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A perceptually grounded model of the singular–plural distinction

Published online by Cambridge University Press:  13 May 2014

HAYDEN WALLES ,
ANTHONY ROBINS  and
ALISTAIR KNOTT
HAYDEN WALLES
Affiliation:
Department of Computer Science, University of Otago, New Zealand
ANTHONY ROBINS
Affiliation:
Department of Computer Science, University of Otago, New Zealand
ALISTAIR KNOTT
Affiliation:
Department of Computer Science, University of Otago, New Zealand
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Abstract

Embodied theories of language posit that the human brain’s adaptations for language exploit pre-existing perceptual and motor mechanisms for interacting with the world. In this paper we propose an embodied account of the linguistic distinction between singular and plural, encoded in the system of grammatical number in many of the world’s languages. We introduce a neural network model of visual object classification and spatial attention, informed by a collection of findings in psychology and neuroscience. The classification component of the model computes the type associated with a visual stimulus without identifying the number of objects present. The distinction between singular and plural is made by a separate mechanism in the attentional system, which directs the classifier towards the local or global features of the stimulus. The classifier can directly deliver the semantics of uninflected concrete noun stems, while the attentional mechanism can directly deliver the semantics of singular and plural number features.

Keywords

grammatical numbervisual attentionobject classificationglobal precedence
Type
Research Article
Information
Language and Cognition , Volume 6 , Issue 3 , September 2014 , pp. 327 - 369
Copyright
Copyright © UK Cognitive Linguistics Association, 2014 

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References

references

Ansari, D., Lyons, I., van Eimeren, L., & Xu, F. (2007). Linking visual attention and number processing in the brain: the role of the temporo-parietal junction in small and large symbolic and nonsymbolic number comparison. Journal of Cognitive Neuroscience, 19(11), 1845–1853.CrossRefGoogle ScholarPubMed
Barner, D., Wood, J., Hauser, M., & Carey, S. (2008). Evidence for a non-linguistic distinction between singular and plural sets in rhesus monkeys. Cognition, 107, 603–622.CrossRefGoogle ScholarPubMed
Barsalou, L. (2008). Grounded cognition. Annual Review of Psychology, 59, 617–645.CrossRefGoogle ScholarPubMed
Barth, H., Kanwisher, N., & Spelke, E. (2003). The construction of large number representations in adults. Cognition, 86, 201–221.CrossRefGoogle ScholarPubMed
Barwise, J., & Cooper, R. (1981). Generalized quantifiers and natural language. Linguistics and Philosophy, 4(2), 159–219.CrossRefGoogle Scholar
Baylis, G., Driver, J., & Rafal, R. (1993). Visual extinction and stimulus repetition. Journal of Cognitive Neuroscience, 5(4), 453–466.CrossRefGoogle ScholarPubMed
Bergen, B., & Chang, N. (2005). Embodied construction grammar in simulation-based language understanding. In Östman, J. O. & Fried, M. (Eds.), Construction grammar(s): cognitive and cross-language dimensions (pp. 147–190). Amsterdam: John Benjamins. [Reprinted in V. Evans, B. Bergen, & J. Zinken (Eds.), The cognitive linguistics reader. Equinox. 2007.]CrossRefGoogle Scholar
Cantlon, J., Brannon, E., Carter, E., & Pelphrey, K. (2006). Functional imaging of numerical processing in adults and 4-y-old children. PLoS Biology, 4(5), 844–854.CrossRefGoogle ScholarPubMed
