Have a personal or library account? Click to login
A Neurorobotics Simulation of Autistic Behavior Induced by Unusual Sensory Precision Cover

A Neurorobotics Simulation of Autistic Behavior Induced by Unusual Sensory Precision

Computational Psychiatry
Volume 2 (2018): Issue 0
By: Hayato Idei,  Shingo Murata,  Yiwen Chen,  Yuichi Yamashita,  Jun Tani and  Tetsuya Ogata  
Open Access
|Dec 2018

References

  1. Adams, R. A., Huys, Q. J. M., & Roiser, J. P. (2015). Computational psychiatry: Towards a mathematically informed understanding of mental illness. Journal of Neurology, Neurosurgery, and Psychiatry. https://doi.org/10.1136/jnnp-2015-310737
    Back to article
  2. Alnajjar, F., Yamashita, Y., & Tani, J. (2013). The hierarchical and functional connectivity of higher-order cognitive mechanisms: Neurorobotic model to investigate the stability and flexibility of working memory. Frontiers in Neurorobotics, 7, 1–13. https://doi.org/10.3389/fnbot.2013.00002
    Back to article
  3. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: Author.
    Back to article
  4. Asada, M., Hosoda, K., Kuniyoshi, Y., Ishiguro, H., Inui, T., Yoshikawa, Y., … Yoshida, C. (2009). Cognitive developmental robotics: A survey. IEEE Transactions on Autonomous Mental Development, 1(1), 12–34. https://doi.org/10.1109/TAMD.2009.2021702
    Back to article
  5. Bar, M. (2007). The proactive brain: Using analogies and associations to generate predictions. Trends in Cognitive Sciences, 11, 280–289. https://doi.org/10.1016/j.tics.2007.05.005
    Back to article
  6. Barakova, E. I., & Chonnaparamutt, W. (2009). Timing sensory integration: Robot simulation of autistic behavior. IEEE Robotics and Automation Magazine, 16(3), 51–58. https://doi.org/10.1109/MRA.2009.933626
    Back to article
  7. Baron Cohen, S. (2001). Theory of mind and autism: A review. Journal of the Bertrand Russell Archives, 23(169), 169–184. https://doi.org/10.1016/S0074-7750(00)80010-5
    Back to article
  8. Bishop, S. L., Hus, V., Duncan, A., Huerta, M., Gotham, K., Pickles, A., … Lord, C. (2013). Subcategories of restricted and repetitive behaviors in children with ASD. Journal of Autism and Developmental Disorders, 43, 1287–1297. https://doi.org/10.1007/s10803-012-1671-0
    Back to article
  9. Bolton, P. F., Carcani-Rathwell, I., Hutton, J., Goode, S., Howlin, P., & Rutter, M. (2011). Epilepsy in autism: Features and correlates. British Journal of Psychiatry, 198, 289–294. https://doi.org/10.1192/bjp.bp.109.076877
    Back to article
  10. Brown, H., Adams, R. A., Parees, I., Edwards, M., & Friston, K. (2013). Active inference, sensory attenuation and illusions. Cognitive Processing, 14, 411–427. https://doi.org/10.1007/s10339-013-0571-3
    Back to article
  11. Chen, Y., Murata, S., Arie, H., Ogata, T., Tani, J., & Sugano, S. (2016). Emergence of interactive behaviors between two robots by prediction error minimization mechanism. Paper ICDL-EpiRob 2016 presented at the 6th Joint International Conference on Development and Learning and Epigenetic Robotics, Cergy-Pontoise, France.
