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The sound of thickness: Prelinguistic infants' associations of space and pitch

MPS-Authors
/persons/resource/persons1196

Dolscheid,  Sarah
Neurobiology of Language Department, MPI for Psycholinguistics, Max Planck Society;
International Max Planck Research School for Language Sciences, MPI for Psycholinguistics, Max Planck Society, Nijmegen, NL;
Categories across Language and Cognition, MPI for Psycholinguistics, Max Planck Society;

/persons/resource/persons119

Majid,  Asifa
Categories across Language and Cognition, MPI for Psycholinguistics, Max Planck Society;
Donders Institute for Brain, Cognition and Behaviour, External Organizations;
Language and Cognition Department, MPI for Psycholinguistics, Max Planck Society;

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paper0065.pdf
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引用

Dolscheid, S., Hunnius, S., Casasanto, D., & Majid, A. (2012). The sound of thickness: Prelinguistic infants' associations of space and pitch. In N., Miyake, D., Peebles, & R. P., Cooper (Eds.), Proceedings of the 34th Annual Meeting of the Cognitive Science Society (CogSci 2012) (pp. 306-311). Austin, TX: Cognitive Science Society.


引用: https://hdl.handle.net/11858/00-001M-0000-000F-3E74-2
要旨
People often talk about musical pitch in terms of spatial metaphors. In English, for instance, pitches can be high or low, whereas in other languages pitches are described as thick or thin. According to psychophysical studies, metaphors in language can also shape people’s nonlinguistic space-pitch representations. But does language establish mappings between space and pitch in the first place or does it modify preexisting associations? Here we tested 4-month-old Dutch infants’ sensitivity to height-pitch and thickness-pitch mappings in two preferential looking tasks. Dutch infants looked significantly longer at cross-modally congruent stimuli in both experiments, indicating that infants are sensitive to space-pitch associations prior to language. This early presence of space-pitch mappings suggests that these associations do not originate from language. Rather, language may build upon pre-existing mappings and change them gradually via some form of competitive associative learning.