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The Cognitive Construction of Dialog: Language and Mind

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Dialog Systems

Part of the book series: Logic, Argumentation & Reasoning ((LARI,volume 22))

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Abstract

The rapid spread of interdisciplinary methods on the study of language has provoked a massive reconfiguration of the concept of dialog in research. Likewise, computer-based communication and the large data that it provides has completely changed our notion of what language entails: we have moved away from written text to spoken conversation, from a sentence-based approach to large corpora and from monologic to an increasing dialogic view of language. This chapter reviews the human memory system and establishes a connection with some theories in the field of Cognitive Linguistics in order to shed some light in understanding the final purpose of human communication and how it connects our most social nature with knowledge and information across our lifespan. Interactions are described as being dialogic in nature having the ultimate goal for the initiator of the communicative interaction to collect information from the outside, and the recipient of that interaction in the role of instantiating that objective. Dialog is seen as specific (the present time frame determines meaning construction), focused, prominent and dynamic (users alternate roles during dialog from initiator to recipient and viceversa). Communicative interaction (“usage events” according to Cognitive Linguistics) is so important in our lives that it shapes the way the brain is wired and posits some interesting theories on how language usage is constructive per se. The chapter finally draws on the correlation that exists between these usage events (“construals” in Current Discourse Space), the subjects of conceptualization, the objects of conceptualization and cognitive mapping, more in particular Working Memory, as the main engine that allows for repetitive stimulation and Long-Term Memory (Semantic and Episodic Memory) which finally stores complex semantic networks made up of form (phonological load) and meaning (semantic load). These sophisticated cognitive patterns stand for knowledge as we know it and represent permanent structures from which users can retrieve, reshape and extend along life experience.

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Notes

  1. 1.

    Predictive techniques attributed to the way the Mental Lexicon is accessed to in normal conversation have been adopted by Artificial Intelligence in areas such as Automatic Speech Recognition (ASR). Jelinek (1997), Murphy (2012), Koehn et al. (2003), among others, have developed statistically-based algorithms in language modeling and machine learning in general from the ideas stated by psychologists such as Marslen-Wilson and Cole (and more) 30 years earlier. This transposition of the concept of prediction (probability) in the way human beings store and retrieve lexical items (form and meaning) have evolved into inferential models in Artificial Intelligence, including (dynamic) Bayesian methods (Murphy and Russell 2002; Juang et al. 1997; Zweig and Russell 1998), hidden Markov models (Cappé et al. 2006; Juang and Rabiner 1991) or maximum entropy (Toutanova and Manning 2000; Rosenfeld 1997).

  2. 2.

    Another interesting debate between the concepts of “acquisition” and “learning” is worth some time, but may escape the objectives of this chapter that attempts to be just a general review. However, much has been said about “acquisition” relating to a constant repetition necessary for “learning” in which case it would be more associated with short-term cognitive storage structures, such as WM, while LTM would account for the containment of more perdurable cognition load, therefore knowledge or learning (Pagpagno et al. 1991; Hulstijn 2001; Gathercole 2006).

  3. 3.

    The author of this chapter would like to remind the reader of the importance of social interactions and the very social nature that characterizes us as individuals. It would be relevant to incorporate more of that social perspective in research on dialog in order to emphasize and account for the relevance of our desire to belong to a social group for which purpose language must be seen as the most important agent (Tomasello 2003).

  4. 4.

    From a neuropsychological point of view, the “zero frame” would require the activation of working memory while the “plus frame” would entail the information being stored in long-term memory structure.

References

  • Auer, P. (2009). On-line syntax: Thoughts on the temporality of spoken language. Language Sciences, 31, 1–13.

    Article  Google Scholar 

  • Baddeley, A. (2003). Working memory: Looking back and looking forward. Nature Reviews Neuroscience, 4(10), 829–839.

    Article  Google Scholar 

  • Baddeley, A. D., & Hitch, G. (1974). Working memory. In G. H. Bower (Ed.), The psychology of learning and motivation: Advances in research and theory (Vol. 8, pp. 47–89). New York: Academic.

    Google Scholar 

  • Baddeley, A. D., & Warrington, E. K. (1970). Amnesia and the distinction between long-and short-term memory. Journal of Verbal Learning and Verbal Behavior, 9(2), 176–189.

    Article  Google Scholar 

  • Baggio, G. (2018). Meaning in the brain. New York: MIT Press.

    Book  Google Scholar 

  • Barlow, M., & Kemmer, S. (Eds.). (2000). Usage-based models of language. Stanford: Center for the Study of Language and Information.

    Google Scholar 

  • Barsalou, L. W. (2005). Situated conceptualization. In Handbook of categorization in cognitive science (pp. 619–650). St. Louis: Elsevier Science Ltd.

