Biological Cybernetics

, Volume 109, Issue 4–5, pp 453–467 | Cite as

The intentional stance as structure learning: a computational perspective on mindreading

  • Haris Dindo
  • Francesco Donnarumma
  • Fabian Chersi
  • Giovanni Pezzulo
Original Paper

Abstract

Recent theories of mindreading explain the recognition of action, intention, and belief of other agents in terms of generative architectures that model the causal relations between observables (e.g., observed movements) and their hidden causes (e.g., action goals and beliefs). Two kinds of probabilistic generative schemes have been proposed in cognitive science and robotics that link to a “theory theory” and “simulation theory” of mindreading, respectively. The former compares perceived actions to optimal plans derived from rationality principles and conceptual theories of others’ minds. The latter reuses one’s own internal (inverse and forward) models for action execution to perform a look-ahead mental simulation of perceived actions. Both theories, however, leave one question unanswered: how are the generative models – including task structure and parameters – learned in the first place? We start from Dennett’s “intentional stance” proposal and characterize it within generative theories of action and intention recognition. We propose that humans use an intentional stance as a learning bias that sidesteps the (hard) structure learning problem and bootstraps the acquisition of generative models for others’ actions. The intentional stance corresponds to a candidate structure in the generative scheme, which encodes a simplified belief-desire folk psychology and a hierarchical intention-to-action organization of behavior. This simple structure can be used as a proxy for the “true” generative structure of others’ actions and intentions and is continuously grown and refined – via state and parameter learning – during interactions. In turn – as our computational simulations show – this can help solve mindreading problems and bootstrap the acquisition of useful causal models of both one’s own and others’ goal-directed actions.

Keywords

Intentional stance Generative model Online learning Structure learning Mindreading 

