Abstract

Generative models, such as predictive coding, posit that perception results from a combination of sensory input and prior prediction, each weighted by its precision (inverse variance), with incongruence between these termed prediction error (deviation from prediction) or surprise (negative log probability of the sensory input). However, direct evidence for such a system, and the physiological basis of its computations, is lacking. Using an auditory stimulus whose pitch value changed according to specific rules, we controlled and separated the three key computational variables underlying perception, and discovered, using direct recordings from human auditory cortex, that surprise due to prediction violations is encoded by local field potential oscillations in the gamma band (>30 Hz), changes to predictions in the beta band (12-30 Hz), and that the precision of predictions appears to quantitatively relate to alpha band oscillations (8-12 Hz). These results confirm oscillatory codes for critical aspects of generative models of perception.

Article and author information

Author details

  1. William Sedley

    Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, United Kingdom
    For correspondence
    willsedley@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
  2. Phillip E Gander

    Human Brain Research Laboratory, University of Iowa, Iowa City, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Sukhbinder Kumar

    Institute of Neuroscience, Newcastle University, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  4. Christopher K Kovach

    Human Brain Research Laboratory, University of Iowa, Iowa City, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Hiroyuki Oya

    Human Brain Research Laboratory, University of Iowa, Iowa City, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Hiroto Kawasaki

    Human Brain Research Laboratory, University of Iowa, Iowa City, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Matthew A Howard

    Human Brain Research Laboratory, University of Iowa, Iowa City, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Timothy D Griffiths

    Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

Ethics

Human subjects: The study was approved by the University of Iowa Institutional Review Board, and with full informed written consent from all participants.

Reviewing Editor

  1. Andrew J King, University of Oxford, United Kingdom

Publication history

  1. Received: September 9, 2015
  2. Accepted: March 5, 2016
  3. Accepted Manuscript published: March 7, 2016 (version 1)
  4. Version of Record published: April 19, 2016 (version 2)

Copyright

© 2016, Sedley et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 4,225
    Page views
  • 953
    Downloads
  • 92
    Citations

Article citation count generated by polling the highest count across the following sources: Scopus, Crossref, PubMed Central.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. William Sedley
  2. Phillip E Gander
  3. Sukhbinder Kumar
  4. Christopher K Kovach
  5. Hiroyuki Oya
  6. Hiroto Kawasaki
  7. Matthew A Howard
  8. Timothy D Griffiths
(2016)
Neural Signatures of Perceptual Inference
eLife 5:e11476.
https://doi.org/10.7554/eLife.11476

Further reading

    1. Neuroscience
    Utsav Gyawali, David A Martin ... Donna Calu
    Research Article

    Midbrain and striatal dopamine signals have been extremely well characterized over the past several decades, yet novel dopamine signals and functions in reward learning and motivation continue to emerge. A similar characterization of real-time sub-second dopamine signals in areas outside of the striatum has been limited. Recent advances in fluorescent sensor technology and fiber photometry permit the measurement of dopamine binding correlates, which can divulge basic functions of dopamine signaling in non-striatal dopamine terminal regions, like the dorsal bed nucleus of the stria terminalis (dBNST). Here, we record GRABDA signals in the dBNST during a Pavlovian lever autoshaping task. We observe greater Pavlovian cue-evoked dBNST GRABDA signals in sign-tracking (ST) compared to goal-tracking/intermediate (GT/INT) rats and the magnitude of cue-evoked dBNST GRABDA signals decreases immediately following reinforcer-specific satiety. When we deliver unexpected rewards or omit expected rewards, we find that dBNST dopamine signals encode bidirectional reward prediction errors in GT/INT rats, but only positive prediction errors in ST rats. Since sign- and goal-tracking approach strategies are associated with distinct drug relapse vulnerabilities, we examined the effects of experimenter-administered fentanyl on dBNST dopamine associative encoding. Systemic fentanyl injections do not disrupt cue discrimination but generally potentiate dBNST dopamine signals. These results reveal multiple dBNST dopamine correlates of learning and motivation that depend on the Pavlovian approach strategy employed.

    1. Medicine
    2. Neuroscience
    Sachin Sharma, Russell Littman ... Olujimi A Ajijola
    Research Article Updated

    The cell bodies of postganglionic sympathetic neurons innervating the heart primarily reside in the stellate ganglion (SG), alongside neurons innervating other organs and tissues. Whether cardiac-innervating stellate ganglionic neurons (SGNs) exhibit diversity and distinction from those innervating other tissues is not known. To identify and resolve the transcriptomic profiles of SGNs innervating the heart, we leveraged retrograde tracing techniques using adeno-associated virus (AAV) expressing fluorescent proteins (GFP or Td-tomato) with single cell RNA sequencing. We investigated electrophysiologic, morphologic, and physiologic roles for subsets of cardiac-specific neurons and found that three of five adrenergic SGN subtypes innervate the heart. These three subtypes stratify into two subpopulations; high (NA1a) and low (NA1b and NA1c) neuropeptide-Y (NPY) -expressing cells, exhibit distinct morphological, neurochemical, and electrophysiologic characteristics. In physiologic studies in transgenic mouse models modulating NPY signaling, we identified differential control of cardiac responses by these two subpopulations to high and low stress states. These findings provide novel insights into the unique properties of neurons responsible for cardiac sympathetic regulation, with implications for novel strategies to target specific neuronal subtypes for sympathetic blockade in cardiac disease.