    Back to article
  12. Den Ouden, H. E. M., Kok, P., & de Lange, F. P. (2012). How prediction errors shape perception, attention, and motivation. Frontiers in Psychology, 3, 1–12. https://doi.org/10.3389/fpsyg.2012.00548
    Back to article
  13. Diwadkar, V. A., Flaugher, B., Jones, T., Zalányi, L., Ujfalussy, B., Keshavan, M. S., & Érdi, P. (2008). Impaired associative learning in schizophrenia: Behavioral and computational studies. Cognitive Neurodynamics, 2, 207–219. https://doi.org/10.1007/s11571-008-9054-0
    Back to article
  14. Eagleman, D. M. (2000). Motion integration and postdiction in visual awareness. Science, 287, 2036–2038. https://doi.org/10.1126/science.287.5460.2036
    Back to article
  15. Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14, 179–211. https://doi.org/10.1016/0364-0213(90)90002-E
    Back to article
  16. Friston, K. J., Daunizeau, J., Kilner, J., & Kiebel, S. J. (2010). Action and behavior: A free-energy formulation. Biological Cybernetics, 102, 227–260. https://doi.org/10.1007/s00422-010-0364-z
    Back to article
  17. Friston, K. J., Lawson, R., & Frith, C. D. (2013). On hyperpriors and hypopriors: Comment on Pellicano and Burr. Trends in Cognitive Sciences, 17(1), 1. https://doi.org/10.1016/j.tics.2012.11.003
    Back to article
  18. Friston, K. J., Stephan, K. E., Montague, R., & Dolan, R. J. (2014). Computational psychiatry: The brain as a phantastic organ. The Lancet Psychiatry, 1, 148–158. https://doi.org/10.1016/S2215-0366(14)70275-5
    Back to article
  19. Gowen, E., & Hamilton, A. (2013). Motor abilities in autism: A review using a computational context. Journal of Autism and Developmental Disorders, 43, 323–344. https://doi.org/10.1007/s10803-012-1574-0
    Back to article
  20. Happé, F., & Frith, U. (2006). The weak coherence account: Detail-focused cognitive style in autism spectrum disorders. Journal of Autism and Developmental Disorders, 36(1), 5–25. https://doi.org/10.1007/s10803-005-0039-0
    Back to article
  21. Hill, E. L. (2004). Executive dysfunction in autism. Trends in Cognitive Sciences, 8(1), 26–32. https://doi.org/10.1016/j.tics.2003.11.003
    Back to article
  22. Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6(6), 65–70. https://www.jstor.org/stable/4615733
    Back to article
  23. Huys, Q. J. M., Maia, T. V., & Frank, M. J. (2016). Computational psychiatry as a bridge from neuroscience to clinical applications. Nature Neuroscience, 19, 404–413.
    Back to article
  24. Ito, M., Noda, K., Hoshino, Y., & Tani, J. (2006). Dynamic and interactive generation of object handling behaviors by a small humanoid robot using a dynamic neural network model. Neural Networks, 19, 323–337. https://doi.org/10.1016/j.neunet.2006.02.007
    Back to article
  25. Jiruska, P., Csicsvari, J., Powell, A. D., Fox, J. E., Chang, W.-C., Vreugdenhil, M., … Jefferys, J. G. R. (2010). High-frequency network activity, global increase in neuronal activity, and synchrony expansion precede epileptic seizures in vitro. Journal of Neuroscience, 30, 5690–5701. https://doi.org/10.1523/JNEUROSCI.0535-10.2010
    Back to article
  26. Kramer, M. A., Truccolo, W., Eden, U. T., Lepage, K. Q., Hochberg, L. R., Eskandar, E. N., … Cash, S. S. (2012). Human seizures self-terminate across spatial scales via a critical transition. Proceedings of the National Academy of Sciences, 109, 21116–21121. https://doi.org/10.1073/pnas.1210047110
    Back to article
  27. Krichmar, J. L. (2013). A neurorobotic platform to test the influence of neuromodulatory signaling on anxious and curious behavior. Frontiers in Neurorobotics, 7, 1–17. https://doi.org/10.3389/fnbot.2013.00001
    Back to article
  28. Lawson, R. P., Mathys, C., & Rees, G. (2017). Adults with autism overestimate the volatility of the sensory environment. Nature Neuroscience, 20, 1293–1299. https://doi.org/10.1038/nn.4615
