MIT HST 722 - Structure and function of auditory cortex

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Robert J. Zatorre*Montreal NeurologicalInstitute, 3801 University St,Montreal, Québec,Canada H3A 2B4.*e-mail:[email protected] BelinDept of Psychology,Université de Montréal,CP 6128 succ Centre-Ville,Montreal, Québec,Canada H3C 3J7.Virginia B.Penhune Dept of Psychology,Concordia University,7141 Sherbrooke West,Montreal, Québec, CanadaH4B 1R6.Music and speech represent the most cognitivelycomplex uses of sound by the human species. Thesetwo domains share a number of properties, includingthe fact that they take advantage of modulations ofacoustic parameters specifically for information-bearing purposes. Moreover, both music and speechare characterized by their generative nature: that is,complexity is built up by rule-based permutations ofa limited number of discrete elements (phonemes ortones) to yield meaningful structures (words ormelodies), which in turn are subject to furtherhierarchical organization resulting in more complexentities (such as sentences or songs) [1]. Apart fromthese formal considerations, music and speech showother interesting similarities. For example, bothshow specific and relatively fixed developmental timecourses [2], and all known human societies make useof both speech and music, regardless of technologicalsophistication [3].The foregoing ideas do not imply that music andspeech necessarily share either a similar underlyingcognitive or neural representation (for furtherdiscussion see Refs [4,5]), but they do suggest thatboth might derive from certain functional propertiesof our auditory nervous system. Put another way,given that all normal humans seem to be capable ofrelatively sophisticated musical and speechfunctions in the absence of explicit training, then itfollows that these cognitive–behavioral skills arelikely to be related to the functional organization ofthe human auditory nervous system. Just as a bat’sability to use echolocation is related to the uniqueorganization of its auditory system, so the humannervous system may be considered as beingorganized such that it enables people to readilyunderstand speech and music of the culture inwhich they are raised. This article explores whatthis organization might be, with the aim of throwinglight upon the neural mechanisms responsible forthe low-level perceptual input stage that is relevantfor music and speech, and, conversely, takingadvantage of speech and music to understand thefunction of the auditory cortex.Speech versus music: different acoustic features?Before considering the evidence regarding neuralspecializations, it is useful to consider some relevantacoustic properties of speech and music. Perhaps themost obvious differences between the two relate tothe fact that speech is produced by a single‘instrument’– the human voice, whereas music canbe produced by practically anything capable ofgenerating sound, including, of course, the voice.However, certain acoustic parameters have beenWe examine the evidence that speech and musical sounds exploit differentacoustic cues: speech is highly dependent on rapidly changing broadbandsounds, whereas tonal patterns tend to be slower, although small and precisechanges in frequency are important. We argue that the auditory cortices in thetwo hemispheres are relatively specialized, such that temporal resolution isbetter in left auditory cortical areas and spectral resolution is better in rightauditory cortical areas. We propose that cortical asymmetries might havedeveloped as a general solution to the need to optimize processing of theacoustic environment in both temporal and frequency domains.Structure and function of auditorycortex:music and speechRobert J.Zatorre,Pascal Belin and Virginia B. Penhune27 Michelsen, A. et al. (1994) Physics of directionalhearing in the cricket gryllus bimaculatus.J. Comp. Physiol. Ser. A175, 153–16228 Webb, B. (1996) A robot cricket. Sci. Am.275, 94–9929 Pfeifer, R. (2000) On the relation amongmorphology and materials in adaptive behavior.In From Animals to Animats 6: Proc. 6th Int. Conf.Simulation Adapt. Behav. (Meyer, J.A. et al., eds),MIT Press30 Harvey, I. et al. (1994) Seeing the light: artificialevolution, real vision. In From Animals toAnimats 3: Proc. 3rd Int. Conf. Simulation Adapt.Behav. (Cliff, D. et al., eds), MIT Press31 Lichtensteiger, L. and Eggenberger, P. (1999)Evolving the morphology of a compound eye on arobot. In Proc. 3rd Eur. Workshop Adv. 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(in press)39 Hinton, G.E. and Nowlan, S.J. (1987) How learningguides evolution. Complex Syst. 1, 495–50240 Nolfi, S. et al. (1994) Learning and evolution inneural networks. Adapt. Behav. 3, 5–2841 Nolfi, S. (1999) How learning and evolution interact:the case of a learning task which differs from theevolutionary task. Adapt. Behav. 7, 231–23642 Husbands, P. et al. (1999) Better living throughchemistry: evolving GasNets for robot control.Connection Sci. 10 (3–4), 185–21043 Nolfi, S. and Floreano, D. (1998) Co-evolvingpredator and prey robots: do ‘arm races’arise inartificial evolution? Artif. Life 4, 311–335TRENDS in Cognitive Sciences Vol.6 No.1 January 2002http://tics.trends.com 1364-6613/02/$ – see front matter © 2002 Elsevier Science Ltd. All rights reserved. PII: S1364-6613(00)01816-737Reviewidentified that are particularly important intransmitting speech, and these tend to emphasizethe temporal properties of speech sounds.Speech contains a large variety of complex soundsof varying temporal grain, including periodic andaperiodic


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