References

  1. 1.
    Aarts H, Gollwitzer P, Hassin R (2004) Goal contagion: perceiving is for pursuing. J Pers Soc Psychol 87:23–37CrossRefPubMedGoogle Scholar
  2. 2.
    Acuna DE, Schrater P (2010) Structure learning in human sequential decision-making. PLoS Comput Biol 6(12):e1001003. doi:10.1371/journal.pcbi.1001003 PubMedCentralCrossRefPubMedGoogle Scholar
  3. 3.
    Arulampalam MS, Maskell S, Gordon N, Clapp T (2002) A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans Signal Process 50(2):174–188CrossRefGoogle Scholar
  4. 4.
    Baker C, Tenenbaum J, Saxe R (2006) Bayesian models of human action understanding. In: Weiss Y, Schölkopf B, Platt J (eds) Advances in neural information processing systems 18. MIT Press, Cambridge, pp 99–106Google Scholar
  5. 5.
    Baker CL, Saxe R, Tenenbaum JB (2009) Action understanding as inverse planning. Cognition 113(3):329–349. doi:10.1016/j.cognition.2009.07.005 CrossRefPubMedGoogle Scholar
  6. 6.
    Baker CL, Saxe RR, Tenenbaum JB (2011) Bayesian theory of mind: modeling joint belief-desire attribution. In: Proceedings of the thirty-second annual conference of the cognitive science societyGoogle Scholar
  7. 7.
    Bauer E, Koller D, Singer Y (1997) Update rules for parameter estimation in Bayesian networks. In: Proceedings of the thirteenth conference on uncertainty in artificial intelligence. Morgan Kaufmann Publishers Inc, pp 3–13Google Scholar
  8. 8.
    Becchio C, Manera V, Sartori L, Cavallo A, Castiello U (2012) Grasping intentions: from thought experiments to empirical evidence. Front Hum Neurosci 6:117. doi:10.3389/fnhum.2012.00117 PubMedCentralCrossRefPubMedGoogle Scholar
  9. 9.
    Bishop CM (2006) Pattern recognition and machine learning. Springer, New YorkGoogle Scholar
  10. 10.
    Blakemore SJ, Frith C (2005) The role of motor contagion in the prediction of action. Neuropsychologia 43(2):260–267. doi:10.1016/j.neuropsychologia.2004.11.012 CrossRefPubMedGoogle Scholar
  11. 11.
    Braun DA, Mehring C, Wolpert DM (2010) Structure learning in action. Behav Brain Res 206(2):157–165. doi:10.1016/j.bbr.2009.08.031 PubMedCentralCrossRefPubMedGoogle Scholar
  12. 12.
    Carroll CD, Kemp C (2013) Hypothesis space checking in intuitive reasoning. In: Proceedings of the 35th annual conference of the cognitive science society. Cognitive science societyGoogle Scholar
  13. 13.
    Chella A, Dindo H, Infantino I (2007) Imitation learning and anchoring through conceptual spaces. Appl Artif Intell 21(4–5):343–359. doi:10.1080/08839510701252619 CrossRefGoogle Scholar
  14. 14.
    Chersi F (2011) Neural mechanisms and models underlying joint action. Exp Brain Res 211(3–4):643–653. doi:10.1007/s00221-011-2690-3 CrossRefPubMedGoogle Scholar
  15. 15.
    Chersi F (2012) Learning through imitation: a biological approach to robotics. IEEE Trans Auton Ment Dev 4(3):204–214. doi:10.1109/TAMD.2012.2200250 CrossRefGoogle Scholar
  16. 16.
    Chickering DM, Geiger D, Heckerman D et al (1994) Learning Bayesian networks is NP-hard. Tech. rep, CiteseerGoogle Scholar
  17. 17.
    Cho HC, Fadali SM (2006) Online estimation of dynamic Bayesian network parameter. In: IEEE International joint conference on neural networks, 2006, IJCNN’06, pp 3363–3370Google Scholar
  18. 18.
    Cohen I, Bronstein A, Cozman FG (2001) Adaptive online learning of Bayesian network parameters. University of Sao Paulo, BrasilGoogle Scholar
  19. 19.
    Csibra G (2007) Action mirroring and action understanding: an alternative account. In: Haggard P, Rosetti Y, Kawato M (eds) Sensorimotor foundations of higher cognition: attention and performance XXII. Oxford University Press, OxfordGoogle Scholar
  20. 20.
    Csibra G, Gergely G (2007) Obsessed with goals: functions and mechanisms of teleological interpretation of actions in humans. Acta Psychol 124:60–78CrossRefGoogle Scholar
  21. 21.
    Csibra G, Gergely G (2009) Natural pedagogy. Trends Cogn Sci 13(4):148–153. doi:10.1016/j.tics.2009.01.005 CrossRefPubMedGoogle Scholar
  22. 22.
    Cuijpers RH, van Schie HT, Koppen M, Erlhagen W, Bekkering H (2006) Goals and means in action observation: a computational approach. Neural Netw 19(3):311–322. doi:10.1016/j.neunet.2006.02.004 CrossRefPubMedGoogle Scholar
  23. 23.
