Journal of Deaf Studies and Deaf Education Advance Access originally published online on November 30, 2005
The Journal of Deaf Studies and Deaf Education 2006 11(2):144-152; doi:10.1093/deafed/enj017
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Empirical Articles |
Neural Correlates for Numerical Processing in the Manual Mode
Kyoto University The Japan Science and Technology Agency
National Center for Neurology and Psychiatry
Correspondence should be sent to Nobuo Masataka, Primate Research Institute, Kyoto University, Inuyama, Aichi 484-8506, Japan (e-mail: masataka{at}pri.kyoto-u.ac.jp).
Received July 5, 2005; revised October 21, 2005; accepted October 24, 2005
This paper reports a study designed to examine the neuronal correlates for comprehending the signs of American Sign Language representing numerals in deaf signers who acquired Japanese Sign Language as their first language. The participants were scanned by functional magnetic resonance imaging (fMRI) twice on the day of the experiment. The results of the measurements revealed that upon learning that the signs actually have numeric meaning, a network of brain areas is activated immediately. Many of these areas have been previously implicated in numerical processing. The similar neural network of brain regions responsible for numerical processing exists on a nonlinguistical basis and works to retrieve arithmetic facts from presented linguistic material regardless of the mode of the language.
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Introduction |
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Digits and number words are considered a very recent cultural invention in the evolution of the human species. Indeed, they arise from the specifically human and evolutionarily recent ability to create and mentally manipulate complex symbols. The sense of numerosity, however, is older. Many animals are sensitive to numerical regularities in their environments, can represent these regularities internally, and can perform elementary and approximate computations with numerical quantities (Gallistel, 1990
; McComb, Packer, & Pusey, 1994; Suzuki & Kobayashi, 2000). Similar abilities are found in human infants in their first year of life, well before they begin to produce language (e.g., Xu & Spelke, 2000). These converging lines of evidence suggest the existence in the animal and human brain of specialized neural systems for processing numbers on a nonlinguistical basis (Kobayashi, Hiraki, Mugitani, & Hasegawa, 2004).
Substantial experimental evidence points to the notion that such domain-specific mechanism, or a core "number sense," accounts for our uniquely human talent for formal mathematics. Several lines of evidence from studies of numerical competence in normal adults, infants, and young children, who are all hearing, as well as in nonhuman animals has led many researchers to conclude that the domain-specific system of knowledge, present in many species, is responsible for the sense of number and forms the basis for the complex symbolic manipulation of number developed by humans (Dehaene, 1997; Gallistel & Gelman, 1992).
Particularly, the findings reported by Barth, Kanwisher, and Spelke (2003), Dehaene, Dupoux, and Mehler (1990), and Xu and Spelke (2000) should be noticeable, in which many tasks that deal explicitly with exact symbolic numerosities automatically activate nonsymbolic number representations. More recently, Barth et al. (in press) investigated this issue more directly in examining whether hearing adults and hearing preschool children can perform simple arithmetic calculations on nonsymbolic numerosities. Results of the five experiments they conducted clearly reveal that both the adults and the children with no training in arithmetic successfully performed approximate arithmetic on large sets of elements. Success at these tasks did not depend on nonnumerical continuous quantities, modality-specific quantity information, or the adoption of alternative nonarithmetic knowledge. Thus, they concluded that abstract numerical quantity representations are computationally functioned and may provide a foundation for formal mathematics.
Such reasoning is also quite consistent with a recent neuronal model for the implementation of elementary numerical abilities proposed based on the findings from a series of brain-imaging experiments (see Piazza & Dehaene, 2005, for a review). Piazza and Dehaene (2005) claim that mathematical ability results from the integration of two nonnumerical neural circuits in the brain: the left frontal lobe, which controls linguistic representations of exact numerical values, and the parietal lobes, which control visuospatial representations of approximate quantities. According to their view, humans have at least two means of representing and processing quantity. One is the ability to make perceptually based judgments and comparisons, in which the degree of accuracy varies with set size. The other allows precise quantification through the use of symbols, concepts, and rules. Arithmetical tasks that require exact numerical answers depend on verbal representations of numbers, whereas tasks requiring estimation or approximation depend on nonlinguistic representations of approximate quantities.