    Chapter  Google Scholar 

  • Barsalou, L. W. (2015). Situated conceptualization: Theory and applications. In Perceptual and emotional embodiment (pp. 19–45). London: Routledge.

    Google Scholar 

  • Boogaart, R., Colleman, T., & Rutten, G. (Eds.). (2014). Extending the scope of construction grammar (Vol. 54). Berlin: Walter de Gruyter GmbH & Co KG.

    Google Scholar 

  • Brône, G., & Coulson, S. (2010). Processing deliberate ambiguity in newspaper headlines: Double grounding. Discourse Processes, 47(3), 212–236.

    Article  Google Scholar 

  • Brône, G., & Zima, E. (2014). Towards a dialogic construction grammar: Ad hoc routines and resonance activation. In R. Giora & J. W. Du Bois (Eds.), special issue of Cognitive Linguistics, 25(3), 457–495.

    Google Scholar 

  • Brown, J. (1958). Some tests of the decay theory of immediate memory. Quarterly Journal of Experimental Psychology, 10(1), 12–21.

    Article  Google Scholar 

  • Bybee, J. L., & Hopper, P. J. (Eds.). (2001). Frequency and the emergence of linguistic structure (Vol. 45). Amsterdam: John Benjamins Publishing.

    Google Scholar 

  • Cappé, O., Moulines, E., & Rydén, T. (2006). Inference in hidden Markov models. New York: Springer.

    Google Scholar 

  • Chang, F., Dell, G. S., & Bock, K. (2006). Becoming syntactic. Psychological Review, 113(2), 234.

    Article  Google Scholar 

  • Cienki, A. (2015). Spoken language usage events. Language and Cognition, 7(4), 499–514.

    Article  Google Scholar 

  • Cole, R. A., & Jakimik, J. (1978). Understanding speech: How words are heard. In Strategies of information processing (pp. 67–116). London: Academic.

    Google Scholar 

  • Collins, A. M., & Quillian, M. R. (1970). Facilitating retrieval from semantic memory: The effect of repeating part of an inference. Acta Psychologica, 33, 304–314.

    Article  Google Scholar 

  • Conway, C. M., Bauernschmidt, A., Huang, S. S., & Pisoni, D. B. (2010). Implicit statistical learning in language processing: Word predictability is the key. Cognition, 114(3), 356–371.

    Article  Google Scholar 

  • Coulson, S. (2000). Semantic leaps: Frame-shifting and conceptual blending in meaning construction. Cambridge: Cambridge University Press.

    Google Scholar 

  • Coulson, S. (2006). Constructing meaning. Metaphor and Symbol, 21(4), 245–266.

    Article  Google Scholar 

  • Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24(1), 87–114.

    Article  Google Scholar 

  • De Saussure, F. (1959). Course in general linguistics. New York: Philosophical Library.

    Google Scholar 

  • Deppermann, A. (2011). The study of formulations as a key to an interactional semantics. Human Studies, 34(2), 115–128.

    Article  Google Scholar 

  • Fauconnier, G., & Turner, M. (2002). The way we think: Conceptual blending and the mind’s hidden complexities. New York: Basic Books.

    Google Scholar 

  • Fischer, K. (2010). Beyond the sentence: Constructions, frames and spoken interaction. Constructions and Frames, 2(2), 185–207.

    Article  Google Scholar 

  • Fried, M., & Östman, J. O. (2005). Construction grammar and spoken language: The case of pragmatic particles. Journal of Pragmatics, 37(11), 1752–1778.

    Article  Google Scholar 

  • Gathercole, S. E., & Baddeley, A. D. (1989). Evaluation of the role of phonological STM in the development of vocabulary in children: A longitudinal study. Journal of Memory and Language, 28(2), 200–213.

    Article  Google Scholar 

  • Gathercole, S. E. (2006). Nonword repetition and word learning: The nature of the relationship. Applied PsychoLinguistics, 27(4), 513–543.

    Article  Google Scholar 

  • Günthner, S., Bücker, J., & Imo, W. (Eds.). (2014). Grammar and dialogism: Sequential, syntactic, and prosodic patterns between emergence and sedimentation. Berlin/Boston: de Gruyter.

    Google Scholar 

  • Harm, M. W., & Seidenberg, M. S. (2004). Computing the meanings of words in reading: Cooperative division of labor between visual and phonological processes. Psychological Review, 111(3), 662.

    Article  Google Scholar 

  • Hebb, D. O. (1949). The organization of behavior: A neuropsychological theory. New York: Wiley/Chapman & Hall.

    Google Scholar 

  • Heritage, J. (2005). Conversation analysis and institutional talk. In Handbook of language and social interaction (Vol. 103, p. 47). Mahwah: Lawrence Erlbaum Associates.

