Skip to main content

Advertisement

SpringerLink
Log in

Improving human-robot interaction based on joint attention

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

The current study proposes a novel cognitive architecture for a computational model of the limbic system, inspired by human brain activity, which improves interactions between a humanoid robot and preschool children using joint attention during turn-taking gameplay. Using human-robot interaction (HRI), this framework may be useful for ameliorating problems related to attracting and maintaining attention levels of children suffering from attention deficit hyperactivity disorder (ADHD). In the proposed framework, computational models including the amygdala, hypothalamus, hippocampus, and basal ganglia are used to simulate a range of cognitive processes such as emotional responses, episodic memory formation, and selection of appropriate behavioral responses. In the currently proposed model limbic system, we applied reinforcement and unsupervised learning-based adaptation processes to a dynamic neural field model, resulting in a system that was capable of observing and controlling the physical and cognitive processes of a humanoid robot. Several interaction scenarios were tested to evaluate the performance of the model. Finally, we compared the results of our methodology with a neural mass model.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

References

  1. Gordon G, Breazeal C, Engel S Can Children Catch Curiosity from a Social Robot? In: Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction, 2015, pp. 91–98

  2. Gordon G, Breazeal C (2015) Bayesian Active Learning-Based Robot Tutor for Children’s Word-Reading Skills In: Twenty-Ninth AAAI Conference on Artificial Intelligence

    Google Scholar 

  3. Uluer P, Akalın N, Köse H (2015) A New Robotic Platform for Sign Language Tutoring. Int J Soc Robot 7:571–585

    Article  Google Scholar 

  4. Sandamirskaya Y (2013) Dynamic neural fields as a step toward cognitive neuromorphic architectures. Front Neurosci 7

  5. Richter M, Lins J, Schneegans S, Schöner G A neural dynamic architecture resolves phrases about spatial relations in visual scenes In: Artificial Neural Networks and Machine Learning–ICANN 2014, ed: Springer, 2014, pp. 201–208

  6. Durán B., Lee G, Lowe R (2012) Learning a DFT-based sequence with reinforcement learning: a NAO implementation. Paladyn 3:181–187

    Google Scholar 

  7. Samsonovich AV (2013) Emotional biologically inspired cognitive architecture. Biologically Inspired Cognitive Architectures 6:109–125

    Article  Google Scholar 

  8. Verschure PF (2012) Distributed adaptive control: a theory of the mind, brain, body nexus. Biologically Inspired Cognitive Architectures 1:55–72

    Article  Google Scholar 

  9. Denizdurduran B, Sengör NS A Realization of Goal-directed Behavior-Implementing a Robot Model based on Cortico-Striato-Thalamic Circuits In: ICAART (1), 2012, pp. 289–294

  10. Navarro-Guerrero N, Lowe R, Wermter S A neurocomputational amygdala model of auditory fear conditioning: A hybrid system approach In: The 2012 International Joint Conference on Neural Networks (IJCNN), 2012, pp. 1–8

  11. Richter M, Sandamirskaya Y, Schöner G. A robotic architecture for action selection and behavioral organization inspired by human cognition In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2012, pp. 2457–2464

  12. Zibner SK, Faubel C, Iossifidis I, Schöner G. (2011) Dynamic neural fields as building blocks of a cortex-inspired architecture for robotic scene representation. IEEE Trans Auton Ment Dev 3:74–91

    Article  Google Scholar 

  13. Pinheiro M, Bicho E, Erlhagen W A dynamic neural field architecture for a pro-active assistant robot In: 2010 3rd IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), 2010, pp. 777–784

  14. Bicho E, Erlhagen W, Louro L, e Silva EC (2011) Neuro-cognitive mechanisms of decision making in joint action: A human–robot interaction study. Hum Mov Sci 30:846–868

    Article  Google Scholar 

  15. Pinheiro M, Bicho E A socially assistive robot for people with motor impairments In: 2013 IEEE 3rd Portuguese Meeting in Bioengineering (ENBENG), 2013, pp. 1–7