In all, it appears that a region of parietal cortex underlies an abstract-semantic number sense and a region of left prefrontal cortex underlies more specific operations mediating exact or approximate calculation. The notion is compelling and provocative. Indeed, there is a growing literature that presents neuronal evidence for the explanation given by Dehaene and his colleagues (see Piazza & Dehaene, 2005, for review). However, it should be noted that all such previous studies about numerical processing, whether cognitive ones or neurological ones, have pursued this issue in hearing subjects who had acquired a spoken language as their first language by presenting digits or number words in written form. None of these studies have worked with deaf subjects who had acquired a signed language as their first language though naturally evolved signed languages, even though such subjects are known to possess identical levels of linguistic organization, including phonology, morphology, syntax, and semantics (Klima & Bellugi, 1979; Padden, 1988). In fact, recent investigations into languages have provided a powerful research opportunity for exploring the neural basis of the human brain that works in conjunction in both the manual and vocal modes for the purpose of language organization (Emmorey, 2002; Masataka, 2003).
Having extended such reasoning into the research field of numerical processing, recently, Masataka (in press) investigated the capacity for nonsymbolic arithmetic performance in deaf adults who acquired Japanese Sign Language (JSL) as their first language as well as in hearing adults. In the study, the participants performed the numerical subtraction task on large sets of elements, presented as visual arrays of dots, for the testing of the capacity for nonsymbolic arithmetic performance. For the task, the participants were presented with three visual arrays of dots and were asked to subtract the second array from the first and to compare this difference to the number of elements in the third array (e.g., "56 – 16 = (40) vs. 35). The results revealed that they could perform simple arithmetic subtraction on nonsymbolic numerosities. Their performance levels were even higher than those of hearing adults who participated in the experiment as a control group.
Based on these findings, therefore, we attempted to conduct neuropsychological research as a next step. Namely, we hypothesized that the neural network of brain areas for numerical processing would also exist on a nonlinguistical basis in the deaf adults who participated in the experiment of Masataka (in press) and that it should function normally for the retrieving of arithmetic facts from presented stimuli independent of any modality difference in the language by which the presented stimuli are coded. In order to test this here, we have undertaken the present experiment with the participants by presenting signs of American Sign Language (ASL) representing numerals. Before the experiment, participants were totally naive to ASL. During the experiment we compared brain activation in the participants both before and after learning the coded representations in the presented ASL signs with the use of functional magnetic resonance imaging (fMRI). Once these representations are learned, a change in brain activation is thought to accompany the transcoding of the numerals. We hypothesized that the network should extensively share the brain regions that have been previously been implicated in the numerical processing studies by Dehaene and his colleagues (Dehaene, 1997; Dehaene et al., 1999).
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Participants
Participants were 13 right-handed profoundly deaf adults (8 males and 5 females) varying in age between 19 and 50, and their parents were all hearing. They were all involved in the study by Masataka (in press)
. Though they had acquired JSL as their first language, the deaf participants were first exposed to JSL at the age of 3 years when they were first diagnosed to be profoundly deaf. Thereafter, they started to attend the educational program by JSL every weekday with their parents. Until then, their parents were not knowledgeable about any form of signed language. The participants learned Japanese in written form through an official elementary education, which started when they were 6–7 years old. All the participants in the group of hearing adults had normal hearing. They spoke Japanese as their first language and had never been exposed to any form of signed language before. Before this study, the participants were totally naive to ASL or Finger Alphabet.
Overall Design
Neuroimaging experiments are becoming easier to carry out with the increasing availability of fMRI scanners. Preprocessing steps are being streamlined and automated, enabling data to be processed faster and more easily. Using fMRI to visualize function in vivo, neuroscientists have demonstrated that the mental operations conducted by the human brain can be empirically measured. Typically, epoch or block experimental designs have been the workhorse of such experimentation. In these designs, stimuli are presented for a period of seconds and alternated randomly or pseudorandomly over the course of the data acquisition period. Usually two different groups of stimuli are prepared: one as task stimuli and the other as control stimuli. The blood-oxygen-level-dependent (BOLD) signals are to be compared between the two stimulus conditions so that we could evaluate the effects of some characteristics provided with the task stimuli, which are not present in the control stimuli. So far, we have undertaken several neuroimaging studies with this experimental paradigm (Masataka, Ohnishi, Imabayashi, Hirakata, & Matsuda, 2005; Ohnishi et al., 2001, 2004), and the present experiment was also undertaken with the same design.
In the present experiment, testing with fMRI was undertaken twice in a single day with the presentation of the same set of stimuli to the same group of participants. In each testing, strings of four to six ASL signs, each of which represented a number between 1,000 and 4,000, were presented on a monitor as task stimuli. For control stimuli, a sign was randomly chosen from the repertoire of the Finger Alphabet described by Shioda (1985) as a counterpart to each ASL sign used, and strings of such signs were presented. The finger movements were close to those of spelling with the index finger of some capital letters of the alphabet, such as X and Q.