    Google Scholar 

  • Holt, R. (2004). Dialogue on the internet: Language, civic identity, and computer-mediated communication. Westport: Greenwood Publishing Group.

    Google Scholar 

  • Hulstijn, J. H. (2001). Intentional and incidental second language vocabulary learning: A reappraisal of elaboration, rehearsal and automaticity. In P. Robinson (Ed.), Cognition and second language instruction (pp. 258–286). Cambridge: Cambridge University Press.

    Chapter  Google Scholar 

  • Ibbotson, P. (2013). The scope of usage-based theory. Frontiers in Psychology, 4, 255.

    Article  Google Scholar 

  • Janda, L. A. (2015). Cognitive linguistics in the year 2015. Cognitive Semantics, 1(1), 131–154.

    Article  Google Scholar 

  • Jelinek, F. (1997). Statistical methods for speech recognition. Cambridge, MA: MIT Press.

    Google Scholar 

  • Juang, B. H., & Rabiner, L. R. (1991). Hidden Markov models for speech recognition. Technometrics, 33(3), 251–272.

    Article  Google Scholar 

  • Juang, B. H., Hou, W., & Lee, C. H. (1997). Minimum classification error rate methods for speech recognition. IEEE Transactions on Speech and Audio Processing, 5(3), 257–265.

    Article  Google Scholar 

  • Koehn, P., Och, F. J., & Marcu, D. (2003, May). Statistical phrase-based translation. In Proceedings of the 2003 conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology-Volume 1 (pp. 48–54). Association for Computational Linguistics.

    Google Scholar 

  • Krug, M. (1998). String frequency: A cognitive motivating factor in coalescence, language processing, and linguistic change. Journal of English Linguistics, 26(4), 286–320.

    Article  Google Scholar 

  • Langacker, R. W. (1987). Foundations of cognitive grammar: Theoretical prerequisites (Vol. 1). Stanford: Stanford University Press.

    Google Scholar 

  • Langacker, R. W. (1988). An overview of cognitive grammar. In Topics in cognitive linguistics (p. 3). John Benjamins. ISO 690.

    Chapter  Google Scholar 

  • Langacker, R. W. (2001). Discourse in cognitive grammar. Cognitive linguistics, 12(2), 143–188.

    Article  Google Scholar 

  • Langacker, R. W., & Langacker, R. (2008). Cognitive grammar: A basic introduction. New York: OUP USA.

    Book  Google Scholar 

  • Langlotz, A. (2015). Local meaning-negotiation, activity types, and the current-discourse-space model. Language and Cognition, 7(4), 515–545.

    Article  Google Scholar 

  • Linell, P. (1998). Approaching dialogue: Talk, interaction and contexts in dialogical perspectives (Vol. 3). Amsterdam: John Benjamins Publishing.

    Book  Google Scholar 

  • Linell, P. (2009). Rethinking language, mind, and world dialogically. Charlotte: IAP.

    Google Scholar 

  • Lupker, S. J., & Pexman, P. M. (2010). Making things difficult in lexical decision: The impact of pseudohomophones and transposed-letter nonwords on frequency and semantic priming effects. Journal of Experimental Psychology: Learning, Memory, and Cognition, 36(5), 1267.

    Google Scholar 

  • Marslen-Wilson, W. (1973). Linguistic structure and speech shadowing at very short latencies. Nature, 244(5417), 522–523.

    Article  Google Scholar 

  • Marslen-Wilson, W. (1987). Functional parallelism in spoken word recognition. Cognition, 25, 71–102.

    Article  Google Scholar 

  • Marslen-Wilson, W. D., & Welsh, A. (1978). Processing interactions and lexical access during word recognition in continuous speech. Cognitive Psychology, 10(1), 29–63.

    Article  Google Scholar 

  • Melby-Lervåg, M., Lyster, S. A. H., & Hulme, C. (2012). Phonological skills and their role in learning to read: A meta-analytic review. Psychological Bulletin, 138(2), 322.

    Article  Google Scholar 

  • Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81.

    Article  Google Scholar 

  • Miller, G. A. (1962). Some psychological studies of grammar. American Psychologist, 17(11), 748–762.

    Article  Google Scholar 

  • Miller, G. A. (1991). The science of words. New York: Scientific American Library.

    Google Scholar 

  • Milner, B. (1966). Amnesia following operation on the temporal lobes. In C. M. W. Whitty & O. L. Zangwill (Eds.), Amnesia. London: Butterworths.

    Google Scholar 

  • Misyak, J. B., & Christiansen, M. H. (2012). Statistical learning and language: An individual differences study. Language Learning, 62(1), 302–331.

