Skip to main content
SpringerLink
Log in

How to Understand Three Types of Cognitive Models

  • Conference paper
  • First Online:
Cognitive Systems and Signal Processing (ICCSIP 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1005))

Included in the following conference series:

  • 1092 Accesses

Abstract

This paper aims to explore more efficient information processing methods through quasi-holographic space, knowledge and language cognitive systems. The method is as follows: First, the spatial computing system is understood as a formal abstract cognitive system; further, the five-loop traversal system is understood as a conceptual knowledge query system; finally, the language cognitive system is understood as a tabular text reusing system. The result is: quasi-holographic space, five-loop traversal and bit-list logic as three thinking modes or three types of cognitive systems, in the object form and knowledge content information processing on the same path. The significance is that not only the three types of cognitive systems, such as quasi-holographic space, five-loop traversal and order-sequence structure, all of them are difficult to understand, now are easily understood, and a new cognitive paradigm that is simplified is obtained.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Chapman, S., Marrochio, H., Myers, R.C.: Holographic complexity in vaidya spacetimes II. J. High Energy Phys. 2018(6), 114 (2018)

    Article  MathSciNet  Google Scholar 

  2. Baik, K., Dudley, C., Marston, P.L.: Acoustic quasi-holographic images of scattering by vertical cylinders from one-dimensional bistatic scans. J. Acoust. Soc. Am. 130(6), 3838 (2011)

    Article  Google Scholar 

  3. Zartman, D.J., Plotnick, D.S., Marston, T.M., et al.: Quasi-holographic processing as an alternative to synthetic aperture sonar imaging. J. Acoust. Soc. Am. 133(5), 3295 (2013)

    Article  Google Scholar 

  4. Tougbaev, V.A., Eom, T.J., Yu, B.A., et al.: Quasi-holographic solution to polarization-sensitive optical coherence tomography acceptable to nonlaboratory applications. J. Biomed. Opt. 13(4), 044014 (2008)

    Article  Google Scholar 

  5. Barbon, J.L.F., Martingarcia, J.: Terminal holographic complexity. J. High Energy Phys. 6(6), 132 (2018)

    Article  MathSciNet  Google Scholar 

  6. Chow, H.K.H., Choy, K.L., Lee, W.B.: A dynamic logistics process knowledge-based system - an RFID multi. Knowl.-Based Syst. 20(4), 357–372 (2007)

    Article  Google Scholar 

  7. Leo Kumar, S.P.: Knowledge-based expert system in manufacturing planning: state-of-the-art review. Int. J. Product. Res. 1–25 (2018)

    Google Scholar 

  8. Shihabudheen, K.V., Pillai, G.N.: Recent advances in neuro-fuzzy system: a survey. Knowl.-Based Syst. 152, 136–162 (2018)

    Article  Google Scholar 

  9. Campbell, K.E., Oliver, D.E., Shortliffe, E.H.: The unified medical language system. J. Am. Med. Inform. Assoc. 5(1), 12–16 (1998)

    Article  Google Scholar 

  10. Hurwitz, J., Kaufman, M., Bowles, A.: 9 IBM’s Watson as a Cognitive System. Cognitive Computing and Big Data Analytics. Wiley (2015)

    Google Scholar 

  11. Spiridonov, V., Ezrina, E.: The interaction of several languages in the cognitive system. Russ. J. Cogn. Sci. 2(4), 12–29 (2015). Social Science Electronic Publishing

    Google Scholar 

  12. SFL Inc.: Dynamically evolving cognitive architecture system based on a natural language intent interpreter (2017)

    Google Scholar 

  13. Leyton, M.: Principles of information structure common to six levels of the human cognitive system. Inf. Sci. 38(1), 1–120 (1986)

    Article  MathSciNet  Google Scholar 

  14. Leu, G., Abbass, H.: A multi-disciplinary review of knowledge acquisition methods: from human to autonomous eliciting agents. Knowl.-Based Syst. 105(C), 1–22 (2016)

    Article  Google Scholar 

  15. Chang, J.S., Wong, H.J.: Selecting appropriate sellers in online auctions through a multi-attribute reputation calculation method. Electron. Commer. Res. Appl. 10(2), 144–154 (2011)