The participants' cerebral activation in response to the ASL signs was measured by subtracting the activation level of BOLD signals recorded when the control stimuli were presented from that recorded when the task stimuli were presented in each testing. After the first testing, the participants were instructed about the meaning of each of the ASL signs used in the experiment. However, they remained naive to Finger Alphabet. Thereafter, they received the second testing. Whereas, in the first testing, not only the control stimuli but also the task stimuli were perceived as meaningless by the participants, the task stimuli became meaningful in the second testing. By comparing the cerebral activation between the first and the second testing, we attempted to examine its practice-related changes if any.
Procedure
Each of the participants received their first testing with fMRI measurement, in which they were instructed to simply "Recognize the meaning of the presented stimulus, which has been chosen from a foreign signed language system." When this testing was finished, the participants' performance with regard to comprehension of the stimuli was scored without the fMRI measurement. In the scoring of performance, each participant was presented with the same set of combinations of signs as those used in the testing with the fMRI measurement. The participants were instructed to write an Arabic number represented by each combination of signs consecutively presented on the monitor of a personal computer at 3-s intervals. Thereafter, the participants were taught about the meaning of each ASL sign used in the testing. They received "exercises" to decode the meaning by producing a JSL sign of the same meaning. In a given such practice trial, a give single ASL sign was presented, and the practice continued until the participants made "correct answers" in 10 consecutive trials. Actually it took approximately 20 min on the average. Upon completion of the exercises, testing with the fMRI measurement was conducted again (second testing). As soon as the second testing was finished, their performance with regard to comprehension of the stimuli was scored again without the fMRI measurement according to the same protocol as that used after the first testing.
fMRI Analysis
Measurement of cerebral activation was conducted using BOLD contrast with a 1.5 T MAGNETOM Vision plus MR scanner (Siemens, Erlangen, Germany) using a standard head coil. After automatic shimming, a time course series of 110 volumes was obtained using single-shot gradient-refocused echo-planar imaging (TR = 4,000 ms, TE = 60 s, flip angle = 90 degrees, in-plane resolution = 3.44 x 3.44 mm, FOV = 22 cm, and contiguous 4-mm slices to cover the entire brain). Head motion was minimized by placing tight but comfortable foam padding around the participant's head. The fMRI protocol was a block design with epochs in which either the task stimuli or the control stimuli were presented. Each epoch lasted 20 s (equivalent to five whole-brain fMRI volume acquisitions). The stimuli were presented using Presentation (neurobehavioral systems) running on a PC and back-projected onto a screen located approximately 50 cm from the subject's head using a 65536-color liquid crystal display and an overhead projector. Participants viewed the screen through a mirror attached to the head coil. The first five volumes of each fMRI scan were discarded because of the nonsteady condition of the magnetization, and the remaining 70 volumes were used for analysis.
Data were analyzed with Statistical Parametric mapping software (SPM99, 1999). Scans were realigned and spatially normalized to the standard stereotactic space of Talairach using an EPI template (Talairach & Tournoux, 1988). The parameter for affine and quadratic transformation to the EPI template that was already fitted to Talairach space was estimated by least squares means. Data were then smoothed in a spatial domain (full width at half maximum = 8 x 8 x 8 mm) to improve the signal-to-noise ratio. After specifying the appropriate design matrix with a delayed box-car function as a reference waveform, condition, slow homodynamic fluctuation (unrelated to the task), and subject effects were estimated according to the general linear model and taking temporal smoothness into account. Global normalization was performed using proportional scaling. To test the hypotheses about regionally specific condition effects, the estimates were compared by means of linear contrasts between each control and task period. The resulting set of voxel values for each contrast constituted a statistical parametric map of the t statistic SPM {t}. To account for interindividual variance, all group analyses were computed using a random-effects model. Further, group analyses across participants involved a one-sample t test on the images generated by pooling over the session of individual contrasts of activation versus control for each participant. In order to evaluate the learning of ASL signs, effects at the first and second testing were analyzed by a paired t test. For these group analyses, we set p < .001 without a correction for multiple comparisons in order to avoid Type II error, and this was followed by applying small volume correction (p < .01) to each cluster to avoid Type I error. The resulting sets of t values constituted the statistical parametric maps {SPM (t)}. Anatomic localization was identified using both MNI coordinates and Talairach coordinates obtained from M. Brett's transformations (Brett, Johnsrude, & Owen, 2002) and were presented as Talairach coordinates (Talairach & Tournoux, 1988).