    Article  Google Scholar 

  • Murdock, B. B. (1974). Human memory: Theory and data. Potomac: Lawrence Erlbaum.

    Google Scholar 

  • Murphy, K. P. (2012). Machine learning: A probabilistic perspective. Cambridge, MA: MIT Press.

    Google Scholar 

  • Murphy, K. P., & Russell, S. (2002). Dynamic Bayesian networks: Representation, inference and learning. PhD thesis, University of California, Berkeley.

    Google Scholar 

  • Norris, D., McQueen, J. M., & Cutler, A. (2016). Prediction, Bayesian inference and feedback in speech recognition. Language, Cognition and Neuroscience, 31(1), 4–18.

    Article  Google Scholar 

  • Papagno, C., Valentine, T., & Baddeley, A. (1991). Phonological short-term memory and foreign-language vocabulary learning. Journal of Memory and Language, 30(3), 331–347.

    Article  Google Scholar 

  • Perruchet, P., & Pacton, S. (2006). Implicit learning and statistical learning: One phenomenon, two approaches. Trends in Cognitive Sciences, 10(5), 233–238.

    Article  Google Scholar 

  • Peterson, L., & Peterson, M. J. (1959). Short-term retention of individual verbal items. Journal of Experimental Psychology, 58(3), 193.

    Article  Google Scholar 

  • Plaut, D. C., & Booth, J. R. (2000). Individual and developmental differences in semantic priming: Empirical and computational support for a single-mechanism account of lexical processing. Psychological Review, 107(4), 786.

    Article  Google Scholar 

  • Rips, L. J., Shoben, E. J., & Smith, E. E. (1973). Semantic distance and the verification of semantic relations. Journal of Verbal Learning and Verbal Behavior, 12(1), 1–20.

    Article  Google Scholar 

  • Rosenfeld, R. (1997, December). A whole sentence maximum entropy language model. In 1997 IEEE workshop on automatic speech recognition and understanding proceedings (pp. 230–237). IEEE.

    Google Scholar 

  • Shallice, T., & Warrington, E. K. (1970). Independent functioning of verbal memory stores: A neuropsychological study. The Quarterly Journal of Experimental Psychology, 22(2), 261–273.

    Article  Google Scholar 

  • Tomasello, M. (2003). The key is social cognition. In Language in mind: Advances in the study of language and thought (pp. 47–57). Cambridge: MIT Press.

    Google Scholar 

  • Toutanova, K., & Manning, C. D. (2000, October). Enriching the knowledge sources used in a maximum entropy part-of-speech tagger. In Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: Held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics-Volume 13 (pp. 63–70). Association for Computational Linguistics.

    Google Scholar 

  • Tse, C. S., & Neely, J. H. (2007). Semantic priming from letter-searched primes occurs for low-but not high-frequency targets: Automatic semantic access may not be a myth. Journal of Experimental Psychology: Learning, Memory, and Cognition, 33(6), 1143.

    Google Scholar 

  • Turk-Browne, N. B., Scholl, B. J., Chun, M. M., & Johnson, M. K. (2009). Neural evidence of statistical learning: Efficient detection of visual regularities without awareness. Journal of Cognitive Neuroscience, 21(10), 1934–1945.

    Article  Google Scholar 

  • Verhagen, A. (2005). Constructions of intersubjectivity: Discourse, syntax, and cognition. Oxford: Oxford University Press on Demand.

    Google Scholar 

  • Zima, E., & Brône, G. (2015). Cognitive linguistics and interactional discourse: Time to enter into dialogue. Language and Cognition, 7(4), 485–498.

    Article  Google Scholar 

  • Zwarts, J. (1997). Vectors as relative positions: A compositional semantics of modified PPs. Journal of Semantics, 14, 57–86.

    Article  Google Scholar 

  • Zwarts, J. (2001). Vectors across spatial domains: From place to size, orientation, shape and parts (1). Unpublished ms., SIL/BTL, Kenya.

    Google Scholar 

  • Zwarts, J., & Winter, Y. (2000). Vector space semantics: A model-theoretic analysis of locative prepositions. Journal of Logic, Language and Information, 9, 169–211.

    Article  Google Scholar 

  • Zweig, G., & Russell, S. (1998). Speech recognition with dynamic Bayesian networks.

    Google Scholar 

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  1. Speech and Phonetic Sciences Lab, University of Seville, Sevilla, Spain

    Teresa Lopez-Soto

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    Teresa Lopez-Soto

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Lopez-Soto, T. (2021). The Cognitive Construction of Dialog: Language and Mind. In: Lopez-Soto, T. (eds) Dialog Systems. Logic, Argumentation & Reasoning, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-030-61438-6_3

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