Chierchia, G. (1998). Plurality of mass nouns and the notion of ‘semantic parameter’. In Rothstein, S. (Ed.), Events and grammar (pp. 53–103). Dordrecht: Kluwer.CrossRefGoogle Scholar
Connolly, A., Guntupalli, S., Gors, J., Hanke, M., Halchenko, Y., Wu, Y.-C., Abdi, H., & Haxby, J. (2012). The representation of biological classes in the human brain. Journal of Neuroscience, 32(8), 2608–2616.CrossRefGoogle ScholarPubMed
Conway, B., Moeller, S., & Tsao, D. (2007). Specialized color modules in macaque extrastriate cortex. Neuron, 56, 560–573.CrossRefGoogle ScholarPubMed
Corbett, G. (2000). Number. Cambridge/New York: Cambridge University Press.CrossRefGoogle Scholar
Damasio, H., Grabowski, T., Tranel, D., Hichwa, R., & Damasio, A. (1996). A neural basis for lexical retrieval. Nature, 380, 499–505.CrossRefGoogle ScholarPubMed
Domahs, F., Nagels, A., Domahs, U., Whitney, C., Wiese, R., & Kircher, T. (2012). Where the mass counts: common cortical activation for different kinds of nonsingularity. Journal of Cognitive Neuroscience, 24(4), 915–932.CrossRefGoogle ScholarPubMed
Feigenson, L., Carey, S., & Hauser, M. (2002). The representations underlying infants’ choice of more: object-files versus analog magnitudes. Psychological Science, 13, 150–156.CrossRefGoogle ScholarPubMed
Feigenson, L., Dehaene, S., & Spelke, E. (2004). Core systems of number. Trends in Cognitive Sciences, 8(7), 307–314.CrossRefGoogle ScholarPubMed
Feldman, J., & Narayanan, S. (2004). Embodiment in a neural theory of language. Brain and Language, 89(2), 385–392.CrossRefGoogle Scholar
Fias, W., & Fischer, M. (2005). Spatial representation of numbers. In Campbell, J. (Ed.), Handbook of mathematical cognition (pp. 43–54). New York: Psychology Press.Google Scholar
Fink, G., Halligan, P., Marshall, J., Frith, C., Frackowiak, R., & Dolan, R. (1996). Where in the brain does visual attention select the forest and the trees. Nature, 382, 626–628.CrossRefGoogle ScholarPubMed
Flevaris, A., Bentin, S., & Robertson, L. (2010). Local or global? Attentional selection of spatial frequencies binds shapes to hierarchical levels. Psychological Science, 21(3), 424–431.CrossRefGoogle ScholarPubMed
Flevaris, A., Bentin, S., & Robertson, L. (2011). Attention to hierarchical level influences attentional selection of spatial scale. Journal of Experimental Psychology: Human Perception and Performance, 37(1), 12–22.Google ScholarPubMed
Gonzalez, R. C., & Woods, R. C. (1992). Digital image processing. Reading, MA: Addison-Wesley.Google Scholar
Gottlieb, J., Kusunoki, M., & Goldberg, M. (1998). The representation of visual salience in monkey parietal cortex. Nature, 391, 481–484.CrossRefGoogle ScholarPubMed
Heinke, D., & Humphreys, G. (2003). Attention, spatial representation, and visual neglect: simulating emergent attention and spatial memory in the selective attention for identification model (SAIM). Psychological Review, 110(1), 29–87.CrossRefGoogle ScholarPubMed
Hughes, H. C., George, N., & Kitterle, F. (1996). Global precedence, spatial frequency channels and the statistics of natural images. Journal of Cognitive Neuroscience, 8(3), 197–230.CrossRefGoogle ScholarPubMed
Hurford, J. (2001). Languages treat 1−4 specially. Mind and Language, 16, 69–75.CrossRefGoogle Scholar
Hurford, J. (2003). The interaction between numerals and nouns. In Plank, F. (Ed.), Noun phrase structure in the languages of Europe (pp. 561–620). Berlin: de Gruyter.Google Scholar
Itti, L., & Koch, C. (2000). A saliency-based search mechanism for overt and covert shifts of visual attention. Vision Research, 40, 1489–1506.CrossRefGoogle ScholarPubMed