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Results |
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In the first testing, clusters of activated voxels were identified only in the left visual association areas (Table 1 and Figure 1). Once the participants had been instructed on how to comprehend presented ASL signs, however, the areas of activation became widespread dramatically. The regions included the bilateral prefrontal cortex, the left premotor area, the bilateral parietal lobules, and the left middle temporal gyrus as well as the visual association areas.
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The areas of the brain where significant increases in BOLD activation levels from the first to second testing were observed included the left prefrontal cortex, the bilateral parietal lobules, and the left middle temporal gyrus (Table 2 and Figure 2), but no decrease was found in any brain region. Results of the scoring with regard to performance on both the ASL number and Finger Alphabet tasks, which were conducted after the first and second fMRI measurements, revealed that the percentage of correct responses was 0% for all participants in both scoring sessions and that no participant could answer correctly in response to any stimulus. So, activation changed to what were now seen as linguistic stimuli, even though participants could not respond correctly to them.
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Discussion |
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Even at the onset of the experiment, the ASL signs were perceived differently from the signs of the Finger Alphabet, and the activation of the visual association area was greater in response to the ASL signs than to the signs of the Finger Alphabet. This might be mostly due to the fact that ASL is a naturally evolving signed language system, whereas the Finger Alphabet used here was developed for the subsidiary means of promoting the oral education of deaf children (Shioda, 1985
). Given the fact that the participants had been exposed to JSL after birth, they could have exhibited a perceptual preference for the ASL stimuli over the signs of the Finger Alphabet due to their previous experience with another naturally evolving signed language system. On the other hand, as has been suggested previously (Fernald & Simon, 1984; Masataka, 2000, 2003), the preference for naturally evolving signed languages could also be due to genetic programing.
Nonetheless, the much more noticeable implication of the present results should be the immediate effects of teaching the participants about ASL signs representing numerals. Significant changes in BOLD activation caused by the learning included increases in the activation level of the left frontal lobe and the bilateral parietal lobes as well as in the left middle temporal gyrus. The temporal gyrus becomes active when picking out the forms of motion of biological entities from other types of motion in the natural environment (Blakemore & Decety, 2001; Frith, 2001). Therefore, the participants should have come to view the ASL signs as meaningful and distinguishable from the control stimuli. Moreover, the frontal and parietal regions have been reported to be regions that participate in numerical processing (Dehaene, Spelke, Pinel, Stanescus, & Tsivkin, 1999; Piazza & Dehaene, 2005). Obviously, upon learning that the presented signing actions actually have numeric meaning, a network of brain areas that has been previously implicated in numerical processing could immediately become activated. Although behavioral measures were not recorded during the scanning in this experiment, the participants certainly could not decode the numerical meaning of the presented stimuli even after the teaching in spite of the fact that the exact same stimuli were presented repeatedly on the day of the experiment.
Similar findings have recently been reported by Masataka et al. (2005), who conducted a study designed to examine the neuronal correlates of reading Roman numerals and the changes that occur with extensive practice on that task. Hearing participants were scanned by fMRI three times on the first day of the experiment and one last time following 2–3 months of practice on the task, allowing the comparison of brain activations with varying levels of practice given on the same day and across 2–3 months of training. The results of the fMRI measurements revealed that upon learning that these alphabetical symbols actually have numeric meaning, a network of brain areas, many of which have been previously implicated in numerical processing, can be immediately activated. This can occur even though the participants are not yet able to decode the meaning of the presented stimuli at all. Further, although the participants became much more skilled at the decoding after subsequent intensive practice, varying levels of such practice did not affect the pattern of the subsequent activation. Taken together with the results of the study of Masataka, the present findings also confirm the fact that the neural network of brain regions responsible for numerical processing is activated once participants start processing the presented stimuli numerically regardless of whether their attempts are successful.
In all, the network exists on a nonlinguistical basis and functions for the retrieval of arithmetic facts from presented linguistic material regardless of the mode of the language, that is, a region of parietal cortex underlies an abstract-semantic number sense, and a region of left prefrontal cortex underlies more specific operations mediating exact or approximate calculation. Particularly, the fact that linguistic representations of exact numerical values are controlled in the brain's left hemisphere even in native signers should be intriguing. It is well known that spoken language is represented in the brain's left hemisphere. However, much is mysterious about whether the brain sites involved in language processing are determined exclusively by the mechanisms for speaking and hearing, or whether they also involve tissue dedicated to aspects of the patterning of natural language.
