Boroditsky, 2007). The idea of language influencing cognition and culture has been criticized and “fell out of favor” in the 1960s (Wikipedia, 2009a) due to a prevalent influence of Chomsky’s ideas emphasizing language and cognition to be separate abilities of the mind (Chomsky, 1965). Recently SWH again attracts much academic attention, including experimental confirmations (see the previous references) and theoretical skepticism (Pinker, 2007). Interactions between language and cognition were confirmed in fMRI experiments (Simmons, Stephan, Carla, Xiaoping, & Barsalou, 2008). Brain imaging experiments by Franklin, Drivonikou, Bevis, Davie, Kay, & Regier (2008) demonstrated that learning a word “rewires” cognitive circuits in the brain, learning a color name moves perception from right to left hemisphere. These recent data address in particular an old line of critique of SWH: whether relations between cultures and languages are causal or correlational; and if causal, what is the cause and what is the effect. Franklin et al (2008) experiments have demonstrated that language affects thinking. All arguments and experiments referenced above concentrated on conceptual effects of language.

Emotional effects might be no less important (Guttfreund, 1990; Harris, Ayçiçegi, & Gleason, 2003). In particular indicative are results of (Guttfreund, 1990): Spanish-English bilinguals reported more intense emotions in psychological interviews conducted in Spanish than in English, irrespective of whether their first language was English or Spanish.

Emotional Sapir Whorf Hypothesis (ESWH)

An emotional version of Sapir-Whorf hypothesis suggests that differences in language emotionalities influence differences among cultures no less than conceptual differences (Perlovsky 2009a). Conceptual contents of languages and cultures to significant extent are determined by words and their semantic differences; these could be borrowed among languages and exchanged among cultures. Emotional differences are related to grammar and mostly cannot be borrowed. Conceptual and emotional mechanisms of language interact and both affect the mind and cultural evolution. In language evolution from primordial undifferentiated animal cries conceptual contents increased, emotional reduced. Whereas animal voicing is controlled from ancient undifferentiated involuntary emotional center in limbic system, human evolve another emotional center in the cortex, partially under voluntary control; controlling voice voluntarily is necessary for evolution of language (Deacon, 1989; Lieberman, 2000; Mithen, 2007).

Emotionality of a language is carried by its sound, like emotionality of a song. Like in a song, but to a smaller degree, sounds of a language emotionally “color” semantic meanings. When an alpha-male ape barks, the neighbor knows unmistakably what it means, affective and semantic meanings are jointly present. Sound directly affects an ancient emotional center. In human languages sounds changed since our pre-human past. Therefore there is not much remaining “sound symbolism,” not much direct semantic meanings of sounds. Sound-meaning relations may seem arbitrary. But this is not quite so. We know from songs, that sound may affect us emotionally. If sounds in a language change slowly, sound-emotion-meaning connections might be strong; instead of inborn connections, human languages maintain habituated connections. Depending on the pace of sound change, different languages have different degrees of connections between meanings and emotions. In part this is controlled by grammar.

In inflectional languages, affixes and endings are fused with sounds of word roots. Pronunciation-sounds of affixes are controlled by few rules, which persist over thousands of words. These few rules are manifest in every phrase. Therefore every child learns to pronounce them correctly. Positions of vocal tract and mouth muscles for pronunciation of affixes are fixed throughout population and are conserved throughout generations. Correspondingly, pronunciation of whole words cannot vary too much, and language sound changes slowly. Inflections therefore play a role of “tail that wags the dog,” they anchor language sounds and preserve meanings. This is likely what Humboldt (1836) meant by “firmness” of inflectional languages. When inflections disappear, this anchor is no more; nothing prevents sound of language to become fluid and change with every generation.

This has happened with English language after transition from Middle English to Modern English (Lerer, 2007), most of inflections have disappeared and sound of the language started changing within each generation, this process continues today. English evolved into a powerful tool of cognition unencumbered by excessive emotionality, English language spread democracy and technology around the world. This was made possible by conceptual differentiation empowered by language, which overtook emotional synthesis. But the loss of synthesis (wholeness) has also lead to ambiguity of meanings and values. Current English language cultures face internal crises, uncertainty about meanings and purposes. Many people cannot cope with diversity of life. Future research in psycholinguistics, anthropology, history, historical and comparative linguistics, and cultural studies will examine interactions between languages and cultures. Initial experimental evidence suggests emotional differences among languages consistent with this hypothesis (Guttfreund, 1990; Harris, Ayçiçegi, & Gleason, 2003).