Izard, V., Dehaene-Lambertz, G., & Dehaene, S. (2008). Distinct cerebral pathways for object identity and number in human infants. PLoS Biology, 6(2), 275–285.CrossRefGoogle ScholarPubMed
Just, M., Cherkassky, V., Aryal, S., & Mitchell, T. (2010). A neurosemantic theory of concrete noun representation based on the underlying brain codes. PLoS One, 5(1), e8622.CrossRefGoogle ScholarPubMed
Kadir, T., & Brady, M. (2001). Saliency, scale and image description. International Journal of Computer Vision, 45(2), 83–105.CrossRefGoogle Scholar
Kadir, T., Hobson, P., & Brady, M. (2005). From salient features to scene description. In Workshop on Image Analysis for Multimedia Interactive Services.Google Scholar
Kamp, H., & Reyle, U. (1993). From discourse to logic. Dordrecht: Kluwer Academic Publishers.Google Scholar
Kanwisher, N. (1991). Repetition blindness and illusory conjunctions: errors in binding visual types with visual tokens. Journal of Experimental Psychology: Human Perception and Performance, 17, 414–421.Google ScholarPubMed
Koch, C., & Ullman, S. (1985). Shifts in selective visual attention: towards the underlying neural circuitry. Human Neurobiology, 4, 219–375.Google ScholarPubMed
Kreiman, G., Koch, C., & Fried, I. (2000). Category-specific visual responses of single neurons in the human medial temporal lobe. Nature Neuroscience, 3(9), 946–953.CrossRefGoogle ScholarPubMed
Kriegeskorte, N., Mur, M., Ruff, D., Kiani, R., Bodurka, J., Esteky, H., Tanaka, K., & Bandettini, P. (2008). Matching categorical object representations in inferior temporal cortex of man and monkey. Neuron, 60, 1120–1141.CrossRefGoogle ScholarPubMed
Lakoff, G. (1987). Women, fire and dangerous things. Chicago/London: University of Chicago Press.CrossRefGoogle Scholar
Lakoff, G., & Johnson, M. (1980). Metaphors we live by. Chicago/London: University of Chicago Press.Google Scholar
Liu, X., & Wang, D. (2000). Texture classification using spectral histograms. Technical Report TR17, Department of Computer and Information Science, Ohio State University, Columbus, OH 43210–1277.Google Scholar
Logothetis, N. K., & Sheinberg, D. L. (1996). Visual object recognition. Annual Review of Neuroscience, 19, 577–621.CrossRefGoogle ScholarPubMed
Martin, A. (2007). The representation of object concepts in the brain. Annual Review of Psychology, 58, 25–45.CrossRefGoogle ScholarPubMed
Moore, T., & Armstrong, K. M. (2003). Selective gating of visual signals by microstimulation of frontal cortex. Nature, 421, 370–373.CrossRefGoogle ScholarPubMed
Mozer, M. C., & Baldwin, D. S. (2008). Experience-guided search: a theory of attentional control. In Platt, J., Koller, D., & Singer, Y. (Eds.), Advances in neural information processing 20 (pp. 1033–1040). Cambridge, MA: MIT Press.Google Scholar
Mozer, M. C., & Sitton, M. (1998). Computational modeling of spatial attention. In Pashler, H. E. (Ed.), Attention (pp. 341–393). Hove: Psychology Press.Google Scholar
Navalpakkam, V., & Itti, L. (2005). Modeling the influence of task on attention. Vision Research, 45(2), 205–231.CrossRefGoogle ScholarPubMed
Navon, D. (1977). Forest before trees: the precedence of global features in visual perception. Cognitive Psychology, 9, 353–383.CrossRefGoogle Scholar
Nieder, A., & Miller, E. K. (2004). A parieto-frontal network for visual numerical information in the monkey. Proceedings of the National Academy of Sciences, 101(19), 7457–7462.CrossRefGoogle Scholar