    Dearden A, Demiris Y (2005) Learning forward models for robotics. In: Proceedings of IJCAI-2005. Edinburgh, pp 1440–1445Google Scholar
  24. 24.
    Demiris J, Hayes G (2002) Imitation as a dual-route process featuring predictive and learning components: a biologically plausible computational model. Imitation in animals and artifacts. MIT Press, Cambridge, pp 327–361Google Scholar
  25. 25.
    Demiris Y (2007) Prediction of intent in robotics and multi-agent systems. Cogn Process 8(3):151–158CrossRefPubMedGoogle Scholar
  26. 26.
    Demiris Y, Khadhouri B (2005) Hierarchical attentive multiple models for execution and recognition (Hammer). Rob Auton Syst J 54:361–369CrossRefGoogle Scholar
  27. 27.
    Dennett D (1987) The intentional stance. MIT Press, CambridgeGoogle Scholar
  28. 28.
    Dindo H, Schillaci G (2010) An adaptive probabilistic graphical model for representing skills in pbd settings. In: Proceedings of the 5th ACM/IEEE international conference on human-robot interaction, pp 89–90Google Scholar
  29. 29.
    Dindo H, Schillaci G (2010) An adaptive probabilistic approach to goal-level imitation learning. In: Proceedings of the 2010 IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 4452–4457. doi:10.1109/IROS.2010.5654440
  30. 30.
    Dindo H, Zambuto D, Pezzulo G (2011) Motor simulation via coupled internal models using sequential Monte Carlo. Proceedings of IJCAI 2011:2113–2119Google Scholar
  31. 31.
    Doucet A, De Freitas N, Gordon N et al (2001) Sequential Monte Carlo methods in practice, vol 1. Springer, New YorkCrossRefGoogle Scholar
  32. 32.
    Fadiga L, Fogassi L, Pavesi G, Rizzolatti G (1995) Motor facilitation during action observation: a magnetic stimulation study. J Neurophysiol 73:2608–2611PubMedGoogle Scholar
  33. 33.
    Friston K (2005) A theory of cortical responses. Philos Trans R Soc Lond B Biol Sci 360(1456):815–836. doi:10.1098/rstb.2005.1622 PubMedCentralCrossRefPubMedGoogle Scholar
  34. 34.
    Friston K, Mattout J, Kilner J (2011) Action understanding and active inference. Biol Cybern 104(1–2):137–160. doi:10.1007/s00422-011-0424-z PubMedCentralCrossRefPubMedGoogle Scholar
  35. 35.
    Friston KJ, Daunizeau J, Kilner J, Kiebel SJ (2010) Action and behavior: a free-energy formulation. Biol Cybern 102(3):227–260. doi:10.1007/s00422-010-0364-z CrossRefPubMedGoogle Scholar
  36. 36.
    Frith CD, Frith U (2006) How we predict what other people are going to do. Brain Res 1079(1):36–46. doi:10.1016/j.brainres.2005.12.126. http://www.sciencedirect.com/science/article/B6SYR-4JCCJXS-3/2/ec6adb1fa305f3d6791ac45f451cf63d
  37. 37.
    Frith CD, Frith U (2008) Implicit and explicit processes in social cognition. Neuron 60(3):503–510. doi:10.1016/j.neuron.2008.10.032 CrossRefPubMedGoogle Scholar
  38. 38.
    Frith U, Frith C (2010) The social brain: allowing humans to boldly go where no other species has been. Philos Trans R Soc Lond B Biol Sci 365(1537):165–176. doi:10.1098/rstb.2009.0160 PubMedCentralCrossRefPubMedGoogle Scholar
  39. 39.
    Fritzke B et al (1995) A growing neural gas network learns topologies. Adv Neural Inf Process Syst 7:625–632Google Scholar
  40. 40.
    Gallagher HL, Jack AI, Roepstorff A, Frith CD (2002) Imaging the intentional stance in a competitive game. Neuroimage 16(3 Pt 1):814–821CrossRefPubMedGoogle Scholar
  41. 41.
    Gallese V, Fadiga L, Fogassi L, Rizzolatti G (1996) Action recognition in the premotor cortex. Brain 119:593–609CrossRefPubMedGoogle Scholar
  42. 42.
    Garrod S, Pickering MJ (2009) Joint action, interactive alignment, and dialog. Top Cogn Sci 1(2):292–304. doi:10.1111/j.1756-8765.2009.01020.x CrossRefPubMedGoogle Scholar
  43. 43.
    Gergely G, Csibra G (2003) Teleological reasoning in infancy: the naive theory of rational action. Trends Cogn Sci 7:287–292CrossRefPubMedGoogle Scholar
  44. 44.
    Gershman SJ, Niv Y (2010) Learning latent structure: carving nature at its joints. Curr Opin Neurobiol 20(2):251–256. doi:10.1016/j.conb.2010.02.008 PubMedCentralCrossRefPubMedGoogle Scholar
  45. 45.
    Gopnik A, Glymour C, Sobel DM, Schulz LE, Kushnir T, Danks D (2004) A theory of causal learning in children: causal maps and Bayes nets. Psychol Rev 111(1):3–32. doi:10.1037/0033-295X.111.1.3 CrossRefPubMedGoogle Scholar
  46. 46.
    Gopnik A, Meltzoff A (1997) Words, thoughts and theories. MIT Press, CambridgeGoogle Scholar