    Article  Google Scholar 

  16. Thompson, J.A., Bell, J.C., Butler, C.A.: Digital elevation model resolution: effects on terrain attribute calculation and quantitative soil-landscape modeling. Geoderma 100(1), 67–89 (2015)

    Google Scholar 

  17. Feinerer, I., Hornik, K., Meyer, D.: Text mining infrastructure in R. (2015). Text MI

    Google Scholar 

  18. Sapiro-Gheiler, E.: “Read my lips”: using automatic text analysis to classify politicians by party and ideology (2018). Read ML

    Google Scholar 

  19. Angeli, G., Premkumar, M.J.J., Manning, C.D.: Leveraging linguistic structure for open domain information extraction, Leveraging LS, ACL (2015)

    Google Scholar 

  20. Del Corro, L., Gemulla, R.: Claus IE: clause-based open information extraction (2013). Claus IECO WWW

    Google Scholar 

  21. Padia, A., Ferraro, F., Finin, T.W.: KGCleaner: identifying and correcting errors produced by information extraction systems. KGC leaner I, journal CoRR, abs/1808.04816 (2018)

    Google Scholar 

  22. Jannin, P., Strauss, G., Meixensberger, J., Burgert, O.: Validation of knowledge acquisition for surgical process models (2018). Validation OK

    Google Scholar 

  23. Gordon, J., Van Durme, B.: Reporting bias and knowledge acquisition (2013). Reporting BA, AKBC @CIKM

    Google Scholar 

  24. Lin, Y., Liu, Z., Luan, H., Sun, M., Rao, S., Liu, S.: Modeling relation paths for representation learning of knowledge bases. In: Proceedings (2015). Modeling RP, EMNLP

    Google Scholar 

  25. Kuznetsov, S.O., Poelmans, J.: Knowledge representation and processing with formal concept analysis. Wiley Interdisc. Rev.: Data Min. Knowl. Discov. 3, 200–215 (2013)

    Google Scholar 

  26. Manning, C., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S., McClosky, D.: The stanford core NLP natural language processing toolkit. In: Proceedings Manning (2014). The SC, ACL

    Google Scholar 

  27. Sarikaya, R., Hinton, G.E., Deoras, A.: Application of deep belief networks for natural language understanding. IEEE/ACM Trans. Audio Speech Lang. Process. 22, 778–784 (2014). Application OD

    Article  Google Scholar 

  28. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. Neural MT, journal CoRR, abs 1409.0473 (2014)

    Google Scholar 

  29. Jia, R., Liang, P.: Adversarial examples for evaluating reading comprehension systems. In: Proceedings (2017). Adversarial EF, EMNLP

    Google Scholar 

  30. Rajpurkar, P., Zhang, J., Lopyrev, K., Liang, P.: SQuAD: 100, 000 + questions for machine comprehension of text. In: Proceedings (2016). SQuAD10, EMNLP

    Google Scholar 

  31. Stanley, G.B.: Reading and writing the neural code. Nat. Neurosci. 16, 259–263 (2013)

    Article  Google Scholar 

  32. King, K.D.: Bringing Creative writing instruction into reminiscence group treatment. Clin. Gerontol. 41, 1–7 (2018)

    Article  Google Scholar 

  33. Uddin, G., Khomh, F.: Automatic summarization of API reviews. In: 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 159–170 (2017). Automatic SO

    Google Scholar 

  34. Zou, X.: Original Collection on Smart-System Studied. Smashwords, Inc. (2018). ISBN 9780463607640

    Google Scholar 

  35. Zou, X.: Advanced Collection on Smart-System Studied. Smashwords, Inc. (2018). ISBN 9780463020036

    Google Scholar 

  36. Zou, X., Zou, S., Ke, L.: Fundamental law of information: proved by both numbers and characters in conjugate matrices. In: Proceedings, vol. 1, p. 60 (2017)

    Google Scholar 

  37. Zou, S., Zou, X.: Understanding: how to resolve ambiguity. In: Shi, Z., Goertzel, B., Feng, J. (eds.) ICIS 2017. IAICT, vol. 510, pp. 333–343. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68121-4_36