Olshausen, B., Anderson, C., & van Essen, D. (1993). A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information. Journal of Neuroscience, 13(11), 4700–4719.CrossRefGoogle ScholarPubMed
Palomares, M., & Egeth, H. (2010). How element visibility affects visual enumeration. Vision Research, 50, 2000–2007.CrossRefGoogle ScholarPubMed
Peggy Li, P., Barner, D., Ogura, T., Yang, S., & Carey, S. (2009). Does the conceptual distinction between singular and plural sets depend on language? Developmental Psychology, 45(6), 1644–1653.Google Scholar
Piazza, M., Izard, V., Pinel, P., Le Bihan, D., & Dehaene, S. (2004). Tuning curves for approximate numerosity in the human intraparietal sulcus. Neuron, 44, 547–555.CrossRefGoogle ScholarPubMed
Piazza, M., Mechelli, A., Price, C., & Butterworth, B. (2006). Exact and approximate judgements of visual and auditory numerosity: an fmri study. Brain Research, 1106, 177–188.CrossRefGoogle ScholarPubMed
Polyn, S., Natu, V., Cohen, J., & Norman, K. (2005). Category-specific cortical activity precedes retrieval during memory search. Science, 310, 1963–1966.CrossRefGoogle ScholarPubMed
Posner, M. I. (1980). Orienting of attention. Quarterly Journal of Experimental Psychology, 32(1), 3–25.CrossRefGoogle ScholarPubMed
Quinlan, P. T., & Wilton, R. N. (1998). Grouping by proximity or similarity? Competition between the gestalt principles in vision. Perception, 27, 417–430.CrossRefGoogle ScholarPubMed
Riedmiller, M. (1994). Rprop − description and implementation details. Technical report, Institut für Logik, Komplexität und Deduktionssyteme, University of Karlsruhe.Google Scholar
Riesenhuber, M., & Poggio, T. (1999). Hierarchical models of object recognition in cortex. Nature Neuroscience, 2(11), 1019–1025.CrossRefGoogle ScholarPubMed
Riesenhuber, M., & Poggio, T. (2002). Neural mechanisms of object recognition. Current Opinion in Neurobiology, 12, 162–168.CrossRefGoogle ScholarPubMed
Robertson, L. (1996). Attentional persistence for features of hierarchical patterns. Journal of Experimental Psychology: General, 125, 227–249.CrossRefGoogle ScholarPubMed
Robertson, L., Lamb, M., & Knight, R. (1988). Effects of lesions of temporo-parietal junction on perceptual and attentional processing in humans. Journal of Neuroscience, 8, 3757–3769.CrossRefGoogle Scholar
Rolls, E. T., & Deco, G. (2006). Attention in natural scenes: neurophysiological and computational bases. Neural Networks, 19, 1383–1394.CrossRefGoogle ScholarPubMed
Rothenstein, L., & Tsotsos, J. (2008). Attention links sensing to recognition. Image and Vision Computing, 26, 114–126.CrossRefGoogle Scholar
Rumelhart, D., Hinton, G., & Williams, R. (1986). Learning internal representations by error propagation. In Parallel Distributed Processing: Explorations in the Microstructures of Cognition, volume 1: Foundations (pp. 318–362). Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Sarnecka, B., Kamenskaya, V., Yamana, Y., Ogura, T., & Yudovina, Y. (2007). From grammatical number to exact numbers: early meanings of ‘one,’ ‘two,’ and ‘three’ in English, Russian, and Japanese. Cognitive Psychology, 55(2), 136–168.CrossRefGoogle ScholarPubMed
Schyns, P., & Oliva, A. (1999). Dr Angry and Mr Smile: when categorization flexibly modifies the perception of faces in rapid serial visual presentations. Cognition, 69, 243–265.CrossRefGoogle Scholar
Shinkareva, S., Mason, R., Malave, V., Wang, W., Mitchell, T., & Just, M. (2008). Using fMRI brain activation to identify cognitive states associated with perception of tools and dwellings. PLoS One, 3(1), e1394.CrossRefGoogle ScholarPubMed
Sowden, P., & Schyns, P. (2006). Channel surfing in the visual brain. Trends in Cognitive Sciences, 10(12), 538–545.CrossRefGoogle ScholarPubMed
Sudre, G., Pomerleau, D., Palatucci, M., Wehbe, L., Fyshe, A., Salmelin, R., & Mitchell, T. (2012). Tracking neural coding of perceptual and semantic features of concrete nouns. NeuroImage, 62, 51–463.CrossRefGoogle ScholarPubMed
Tanaka, K. (1996). Inferotemporal cortex and object vision. Annual Review of Neuroscience, 19, 103–139.CrossRefGoogle ScholarPubMed
Theeuwes, J. (1994). The effects of location cuing on redundant-target processing. Psychological Research, 57, 15–19.CrossRefGoogle ScholarPubMed
Thompson, K., & Bichot, N. (2005). A visual salience map in the primate frontal eye field. Progress in Brain Research, 147, 251–262.Google ScholarPubMed
Tranel, D., Adolphs, R., Damasio, H., & Damasio, A. (2001). A neural basis for the retrieval of words for actions. Cognitive Neuropsychology, 18(7), 655–674.CrossRefGoogle ScholarPubMed
Trick, L., & Pylyshyn, Z. (1994). Why are small and large numbers enumerated differently? A limited capacity preattentive stage in vision. Psychological Review, 101, 80–102.CrossRefGoogle ScholarPubMed
Tsotsos, J., Culhane, S., Wai, W., Lai, Y, Davis, N., & Nuflo, F. (1995). Modeling visual attention via selective tuning. Artificial Intelligence, 78, 507–545.CrossRefGoogle Scholar
Ullman, S. (1996). High-level vision: object recognition and visual cognition. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Ungerleider, L. A., & Mishkin, M. (1982). Two cortical visual systems. In Ingle, D. J.Goodale, M. A., & Mansfield, R. J. (Eds.), Analysis of visual behavior (pp. 549–586). Cambridge, MA: MIT Press.Google Scholar
Van der Sandt, R. (1992). Presupposition projection as anaphora resolution. Journal of Semantics, 9, 333–377.CrossRefGoogle Scholar
Vigliocco, G., Vinson, D., Druks, J., Barber, H., & Cappa, S. (2011). Nouns and verbs in the brain: a review of behavioural, electrophysiological, neuropsychological and imaging studies. Neuroscience and Biobehavioral Reviews, 35, 407–426.CrossRefGoogle Scholar
Walles, H., Knott, A., & Robins, A. (2008). A model of cardinality blindness in inferotemporal cortex. Biological Cybernetics, 98(5), 427–437.CrossRefGoogle Scholar
Walther, D., & Koch, C. (2006). Modeling attention to salient proto-objects. Neural Networks, 19(9), 1395 – 1407.CrossRefGoogle ScholarPubMed
Wolfe, J. M. (1994). Guided search 2.0 − a revised model of visual-search. Psychonomic Bulletin & Review, 1(2), 202–238.CrossRefGoogle ScholarPubMed
Wolfe, J. M. (2007). Guided search 4.0: current progress with a model of visual search. In Graw, W. (Ed.), Integrated models of cognitive systems (pp. 99–119). New York: Oxford University Press.CrossRefGoogle Scholar
Xue, G., Dong, Q., Chen, C., Lu, Z., Mumford, J., & Poldrack, R. (2010). Greater neural pattern similarity across repetitions is associated with better memory. Science, 330, 97–101.CrossRefGoogle ScholarPubMed
Yamaguchi, S., Yamagata, S., & Kobayashi, S. (2000). Cerebral asymmetry of the ‘top-down’ allocation of attention to global and local features. Journal of Neuroscience, 20(9), RC72.CrossRefGoogle ScholarPubMed
Zhang, Y., Meyers, E., Bichot, N., Serre, T., Poggio, T., & Desimone, R. (2011). Object decoding with attention in inferior temporal cortex. Proceedings of the National Academy of Sciences of the USA, 108(21), 8850–8855.CrossRefGoogle ScholarPubMed
Zwaan, R., & Taylor, L. (2006). Seeing, acting, understanding: motor resonance in language comprehension. Journal of Experimental Psychology: General, 135(1), 1–11.CrossRefGoogle ScholarPubMed