  16. Sousa E, Erlhagen W, Ferreira F, Bicho E (2015) Off-line simulation inspires insight: a neurodynamics approach to efficient robot task learning. Neural Netw 72:123–139

    Article  Google Scholar 

  17. Sauser EL, Billard AG Biologically inspired multimodal integration: Interferences in a human-robot interaction game In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2006, pp. 5619–5624

  18. Daglarli E, Kose H, İnce G. Computational Model of the Biologically Inspired Cognitive Architecture for Human Robot Interaction In: Proceedings of the IEEE/RSJ International Conference on Robots and Intelligent Systems (IROS), International Workshop on Developmental Social Robotics (DevSoR), Tokyo, 2013, pp. 37–44

  19. Knudsen EI (2007) Fundamental components of attention. Annu Rev Neurosci 30:57–78

    Article  Google Scholar 

  20. Ioannou A, Andreou E, Christofi M (2015) Pre-schoolers’ Interest and Caring Behaviour Around a Humanoid Robot. TechTrends 59:23–26

    Article  Google Scholar 

  21. Kennedy J, Baxter P, Belpaeme T (2015) The robot who tried too hard: Social behaviour of a robot tutor can negatively affect child learning In: Proc. HRI

    Google Scholar 

  22. Anderson JR (2005) Cognitive psychology and its implications. Macmillan

  23. Weyhenmeyer JA, Gallman EA (2007) Rapid Review of Neuroscience. Mosby Elsevier, ISBN-10: 0323022618, 102

  24. Grimbert F, Faugeras O (2006) Bifurcation analysis of Jansen’s neural mass model. Neural Comput 18:3052–3068

    Article  MathSciNet  MATH  Google Scholar 

  25. Amari S-I (1977) Dynamics of pattern formation in lateral-inhibition type neural fields. Biol Cybern 27:77–87

    Article  MathSciNet  MATH  Google Scholar 

  26. Hebb DO (1949) The organization of behavior: A neuropsychological approach. Wiley

  27. Sutton RS, Barto AG (1998) Introduction to reinforcement learning, vol 135. MIT Press Cambridge

  28. Kohonen T (1990) The self-organizing map. Proc IEEE 78:1464–1480

    Article  Google Scholar 

  29. Amunts K, Kedo O, Kindler M, Pieperhoff P, Mohlberg H, Shah N, et al. (2005) Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps. Anat Embryol 210:343–352

    Article  Google Scholar 

  30. Breazeal C (2004) Function meets style: insights from emotion theory applied to HRI. IEEE Trans Syst Man Cybern Part C Appl Rev 34:187–194

    Article  Google Scholar 

  31. Akinci HM, Yesil E Emotion modeling using Fuzzy Cognitive Maps In: 2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI), 2013, pp. 49–55

  32. Amaral D, Lavenex P (2007) Hippocampal neuroanatomy. The hippocampus book 3:37–114

    Google Scholar 

  33. Stocco A, Lebiere C, Anderson JR (2010) Conditional routing of information to the cortex: A model of the basal ganglia’s role in cognitive coordination. Psychoanal Rev 117:541

    Article  Google Scholar 

Download references

Acknowledgments

This research was supported by the Istanbul Technical University BAP foundation under project number 37738.

Author information

Authors and Affiliations

  1. Istanbul Technical University, Department of Computer Engineering, Istanbul, Turkey

    Evren Dağlarlı & Hatice Köse

  2. Children Services at Ministry of Family and Social Policies, Istanbul, Turkey

    Sare Funda Dağlarlı

  3. Istanbul Technical University, Department of Control Engineering, Istanbul, Turkey

    Gülay Öke Günel

Authors
  1. Evren Dağlarlı
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sare Funda Dağlarlı
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gülay Öke Günel
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hatice Köse
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Evren Dağlarlı.

Ethics declarations

Conflict of interests

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dağlarlı, E., Dağlarlı, S.F., Günel, G.Ö. et al. Improving human-robot interaction based on joint attention. Appl Intell 47, 62–82 (2017). https://doi.org/10.1007/s10489-016-0876-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-016-0876-x

Keywords

Search

Navigation