    Back to article
  29. Lawson, R. P., Rees, G., & Friston, K. J. (2014). An aberrant precision account of autism. Frontiers in Human Neuroscience, 8, 302. https://doi.org/10.3389/fnhum.2014.00302
    Back to article
  30. Leekam, S. R., Prior, M. R., & Uljarevic, M. (2011). Restricted and repetitive behaviors in autism spectrum disorders: A review of research in the last decade. Psychological Bulletin, 137, 562–593. https://doi.org/10.1037/a0023341
    Back to article
  31. Lenton, T. M., Myerscough, R. J., Marsh, R., Livina, V. N., Price, A. R., & Cox, S. J. (2009). Using GENIE to study a tipping point in the climate system. Philosophical Transactions of the Royal Society of London, Series A, 367, 871–884. https://doi.org/10.1098/rsta.2008.0171
    Back to article
  32. Lewis, M., & Kim, S. J. (2009). The pathophysiology of restricted repetitive behavior. Journal of Neurodevelopmental Disorders, 1, 114–132. https://doi.org/10.1007/s11689-009-9019-6
    Back to article
  33. Lord, C., & Jones, R. M. (2012). Re-thinking the classification of autism spectrum disorders. Journal of Child Psychology and Psychiatry, 53, 490–509. https://doi.org/10.1111/j.1469-7610.2012.02547.x
    Back to article
  34. Marocco, D., Cangelosi, A., Fischer, K., & Belpaeme, T. (2010). Grounding action words in the sensorimotor interaction with the world: Experiments with a simulated iCub humanoid robot. Frontiers in Neurorobotics, 4, 1–15. https://doi.org/10.3389/fnbot.2010.00007
    Back to article
  35. May, R. M. (1977). Thresholds and breakpoints in ecosystems with a multiplicity of stable states. Nature, 269, 471–477. https://doi.org/10.1038/269471a0
    Back to article
  36. Montague, P. R., Dolan, R. J., Friston, K. J., & Dayan, P. (2012). Computational psychiatry. Trends in Cognitive Sciences, 16(1), 72–80. https://doi.org/10.1016/j.tics.2011.11.018
    Back to article
  37. Murata, S., Namikawa, J., Arie, H., Sugano, S., & Tani, J. (2013). Learning to reproduce fluctuating time series by inferring their time-dependent stochastic properties: Application in robot learning via tutoring. IEEE Transactions on Autonomous Mental Development, 5, 298–310. https://doi.org/10.1109/TAMD.2013.2258019
    Back to article
  38. O’Loughlin, C., & Thagard, P. (2000). Autism and coherence: A computational model. Mind and Language, 15, 375–392. https://doi.org/10.1111/1468-0017.00140
    Back to article
  39. Palmer, C. J., Lawson, R. P., & Hohwy, J. (2017). Bayesian approaches to autism: Towards volatility, action, and behavior. Psychological Bulletin, 143, 521–542. https://doi.org/10.1037/bul0000097
    Back to article
  40. Pellicano, E., & Burr, D. (2012). When the world becomes “too real”: A Bayesian explanation of autistic perception. Trends in Cognitive Sciences, 16, 504–510. https://doi.org/10.1016/j.tics.2012.08.009
    Back to article
  41. Poljac, E., & Bekkering, H. (2012). A review of intentional and cognitive control in autism. Frontiers in Psychology, 3, 1–15. https://doi.org/10.3389/fpsyg.2012.00436
    Back to article
  42. Poljac, E., Hoofs, V., Princen, M. M., & Poljac, E. (2017). Understanding behavioural rigidity in autism spectrum conditions: The role of intentional control. Journal of Autism and Developmental Disorders, 47, 714–727. https://doi.org/10.1007/s10803-016-3010-3
    Back to article
  43. Powers, A. R., Mathys, C., & Corlett, P. R. (2017). Pavlovian conditioning induced hallucinations result from overweighting of perceptual priors. Science, 357, 596–600. https://doi.org/10.1126/science.aan3458
    Back to article
  44. Redish, A., & Gordon, J. (2016). Computational psychiatry: New perspectives on mental illness. Cambridge, MA: MIT Press.