Actually, in order to investigate the problem, the existence of naturally evolved signed languages of deaf people has provided a powerful research opportunity because they possess identical levels of linguistic organizations with spoken languages (Klima & Bellugi, 1979). To date, however, available evidence has provoked controversy (Corina, Bavelier, & Neville, 1998; Hickok, Bellugi, & Klima, 1998a). Pioneering lesion studies of brain-damaged deaf adults (Bellugi, Poizner, & Klima, 1989; Hickok, Bellugi, & Klima, 1998b) have shown that deaf signers suffer aphasic symptoms in signed language following left-hemisphere lesions that are similar to those seen in Broca's and Wernicke's aphasia in hearing patients. Because lasting deficits to signed language processing were not observed after lesions to the right hemisphere, they concluded that the contribution of the right hemisphere may not be central to the processing of natural signed language. Nevertheless, findings from brain-imaging studies, which have been conducted more recently, neither fully concur with this view nor are they consistent across studies. In a large study of Neville and colleagues with the use of fMRI, neural activity was investigated while Deaf and hearing participants processed sentences in ASL and written English (Bavelier et al., 1998; Neville et al., 1998). As the English stimuli, written sentences were presented in 30-s blocks, which alternated with 30-s blocks of consonant strings. As the ASL stimuli, ASL sentences were presented, which alternated with strings of nonsign gestures. At the end of each run, participants were required to decide whether or not specific sentences and nonsense strings had been presented.
When statistical analysis of the MR signal determined which areas of the brain were more active during the language processing blocks, compared to the blocks with nonsense stimuli, elevated activation was found within left-hemisphere structures that are classically linked to language processing for both hearing and deaf native ASL signers. These same areas were also found to be active when English sentences were read by native speakers. Moreover, comparable increase in neural activation was identified in the equivalent areas within the right hemisphere of both deaf and hearing signers. On the other hand, the present results present evidence against such contribution of the right hemisphere with respect to language representation of exact numerical values in the numerical processing at the manual mode by deaf adults.
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Acknowledgments |
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This work was partly supported by a Health Science Research Grant from the Ministry of Health, Labor and Welfare (H13-005), Center of Research for Excellent Study and Technology (CREST), Japan Science and Technology Agency, and 21st Century Grant-in-Aid Research for Center of Excellence (COE), Kyoto University. Open Access charges for this paper were provided by CREST and the 21st Grant for the COE.
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Barth, H., Kanwisher, N., & Spelke, E. (2003). The construction of large number representations in adults. Cognition, 86, 201–221.[CrossRef][Web of Science][Medline]
Barth, H., LaMonto, K., Lipton, J., Dehaene, S., Kanwisher, N., & Spelke, E. (in press). Non-symbolic arithmetic in adults and young children. Cognition.
Bavelier, D., Corina, D., Jezzard, P., Clark, V., Karni, A., Lalwani, A., et al. (1998). Hemispheric specialization for English and ASL: Left invariance-right variability. Neuroreport, 9, 1537–1542.[Web of Science][Medline]
Bellugi, U., Poizner, H., & Klima, E. S. (1989). Language, modality and the brain. Trends in Neurosciences, 12, 380–388.[CrossRef][Web of Science][Medline]
Blakemore, S.-J., & Decety, J. (2001). From the perception of action to the understanding of intention. Nature Reviews Neuroscience, 2, 561–567.[CrossRef][Web of Science][Medline]
Brett, M., Johnsrude, I.S., & Owen, A.M. (2002). The problem of functional localization in the human brain. Nature Reviews Neuroscience, 3, 243–249.[CrossRef][Web of Science][Medline]
Corina, D. P., Bavelier, D., & Neville, H. J. (1998). Response. Trends in Cognitive Sciences, 2, 468–470.
Dehaene, S. (1997). The number sense. Oxford, UK: Oxford University Press.
Dehaene, S., Dupoux, E., & Mehler, J. (1990). Is numerical comparison digital? Analogical and symbolic effects in two-digit number comparison. Journal of Experimental psychology: Human Perception and Performance, 16, 626–641.
Dehaene, S., Spelke, E., Pinel, P., Stanescus, R., & Tsivkin, S. (1999). Sources of mathematical thinking: Behavioral and brain-imaging evidence. Science, 284, 970–974.