  47. 47.
    Grush R (2004) The emulation theory of representation: motor control, imagery, and perception. Behav Brain Sci 27(3):377–396PubMedGoogle Scholar
  48. 48.
    Hamilton AFdC, Grafton ST (2007) The motor hierarchy: from kinematics to goals and intentions. In: Haggard P, Rossetti Y, Kawato M (eds) Sensorimotor foundations of higher cognition. Oxford University Press, OxfordGoogle Scholar
  49. 49.
    Heckerman D (1998) A tutorial on learning with Bayesian networks. Springer, New YorkCrossRefGoogle Scholar
  50. 50.
    Heil L, van Pelt S, Kwisthout J, van Rooij I, Bekkering H (2014) Higher-level processes in the formation and application of associations during action understanding. Behav Brain Sci 37(02):202–203CrossRefPubMedGoogle Scholar
  51. 51.
    Hommel B, Musseler J, Aschersleben G, Prinz W (2001) The theory of event coding (tec): a framework for perception and action planning. Behav Brain Sci 24(5):849–878CrossRefPubMedGoogle Scholar
  52. 52.
    Jeannerod M (2001) Neural simulation of action: a unifying mechanism for motor cognition. NeuroImage 14:S103–S109CrossRefPubMedGoogle Scholar
  53. 53.
    Jeannerod M (2006) Motor cognition. Oxford University Press, OxfordCrossRefGoogle Scholar
  54. 54.
    Jockusch J, Ritter H (1999) An instantaneous topological mapping model for correlated stimuli. In: Proceedings of the international joint conference on neural networks, Washington (US), vol 1, pp 529–534Google Scholar
  55. 55.
    Kiebel SJ, von Kriegstein K, Daunizeau J, Friston KJ (2009) Recognizing sequences of sequences. PLoS Comput Biol 5(8):e1000464. doi:10.1371/journal.pcbi.1000464 PubMedCentralCrossRefPubMedGoogle Scholar
  56. 56.
    Kilner JM (2011) More than one pathway to action understanding. Trends Cogn Sci 15(8):352–357. doi:10.1016/j.tics.2011.06.005 PubMedCentralCrossRefPubMedGoogle Scholar
  57. 57.
    Kilner JM, Friston KJ, Frith CD (2007) Predictive coding: an account of the mirror neuron system. Cogn Process 8(3):159–166PubMedCentralCrossRefPubMedGoogle Scholar
  58. 58.
    Kohonen T (1998) The self-organizing map. Neurocomputing 21(1):1–6CrossRefGoogle Scholar
  59. 59.
    Koller D, Friedman N (2009) Probabilistic graphical models: principles and techniques. MIT Press, CambridgeGoogle Scholar
  60. 60.
    Levinson SC (2006) On the human “interaction engine”. In: Enfield NJ, Levinson SC (eds) Roots of human sociality: culture, cognition and interaction. Berg, Oxford, pp 39–69Google Scholar
  61. 61.
    Liang P, Klein D (2009) Online em for unsupervised models. In: Proceedings of human language technologies: the 2009 annual conference of the North American chapter of the association for computational linguistics, pp 611–619. Association for computational linguisticsGoogle Scholar
  62. 62.
    Meltzoff AN (2007) ’like me’: a foundation for social cognition. Dev Sci 10(1):126–134. doi:10.1111/j.1467-7687.2007.00574.x PubMedCentralCrossRefPubMedGoogle Scholar
  63. 63.
    Murphy KP (2002) Dynamic Bayesian networks: representation, inference and learning. Ph.D. thesis, UC Berkeley, Computer Science Division. http://www.worldcat.org/oclc/52827959
  64. 64.
    Murphy KP (2012) Machine learning: a probabilistic perspective. MIT Press, CambridgeGoogle Scholar
  65. 65.
    Neal RM, Hinton GE (1998) A view of the EM algorithm that justifies incremental, sparse, and other variants. In: Jordan MI (ed) Learning in graphical models. Kluwer Academic Publishers, Norwell, pp 355–368Google Scholar
  66. 66.
    Ognibene D, Wu Y, Lee K, Demiris Y (2013) Hierarchies for embodied action perception. In: Computational and robotic models of the hierarchical organization of behavior. Springer, pp 81–98Google Scholar
  67. 67.
    Pacherie E (2008) The phenomenology of action: a conceptual framework. Cognition 107:179–217CrossRefPubMedGoogle Scholar
  68. 68.
    Pearl J (2000) Causality: models, reasoning, and inference. Cambridge University Press, CambridgeGoogle Scholar
  69. 69.
    Pezzulo G (2008) Coordinating with the future: the anticipatory nature of representation. Minds Mach 18(2):179–225. doi:10.1007/s11023-008-9095-5 CrossRefGoogle Scholar
  70. 70.
    Pezzulo G (2011) Grounding procedural and declarative knowledge in sensorimotor anticipation. Mind Lang 26(1):78–114CrossRefGoogle Scholar
  71. 71.
    Pezzulo G (2012) The interaction engine: a common pragmatic competence across linguistic and non-linguistic interactions. IEEE Trans Auton Ment Dev 4(2):105–123CrossRefGoogle Scholar
  72. 72.
    Pezzulo G (2013) Studying mirror mechanisms within generative and predictive architectures for joint action. Cortex 49:2968–2969CrossRefPubMedGoogle Scholar
  73. 73.
    Pezzulo G, Candidi M, Dindo H, Barca L (2013) Action simulation in the human brain: twelve questions. New Ideas Psychol 31(3):270–290CrossRefGoogle Scholar
  74. 74.
    Pezzulo G, Dindo H (2011) What should i do next? Using shared representations to solve interaction problems. Exp Brain Res 211(3):613–630CrossRefPubMedGoogle Scholar
  75. 75.
    Pezzulo G, Donnarumma F, Dindo H (2013) Human sensorimotor communication: a theory of signaling in online social interactions. PLoS One 8(11):e79876. doi:10.1371/journal.pone.0079876 PubMedCentralCrossRefPubMedGoogle Scholar
  76. 76.
    Pezzulo G, van der Meer MA, Lansink CS, Pennartz CM (2014) Internally generated sequences in learning and executing goal-directed behavior. Trends Cogn Sci 18(12):647–657CrossRefPubMedGoogle Scholar
  77. 77.
    Pickering MJ, Garrod S (2007) Do people use language production to make predictions during comprehension? Trends Cogn Sci 11(3):105–110CrossRefPubMedGoogle Scholar
  78. 78.
    Ramirez M, Geffner H (2010) Probabilistic plan recognition using off-the-shelf classical planners. In: Proceedings of AAAI-10. Atlanta, USAGoogle Scholar
  79. 79.
    Ramírez M, Geffner H (2011) Goal recognition over pomdps: inferring the intention of a pomdp agent. In: IJCAI, pp 2009–2014Google Scholar
  80. 80.
    Schaal S (1999) Is imitation learning the route to humanoid robots? Trends Cogn Sci 3:233–242CrossRefPubMedGoogle Scholar
  81. 81.
    Sebanz N, Bekkering H, Knoblich G (2006) Joint action: bodies and minds moving together. Trends Cogn Sci 10(2):70–76. doi:10.1016/j.tics.2005.12.009 CrossRefPubMedGoogle Scholar
  82. 82.
    Shadmehr R, Mussa-Ivaldi S (2012) Biological learning and control: how the brain builds representations, predicts events, and makes decisions. MIT Press, CambridgeCrossRefGoogle Scholar
  83. 83.
    Skinner BF (1948) Superstition in the pigeon. J Exp Psychol 38(2):168–172CrossRefPubMedGoogle Scholar
  84. 84.
    Teh YW, Jordan MI (2010) Hierarchical Bayesian nonparametric models with applications. Bayesian Nonparametrics. Cambridge University Press, pp 158–207Google Scholar
  85. 85.
    Tenenbaum JB, Kemp C, Griffiths TL, Goodman ND (2011) How to grow a mind: statistics, structure, and abstraction. Science 331(6022):1279–1285. doi:10.1126/science.1192788 CrossRefPubMedGoogle Scholar
  86. 86.
    Todorov E, Jordan MI (2002) Optimal feedback control as a theory of motor coordination. Nat Neurosci 5(11):1226–1235. doi:10.1038/nn963 CrossRefPubMedGoogle Scholar
  87. 87.
    Tomasello M, Carpenter M, Call J, Behne T, Moll H (2005) Understanding and sharing intentions: the origins of cultural cognition. Behav Brain Sci 28(5):675–691. doi:10.1017/S0140525X05000129 PubMedGoogle Scholar
  88. 88.
    Ullman T, Tenenbaum J, Baker C, Macindoe O, Evans O, Goodman N et al. (2009) Help or hinder: Bayesian models of social goal inference. In: Proceedings of neural information processing systemsGoogle Scholar
  89. 89.
    Vasquez D, Fraichard T, Laugier C (2009) Incremental learning of statistical motion patterns with growing hidden markov models. IEEE Trans Intell Trans Syst 10(3):403–416CrossRefGoogle Scholar
  90. 90.
    Wilson M, Knoblich G (2005) The case for motor involvement in perceiving conspecifics. Psychol Bull 131:460–473CrossRefPubMedGoogle Scholar
  91. 91.
    Wolpert DM, Doya K, Kawato M (2003) A unifying computational framework for motor control and social interaction. Philos Trans R Soc Lond B Biol Sci 358(1431):593–602. doi:10.1098/rstb.2002.1238 PubMedCentralCrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Haris Dindo
    • 1
  • Francesco Donnarumma
    • 2
  • Fabian Chersi
    • 3
  • Giovanni Pezzulo
    • 2
  1. 1.RoboticsLab, Polytechnic School (DICGIM)University of PalermoPalermoItaly
  2. 2.Institute of Cognitive Sciences and TechnologiesNational Research CouncilRomeItaly
  3. 3.Institute of Cognitive NeuroscienceUniversity College LondonLondonUK

Personalised recommendations