    Back to article
  45. Rosenberg, A., Patterson, J. S., & Angelaki, D. E. (2015). A computational perspective on autism. Proceedings of the National Academy of Sciences of the United States of America, 112, 9158–9165. https://doi.org/10.1073/pnas.1510583112
    Back to article
  46. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536. https://doi.org/10.1038/323533a0
    Back to article
  47. Scheffer, M., Bascompte, J., Brock, W. A., Brovkin, V., Carpenter, S. R., Dakos, V., … Sugihara, G. (2009). Early-warning signals for critical transitions. Nature, 461, 53–59. https://doi.org/10.1038/nature08227
    Back to article
  48. Scheffer, M., Carpenter, S. R., Lenton, T. M., Bascompte, J., Brock, W., Dakos, V., … Vandermeer, J. (2012). Anticipating critical transitions. Science, 338, 344–348. https://doi.org/10.1126/science.1225244
    Back to article
  49. Shimojo, S. (2014). Postdiction: Its implications on visual awareness, hindsight, and sense of agency. Frontiers in Psychology, 5, 1–19. https://doi.org/10.3389/fpsyg.2014.00196
    Back to article
  50. Simmons, D. R., Robertson, A. E., McKay, L. S., Toal, E., McAleer, P., & Pollick, F. E. (2009). Vision in autism spectrum disorders. Vision Research, 49, 2705–2739. https://doi.org/10.1016/j.visres.2009.08.005
    Back to article
  51. Smith, L., & Gasser, M. (2005). The development of embodied cognition: Six lessons from babies. Artificial Life, 11(1–2), 13–29. https://doi.org/10.1162/1064546053278973
    Back to article
  52. Tani, J., Ito, M., & Sugita, Y. (2004). Self-organization of distributedly represented multiple behavior schemata in a mirror system: Reviews of robot experiments using RNNPB. Neural Networks, 17, 1273–1289. https://doi.org/10.1016/j.neunet.2004.05.007
    Back to article
  53. Teufel, C., & Fletcher, P. C. (2016). The promises and pitfalls of applying computational models to neurological and psychiatric disorders. Brain, 139, 2600–2608. https://doi.org/10.1093/brain/aww209
    Back to article
  54. Van de Cruys, S., Evers, K., Van der Hallen, R., Van Eylen, L., Boets, B., De-Wit, L., & Wagemans, J. (2014). Precise minds in uncertain worlds: Predictive coding in autism. Psychological Review, 121, 649–675. https://doi.org/10.1037/a0037665
    Back to article
  55. Van de Cruys, S., Van der Hallen, R., & Wagemans, J. (2017). Disentangling signal and noise in autism spectrum disorder. Brain and Cognition, 112, 78–83. https://doi.org/10.1016/j.bandc.2016.08.004
    Back to article
  56. van Boxtel, J. J. A., & Lu, H. (2013). A predictive coding perspective on autism spectrum disorders. Frontiers in Psychology, 4, 1–3. https://doi.org/10.3389/fpsyg.2013.00019
    Back to article
  57. van Schalkwyk, G. I., Volkmar, F. R., & Corlett, P. R. (2017). A predictive coding account of psychotic symptoms in autism spectrum disorder. Journal of Autism and Developmental Disorders, 47, 1323–1340. https://doi.org/10.1007/s10803-017-3065-9
    Back to article
  58. Yamashita, Y., & Tani, J. (2012). Spontaneous prediction error generation in schizophrenia. PLoS One, 7(5). https://doi.org/10.1371/journal.pone.0037843
    Back to article
  59. Zandt, F., Prior, M., & Kyrios, M. (2007). Repetitive behaviour in children with high functioning autism and obsessive compulsive disorder. Journal of Autism and Developmental Disorders, 37, 251–259. https://doi.org/10.1007/s10803-006-0158-2
    Back to article
DOI: https://doi.org/10.1162/CPSY_a_00019 | Journal eISSN: 2379-6227
Journal RSS Feed
Language: English
Submitted on: Dec 27, 2017
Accepted on: Jul 17, 2018
Published on: Dec 1, 2018
Published by: MIT Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
autism spectrum disorder,
neurorobotics,
recurrent neural network,
prediction error minimization,
sensory uncertainty,
online adaptation

© 2018 Hayato Idei, Shingo Murata, Yiwen Chen, Yuichi Yamashita, Jun Tani, Tetsuya Ogata, published by MIT Press
This work is licensed under the Creative Commons Attribution 4.0 License.

Previous articleVolume 2 (2018): Issue 0Next article