Hostname: page-component-76fb5796d-2lccl Total loading time: 0 Render date: 2024-04-27T00:58:21.129Z Has data issue: false hasContentIssue false
  • English
  • Français

The role of arousal in predictive coding

Published online by Cambridge University Press:  05 January 2017

Fernando Ferreira-Santos
Fernando Ferreira-Santos*
Affiliation:
Laboratory of Neuropsychophysiology, Faculty of Psychology and Education Sciences, University of Porto, 4200-135 Porto, Portugalfrsantos@fpce.up.pt
Get access Rights & Permissions [Opens in a new window]

Abstract

Within a predictive coding approach, the arousal/norepinephrine effects described by the GANE (glutamate amplifies noradrenergic effects) model seem to modulate the precision attributed to prediction errors, favoring the selective updating of predictive models with larger prediction errors. However, to explain how arousal effects are triggered, it is likely that different kinds of prediction errors (including interoceptive/affective) need to be considered.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 39 , 2016 , e207
Copyright
Copyright © Cambridge University Press 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Barrett, L. F. & Bar, M. (2009) See it with feeling: Affective predictions during object perception. Philosophical Transactions of the Royal Society B: Biological Sciences 364:1325–34. doi: 10.1098/rstb.2008.0312.Google Scholar
Barrett, L. F. & Simmons, W. K. (2015) Interoceptive predictions in the brain. Nature Reviews Neuroscience 16:419–29. doi: 10.1038/nrn3950.Google Scholar
Clark, A. (2013) Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences 36:181–253. doi: 10.1017/S0140525X12000477.Google Scholar
Dayan, P. & Yu, A. J. (2006) Phasic norepinephrine: A neural interrupt signal for unexpected events. Network: Computation in Neural Systems 17:335–50. doi: 10.1080/09548980601004024.Google Scholar
Friston, K. (2005) A theory of cortical responses. Philosophical Transactions of the Royal Society B: Biological Sciences 360:815–36. doi: 10.1098/rstb.2005.1622.Google Scholar
Friston, K. (2010) The free-energy principle: A unified brain theory? Nature Reviews Neuroscience 11(2):127–38.Google Scholar
Huang, Y. & Rao, R. P. N. (2011) Predictive coding. Wiley Interdisciplinary Reviews: Cognitive Science 2:580–93. doi: 10.1002/wcs.142.Google Scholar
Petersen, S. E. & Posner, M. I. (2012) The attention system of the human brain: 20 years after. Annual Review of Neuroscience 35:73–89. doi: 10.1146/annurev-neuro-062111-150525.CrossRefGoogle ScholarPubMed
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 2:79–97. doi: 10.1038/4580.Google Scholar
Schultz, W. & Dickinson, A. (2000) Neuronal coding of prediction errors. Annual Review in Neuroscience 23:473–500. doi: 10.1146/annurev.neuro.23.1.473.Google Scholar
Seth, A. K. (2013) Interoceptive inference, emotion, and the embodied self. Trends in Cognitive Sciences 17:565–73. doi: 10.1016/j.tics.2013.09.007.Google Scholar
Vogel, B. O., Shen, C. & Neuhaus, A. H. (2015a) Emotional context facilitates cortical prediction error responses. Human Brain Mapping 36(9):3641–52. doi: 10.1002/hbm.22868.Google Scholar