Emmorey, K. (2002). Language, cognition and the brain: Insights from sign language research. Mahwah, NJ: Erlbaum.
Fernald, A., & Simon, T. (1984). Expanded intonation contours in mother's speech to newborns. Developmental Psychology, 20, 104–113.[CrossRef]
Frith, U. (2001). Mind blindness and the brain in autism. Neuron, 32, 969–979.[CrossRef][Web of Science][Medline]
Gallistel, C. R. (1990). The organization of learning. Cambridge, MA: MIT Press.
Gallistel, C. R., & Gelman, R. (1992). Preverbal and verbal counting and computation. Cognition, 44, 43–74.[CrossRef][Web of Science][Medline]
Hickok, G., Bellugi, U., & Klima, E. (1998a). What's right about the neural organization of sign language? A perspective on recent neuroimaging results. Trends in Cognitive Sciences, 2, 465–468.[CrossRef]
Hickok, G., Bellugi, U., & Klima, E. (1998b). The neural organization of language: Evidence from sign language aphasia. Trends in Cognitive Sciences, 2, 129–136.
Kobayashi, T., Hiraki, K., Mugitani, R., & Hasegawa, T. (2004). Baby arithmetic: One object plus one tone. Cognition, 91, B23–B34.[CrossRef][Web of Science][Medline]
Klima, E. S., & Bellugi, U. (1979). The signs of language. Cambridge, MA: Harvard University Press.
Masataka, N. (2000). The role of modality and input in the earliest stage of language acquisition: Studies of Japanese Sign Language. In C. Chamberlain, J. P. Morford, & R. I. Mayberry (Eds.), Language acquisition by eye (pp. 3–24). Hillsdale, NJ: Erlbaum.
Masataka, N. (2003). The onset of language. Cambridge: Cambridge University Press.
Masataka, N. (in press). Differences in arithmetic subtraction of non-symbolic numerosities by deaf and hearing adults. Journal of Deaf Studies and Deaf Education.
Masataka, N., Ohnishi, T., Imabayashi, E., Hirakata, M., & Matsuda, H. (2005). Neural correlates for learning to read Roman numerals. Manuscript in preparation.
McComb, K., Packer, C., & Pusey, A. (1994). Roaring and numerical assessment in contests between groups of female lions, Panthara leo. Animal Behaviour, 47, 379–387.[CrossRef]
Neville, H. J., Bavelier, D., Corina, D., Rauschecker, J., Karni, A., Lalwani, A., et al. (1998). Cerebral organization for language in deaf and hearing subjects: Biological constraints and effects of experience. Proceedings of the National Academy of Sciences United States of America, 95, 922–929.
Ohnishi, T., Matsuda, H., Asada, T, Aruga, M., Hirakata, M., Hishikawa, M., et al. (2001). Functional anatomy of musical perception in musicians. Cerebral Cortex, 11, 754–760.
Ohnishi, T., Moriguchi, Y., Matsuda, H., Mori, T., Hirakata, M., Imabayashi, E., et al. (2004). The neural network for the mirror system and the ‘theory of mind’ in normally developed children: An fMRI Study. Neuroreport, 15, 1483–1488.[CrossRef][Web of Science][Medline]
Padden, C. A. (1988). Grammatical theory and signed languages. In F. Newmayer (Ed.), Linguistics: The Cambridge survey (pp. 250–266). Cambridge: Cambridge University Press.
Piazza, M., & Dehaene, S. (2005). From number neurons to mental arithmetic: The cognitive neuroscience of number sense. In M. S. Gazzaniga (Ed.), The cognitive neourosciences (3rd ed., pp. 865–875). Cambridge, MA: MIT Press.
Shioda, H. (1985). Sekai no shuwa [Signed languages in the world]. Tokyo: Sanseido.
SPM99. (1999). London: Wellcome Department of Cognitive Neurology. Retrieved November 21, 2005 from http://www.fil.ion.ucl.ac.uk/spm.
Suzuki, K., & Kobayashi, T. (2000). Numerical competence in rats (Rattus norvegicus): Davis and Bradford (1986) extended. Journal of Comparative Psychology, 114, 43–85.
Talairach, J., & Tournoux, P. (1988). Co-planar streotaxic atlas of the human brain. Stuttgart: Thieme.
Xu, F., & Spelke, E. (2000). Large number discrimination I 6-month-old infants. Cognition, 74, B1–B11.[CrossRef][Web of Science][Medline]
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