    Chapter  Google Scholar 

  38. Underhill, J.W.: Humboldt Worldview and Language. Edinburgh University Press, Edinburgh (2013). pp. 12, 161

    Google Scholar 

  39. Joseph, J.E.: Saussurean tradition in linguistics. In: Concise History of the Language Sciences, pp. 233–239 (1995)

    Google Scholar 

  40. French, R.M.: Subcognition and the limits of the turing test. Mind 99(393), 53–65 (1990)

    Article  MathSciNet  Google Scholar 

  41. Preston, J., Bishop, M.: Views into the Chinese room: new essays on searle and artificial intelligence. Minds Mach. 15(1–111), 97–106 (2005)

    MATH  Google Scholar 

  42. Starks, M.R.: The Logical Structure of Philosophy, Psychology, Mind and Language as Revealed in the Writings of Wittgenstein and Searle (2016)

    Google Scholar 

  43. Strong, T.: Therapy as a New Language Game? A Review of Wittgenstein and Psychotherapy: From Paradox to Wonder. Psyccritiques (2015)

    Google Scholar 

  44. Fox, C.: Heidegger’s “black notebooks”. Philosophy 90(2), 1–12 (2018)

    Google Scholar 

  45. Zuo, X., Zuo, S.: Indirect computing model with indirect formal method. Computer Engineering & Software (2011)

    Google Scholar 

  46. Zou, X., Zou, S.: Two major categories of formal strategy. Comput. Appl. Softw. 24(16), 3086–3114 (2013)

    Google Scholar 

  47. Xiaohui, Z., Shunpeng, Z.: Bilingual information processing method and principle. J. Comput. Appl. Softw. 32(11), 69–76 (2015)

    Google Scholar 

  48. Zou, X., Zou, S.: Virtual twin turing machine: bilingual information processing as an example. Software (2011)

    Google Scholar 

  49. Zou, X., Zou, S.: Basic law of information: the fundamental theory of generalized bilingual processing. In: ISIS Summit Vienna 2015. The Information Society at the Crossroads. 2015:T9.1002 (2015)

    Google Scholar 

  50. Loeb, I.: The role of universal language in the early work of Carnap and Tarski. Synthese 194, 1–17 (2017)

    Article  MathSciNet  Google Scholar 

  51. Hernández-Orallo, J.: Evaluation in artificial intelligence: from task-oriented to ability-oriented measurement. Artif. Intell. Rev. 48, 1–51 (2017)

    Article  Google Scholar 

  52. Lu, W., Chen, T.: New conditions on global stability of Cohen-Grossberg neural networks. Neural Comput. 15(5), 1173 (2003)

    Article  Google Scholar 

  53. Traoré, M.K., Muzy, A.: Capturing the dual relationship between simulation models and their context. Simul. Model. Pract. Theory 14(2), 126–142 (2018)

    Article  Google Scholar 

  54. Mcgregor, A., Vu, H.T.: Better streaming algorithms for the maximum coverage problem. Theory Comput. Syst. 1–25 (2018)

    Google Scholar 

  55. Partala, T., Surakka, V.: The effects of affective interventions in human–computer interaction. Interact. Comput. 16(2), 295–309 (2018)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Sino-American Searle Research Center, UC Berkeley, Berkeley, 94720-3840, USA

    Xiaohui Zou

  2. Room 415, Audio-Visual Building, Peking University, Beijing, 100871, China

    Xiaohui Zou

  3. Research Department, Tsinghua University China Knowledge Network (CNKI), Beijing, China

    Yizhen Qi

  4. Beijing Wang Dixing Intelligent Computer Technology Co., Ltd., Beijing, China

    Dixing Wang

Authors
  1. Xiaohui Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yizhen Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dixing Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaohui Zou .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Fuchun Sun

  2. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Huaping Liu

  3. College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan, China

    Dewen Hu

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zou, X., Qi, Y., Wang, D. (2019). How to Understand Three Types of Cognitive Models. In: Sun, F., Liu, H., Hu, D. (eds) Cognitive Systems and Signal Processing. ICCSIP 2018. Communications in Computer and Information Science, vol 1005. Springer, Singapore. https://doi.org/10.1007/978-981-13-7983-3_24

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-7983-3_24

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-7982-6

  • Online ISBN: 978-981-13-7983-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation