Cognitive, Affective, & Behavioral Neuroscience

, Volume 14, Issue 3, pp 902–911 | Cite as

Why do you fear the bogeyman? An embodied predictive coding model of perceptual inference



Why are we scared by nonperceptual entities such as the bogeyman, and why does the bogeyman only visit us during the night? Why does hearing a window squeaking in the night suggest to us the unlikely idea of a thief or a killer? And why is this more likely to happen after watching a horror movie? To answer these and similar questions, we need to put mind and body together again and consider the embodied nature of perceptual and cognitive inference. Predictive coding provides a general framework for perceptual inference; I propose to extend it by including interoceptive and bodily information. The resulting embodied predictive coding inference permits one to compare alternative hypotheses (e.g., is the sound I hear generated by a thief or the wind?) using the same inferential scheme as in predictive coding, but using both sensory and interoceptive information as evidence, rather than just considering sensory events. If you hear a window squeaking in the night after watching a horror movie, you may consider plausible a very unlikely hypothesis (e.g., a thief, or even the bogeyman) because it explains both what you sense (e.g., the window squeaking in the night) and how you feel (e.g., your high heart rate). The good news is that the inference that I propose is fully rational and gives minds and bodies equal dignity. The bad news is that it also gives an embodiment to the bogeyman, and a reason to fear it.


Embodied predictive coding Perceptual inference Decision-making Interoception 


  1. Anderson, A. K., & Phelps, E. A. (2001). Lesions of the human amygdala impair enhanced perception of emotionally salient events. Nature, 411, 305–309. doi:10.1038/35077083 PubMedCrossRefGoogle Scholar
  2. Barsalou, L. W. (2008). Grounded cognition. Annual Review of Psychology, 59, 617–645. doi:10.1146/annurev.psych.59.103006.093639 PubMedCrossRefGoogle Scholar
  3. Carhart-Harris, R. L., & Friston, K. J. (2010). The default-mode, ego-functions and free-energy: A neurobiological account of Freudian ideas. Brain, 133, 1265–1283. doi:10.1093/brain/awq010 PubMedCentralPubMedCrossRefGoogle Scholar
  4. Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36, 181–204. doi:10.1017/S0140525X12000477 PubMedCrossRefGoogle Scholar
  5. Damasio, A. (2000). The feeling of what happens: Body and emotion in the making of consciousness. New York, NY: Harcourt Brace.Google Scholar
  6. Dayan, P., Hinton, G. E., Neal, R. M., & Zemel, R. S. (1995). The Helmholtz machine. Neural Computation, 7, 889–904.PubMedCrossRefGoogle Scholar
  7. Ernst, M. O., & Bülthoff, H. H. (2004). Merging the senses into a robust percept. Trends in Cognitive Sciences, 8, 162–169. doi:10.1016/j.tics.2004.02.002 PubMedCrossRefGoogle Scholar
  8. Feldman, H., & Friston, K. J. (2010). Attention, uncertainty, and free-energy. Frontiers in Human Neuroscience, 4, 215. doi:10.3389/fnhum.2010.00215 PubMedCentralPubMedCrossRefGoogle Scholar
  9. Friston, K. (2005). A theory of cortical responses. Philosophical Transactions of the Royal Society B, 360, 815–836. doi:10.1098/rstb.2005.1622 CrossRefGoogle Scholar
  10. Friston, K. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11, 127–138. doi:10.1038/nrn2787 PubMedCrossRefGoogle Scholar
  11. Friston, K., Adams, R. A., Perrinet, L., & Breakspear, M. (2012). Perceptions as hypotheses: Saccades as experiments. Frontiers in Psychology, 3, 151. doi:10.3389/fpsyg.2012.00151 PubMedCentralPubMedGoogle Scholar
  12. Friston, K., Daunizeau, J., & Kiebel, S. J. (2009). Reinforcement learning or active inference? PLoS ONE, 4, e6421. doi:10.1371/journal.pone.0006421 PubMedCentralPubMedCrossRefGoogle Scholar
  13. Garrett, A. S., & Maddock, R. J. (2001). Time course of the subjective emotional response to aversive pictures: Relevance to fMRI studies. Psychiatry Research, 108, 39–48.PubMedCrossRefGoogle Scholar
  14. Grau-Moya, J., Hez, E., Pezzulo, G., & Braun, D. A. (2013). The effect of model uncertainty on cooperation in sensorimotor interactions. Journal of the Royal Society Interface, 10, 10130554. doi:10.1098/rsif.2013.0554 CrossRefGoogle Scholar
  15. Grau-Moya, J., Ortega, P. A., & Braun, D. A. (2012). Risk-sensitivity in Bayesian sensorimotor integration. PLoS Computational Biology, 8, e1002698. doi:10.1371/journal.pcbi.1002698 PubMedCentralPubMedCrossRefGoogle Scholar
  16. Grush, R. (2004). The emulation theory of representation: Motor control, imagery, and perception. Behavioral and Brain Sciences, 27, 377–396.PubMedGoogle Scholar
  17. Halloy, A. (2012). Gods in the Flesh: Learning Emotions in the. Xangô Possession Cult (Brazil). Ethnos: Journal of Anthropology, 77, 177-202. doi:10.1080/00141844.2011.586465
  18. Helmholtz, H. von. (1962). Concerning the perceptions in general. In J. P. C. Southall (Ed. and Trans.), Helmholtz’s Treatise on physiological optics (Vol. 3). New York, NY: Dover. (Original work published 1866).Google Scholar
  19. Hinton, G. E. (2007a). Learning multiple layers of representation. Trends in Cognitive Sciences, 11, 428–434. doi:10.1016/j.tics.2007.09.004 PubMedCrossRefGoogle Scholar
  20. Hinton, G. E. (2007b). To recognize shapes, first learn to generate images. Progress in Brain Research, 165, 535–547. doi:10.1016/S0079-6123(06)65034-6 PubMedCrossRefGoogle Scholar
  21. Hirstein, W., & Ramachandran, V. S. (1997). Capgras syndrome: A novel probe for understanding the neural representation of the identity and familiarity of persons. Proceedings of the Royal Society B, 264, 437–444. doi:10.1098/rspb.1997.0062 PubMedCentralPubMedCrossRefGoogle Scholar
  22. Hohwy, J., Roepstorff, A., & Friston, K. (2008). Predictive coding explains binocular rivalry: An epistemological review. Cognition, 108, 687–701. doi:10.1016/j.cognition.2008.05.010 PubMedCrossRefGoogle Scholar
  23. James, W. (1890). The principles of psychology. New York, NY: Henry Holt.CrossRefGoogle Scholar
  24. Kahneman, D. (2003). A perspective on judgment and choice: Mapping bounded rationality. American Psychologist, 58, 697–720. doi:10.1037/0003-066X.58.9.697 PubMedCrossRefGoogle Scholar
  25. Kiebel, S. J., Daunizeau, J., & Friston, K. J. (2008). A hierarchy of time-scales and the brain. PLoS Computational Biology, 4, e1000209. doi:10.1371/journal.pcbi.1000209 PubMedCentralPubMedCrossRefGoogle Scholar
  26. Koller, D., & Friedman, N. (2009). Probabilistic graphical models: Principles and techniques. Cambridge, MA: MIT Press.Google Scholar
  27. Machens, C. K., Gollisch, T., Kolesnikova, O., & Herz, A. V. M. (2005). Testing the efficiency of sensory coding with optimal stimulus ensembles. Neuron, 47, 447–456. doi:10.1016/j.neuron.2005.06.015 PubMedCrossRefGoogle Scholar
  28. Montague, P. R., Dolan, R. J., Friston, K. J., & Dayan, P. (2012). Computational psychiatry. Trends in Cognitive Sciences, 16, 72–80. doi:10.1016/j.tics.2011.11.018 PubMedCentralPubMedCrossRefGoogle Scholar
  29. Montague, P. R., & King-Casas, B. (2007). Efficient statistics, common currencies and the problem of reward-harvesting. Trends in Cognitive Sciences, 11, 514–519. doi:10.1016/j.tics.2007.10.002 PubMedCrossRefGoogle Scholar
  30. Pezzulo, G. (2008). Coordinating with the future: The anticipatory nature of representation. Minds and Machines, 18, 179–225. doi:10.1007/s11023-008-9095-5 CrossRefGoogle Scholar
  31. Pezzulo, G. (2011). Grounding procedural and declarative knowledge in sensorimotor anticipation. Mind and Language, 26, 78–114.CrossRefGoogle Scholar
  32. Pezzulo, G. (2012). An active inference view of cognitive control. Frontiers in Theoretical and Philosophical Psychology, 3, 478. doi:10.3389/fpsyg.2012.00478 Google Scholar
  33. Pezzulo, G., Barsalou, L. W., Cangelosi, A., Fischer, M. H., McRae, K., & Spivey, M. J. (2011). The mechanics of embodiment: A dialog on embodiment and computational modeling. Frontiers in Cognition, 2(5), 1–21. doi:10.3389/fpsyg.2011.00005 Google Scholar
  34. Pezzulo, G., Barsalou, L. W., Cangelosi, A., Fischer, M. H., McRae, K., & Spivey, M. J. (2013). Computational grounded cognition: A new alliance between grounded cognition and computational modeling. Frontiers in Psychology, 3, 612. doi:10.3389/fpsyg.2012.00612 PubMedCentralPubMedCrossRefGoogle Scholar
  35. Pezzulo, G., & Castelfranchi, C. (2009). Thinking as the control of imagination: a conceptual framework for goal-directed systems. Psychological Research, 73, 559–577.Google Scholar
  36. Rao, R. P. N., & Ballard, D. H. (1999). Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience, 2, 79–87. doi:10.1038/4580 PubMedCrossRefGoogle Scholar
  37. Roy, D. (2005). Semiotic schemas: A framework for grounding language in action and perception. Artificial Intelligence, 167, 170–205.CrossRefGoogle Scholar
  38. Seth, A. K., Suzuki, K., & Critchley, H. D. (2012). An interoceptive predictive coding model of conscious presence. Frontiers in Psychology, 2, 395. doi:10.3389/fpsyg.2011.00395 PubMedCentralPubMedCrossRefGoogle Scholar
  39. Spivey, M. (2007). The continuity of mind. Oxford, UK: Oxford University Press.Google Scholar
  40. Stanovich, K. E., & West, R. F. (2000). Individual differences in reasoning: Implications for the rationality debate? Behavioral and Brain Sciences, 23, 645–665. disc. 665–726.PubMedCrossRefGoogle Scholar
  41. Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011). How to grow a mind: Statistics, structure, and abstraction. Science, 331, 1279–1285. doi:10.1126/science.1192788 PubMedCrossRefGoogle Scholar
  42. Wacongne, C., Changeux, J.-P., & Dehaene, S. (2012). A neuronal model of predictive coding accounting for the mismatch negativity. Journal of Neuroscience, 32, 3665–3678. doi:10.1523/JNEUROSCI.5003-11.2012 PubMedCrossRefGoogle Scholar
  43. Wilson-Mendenhall, C. D., Barrett, L. F., Simmons, W. K., & Barsalou, L. W. (2011). Grounding emotion in situated conceptualization. Neuropsychologia, 49, 1105–1127. doi:10.1016/j.neuropsychologia.2010.12.032 PubMedCentralPubMedCrossRefGoogle Scholar

Copyright information

© Psychonomic Society, Inc. 2013

Authors and Affiliations

  1. 1.National Research CouncilInstitute of Cognitive Sciences and TechnologiesRomeItaly

Personalised recommendations