These effects can be modeled using mathematical models: the knowledge instinct driving cognitive evolution (Perlovsky 2006), the dual model connecting language and cognition (Perlovsky 2009b), neural modeling fields and dynamic logic describing joint evolution of language and cognition from vague primordial to differentiated contemporary states (Perlovsky 2004, 2006, 2007a, 2009a). These mathematical models identify three types of cultures: “conceptual” pragmatic cultures, in which emotionality of language is reduced and differentiation overtakes synthesis resulting in fast evolution at the price of uncertainty of values, self doubts, and internal crises; “traditional-emotional” cultures where differentiation lags behind synthesis, resulting in cultural stability at the price of stagnation; and “multi-cultural” societies combining fast cultural evolution and stability.


Bar, M., Kassam, K. S., Ghuman, A. S., Boshyan, J., Schmid, A. M., Dale, A. M., Hämäläinen, M. S., Marinkovic, K., Schacter, D. L., Rosen, B. R. & Halgren, E. (2006). Top-down facilitation of visual recognition. Proceedings of the National Academy of Sciences USA, 103, 449-54.

Bhartrihari. (IVCE/1971). The Vâkyapadîya, Critical texts of Cantos I and II with English Translation. Trans. K. Pillai. Delhi: Motilal Banarsidass.

Chomsky, N. (1965). Aspects of the theory of syntax. Cambridge: MIT Press.

Deacon, T. W. (1989). The neural circuitry underlying primate calls and human language. Human Evolution, 4(5): 367-401.

Fontanari, J. F. & Perlovsky, L. I. (2007). Evolving Compositionality in Evolutionary Language Games. IEEE Transactions on Evolutionary Computations., 11(6), 758-769. doi:10.1109/TEVC.2007.892763

Fontanari, J. F. & Perlovsky, L. I. (2008a). A game theoretical approach to the evolution of structured communication codes. Theory in Biosciences, 127(3), 205-214.

Fontanari, J. F. & Perlovsky, L. I. (2008b). How language can help discrimination in the Neural Modeling Fields framework. Neural Networks, 21(2-3), pp. 250–256.

Franklin, A., Drivonikou, G. V., Bevis, L., Davie, I. R. L., Kay, P. & Regier, T. (2008). Categorical perception of color is lateralized to the right hemisphere in infants, but to the left hemisphere in adults. PNAS, 105(9), 3221–3225.

Guttfreund D. G. (1990). Effects of language usage on the emotional experience of Spanish-English and English-Spanish bilinguals. J Consult Clin Psychol, 58, 604-607.

Harris, C. L., Ayçiçegi, A. & Gleason, J. B. (2003). Taboo words and reprimands elicit greater autonomic reactivity in a first language than in a second language. Applied Psycholinguistics, 24: 561-579.

Humboldt, W. von. (1836/1967). Über die Verschiedenheit des menschlichen Sprachbaues und ihren Einfluss auf die geistige Entwickelung des Menschengeschlechts. Berlin: F. Dummler. Also in Lehmann, W. P. Ed. A Reader in Nineteenth Century

Historical Indo-European Linguistics. Indiana University Press, Bloomington, IN.

Lerer, S. (2007). Inventing English. Chichester, NY: Columbia University Press.

Levine, D. S. & Perlovsky, L. I. (2008). Neuroscientific insights on Biblical myths: Simplifying heuristics versus careful thinking: Scientific analysis of millennial spiritual issues. Zygon, Journal of Science and Religion, 43(4), 797-821.

Lieberman, P. (2000). Human language and our reptilian brain. Cambridge: Harvard University Press.

Mithen, S. (2007). The Singing Neanderthals. Cambridge: Harvard Univ. Press.

Nietzsche, F. (1876/1983). Untimely Meditations. Tr. Hollingdale, Cambridge: Cambridge Univ. Press.

Perlovsky, L. I. (1997). Physical concepts of intellect . Proceedings of Russian Academy of Sciences, 354(3), 320-323.

Perlovsky, L. I. (2000). Neural Networks and Intellect: using model based concepts. New York: Oxford University Press.

Perlovsky, L.I. (2004). Integrating language and cognition. IEEE Connections, 2(2), 8-12.

Perlovsky, L. I. (2006a). Toward physics of the mind: concepts, emotions, consciousness, and symbols. Physics of Life Reviews 3, 23-55.

Perlovsky, L. I. (2006b). Modeling field theory of higher cognitive functions. In Artificial Cognition Systems, Eds. A. Loula, R. Gudwin, J. Queiroz. Hershey, PA: Idea Group, pp. 64-105.

Perlovsky, L. I. (2006c). Symbols: Integrated cognition and language. In Semiotics and Intelligent Systems Development. Eds. R. Gudwin, J. Queiroz. Hershey, PA: Idea Group, pp. 121-151.

Perlovsky, L. I. (2006d). Music–the first principles. Musical Theater;

Perlovsky, L. I. (2007a). Evolution of languages, consciousness, and cultures. IEEE Computational Intelligence Magazine, 2(3), 25-39.

Perlovsky, L. I. (2007b). Neural Networks, Fuzzy Models and Dynamic Logic. In Aspects of Automatic Text Analysis (Festschrift in Honor of Burghard Rieger), Eds. R. Köhler and A. Mehler, Springer, Germany, pp.363-386.

Perlovsky, L. & Kozma, R. (2007). Editorial – Neurodynamics of Cognition and Consciousness. In Neurodynamics of Cognition and Consciousness, Perlovsky, L. & Kozma, R. (eds), Springer Verlag, Heidelberg, Germany, pp.1-8.

Perlovsky, L. I. (2008). Music and consciousness, Leonardo, Journal of Arts, Sciences and Technology, 41(4), 420-421.

Perlovsky, L. I. (2009a). Language and Cognition. Neural Networks, 22(3), 247-257. doi:10.1016/j.neunet.2009.03.007

Perlovsky, L.I. (2009). Language and Emotions: Emotional Sapir-Whorf Hypothesis. Neural Networks, in print. doi:10.1016/j.neunet.2009.06.034

Perlovsky, L. I. & McManus, M. M. (1991). Maximum Likelihood Neural Networks for Sensor Fusion and Adaptive Classification. Neural Networks, 4(1), 89-102.

Pinker, S. (2007). The Stuff of Thought: Language as a Window into Human Nature, NY: Viking.

Roberson, D., Davidoff, J. & Braisbyb, N. (1999). Similarity and categorization: neuropsychological evidence for a dissociation in explicit categorization tasks. Cognition, 71, 1-42.

Sapir, E. (1985). Culture, Language and Personality: Selected Essays by Edward Sapir. Berkeley: University of California Press.

Simmons, W. K., Stephan B. H., Carla L. H., Xiaoping P. H. & Barsalou, L. W. (2008). fMRI evidence for word association and situated simulation in conceptual processing, Journal of Physiology – Paris 102, 106–119.

Whorf, B. L. (1956). Language, Thought, and Reality. Cambridge: MIT Press.

Wikipedia, (2009a). Linguistic relativity.

Wikipedia. (2009b). Sapir-Whorf hypothesis.

Winawer, J., Witthoft, N., Frank, M., Wu, L., Wade, A., & Boroditsky, L. (2007). Russian blues reveal effects of language on color discrimination. PNAS, 104(19), 7780–7785; 10.1073/pnas.0701644104

Further reading

  • Perlovsky, L.I. (2009a). Language and Cognition. Neural Networks, 22( 3), 247-257. (Link »)
  • Perlovsky, L.I. (2009b). Language and Emotions: Emotional Sapir-Whorf Hypothesis. Neural Networks, in print. doi:10.1016/j.neunet.2009.06.034 (Link »)
  • Perlovsky, L.I. (2006). Toward Physics of the Mind: Concepts, Emotions, Consciousness, and Symbols. Phys. Life Rev. 3(1), pp.22-55. (Link »)
  • Perlovsky, L.I. (2001). Neural Networks and Intellect: using model- based concepts. Oxford University Press, New York, NY (3rd printing) . (Link »)
  • Perlovsky, L.I. (2009). ‘Vague-to-Crisp’ Neural Mechanism of Perception. IEEE Trans. Neural Networks, in print. (Link »)
  • Mayorga, R., Perlovsky, L.I., Eds. (2007). Sapient Systems. Springer, London, UK. (Link »)
  • Perlovsky, L.I., Kozma, R., Eds. (2007). Neurodynamics of Higher-Level Cognition and Consciousness. ISBN 978-3-540-73266-2, Springer-Verlag, Heidelberg, Germany. (Link »)
  • Perlovsky, L.I. (2006). Symbols: Integrated Cognition and Language. Chapter in Semiotics and Intelligent Systems Development. Eds. R. Gudwin, J. Queiroz. Idea Group, Hershey, PA, pp.121-151. (Link »)
  • Perlovsky, L.I. (2006). Modeling Field Theory of Higher Cognitive Functions. Chapter in Artificial Cognition Systems, Eds. A. Loula, R. Gudwin, J. Queiroz. Idea Group, Hershey, PA, pp.64-105. (Link »)

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