IDEAS home Printed from https://ideas.repec.org/a/plo/pbio00/1000167.html
   My bibliography  Save this article

Ready…Go: Amplitude of the fMRI Signal Encodes Expectation of Cue Arrival Time

Author

Listed:
  • Xu Cui
  • Chess Stetson
  • P Read Montague
  • David M Eagleman

Abstract

A neuroimaging study reveals novel insights into how the brain responds to an anticipated event, such as a starting gun or responding to a green light.What happens when the brain awaits a signal of uncertain arrival time, as when a sprinter waits for the starting pistol? And what happens just after the starting pistol fires? Using functional magnetic resonance imaging (fMRI), we have discovered a novel correlate of temporal expectations in several brain regions, most prominently in the supplementary motor area (SMA). Contrary to expectations, we found little fMRI activity during the waiting period; however, a large signal appears after the “go” signal, the amplitude of which reflects learned expectations about the distribution of possible waiting times. Specifically, the amplitude of the fMRI signal appears to encode a cumulative conditional probability, also known as the cumulative hazard function. The fMRI signal loses its dependence on waiting time in a “countdown” condition in which the arrival time of the go cue is known in advance, suggesting that the signal encodes temporal probabilities rather than simply elapsed time. The dependence of the signal on temporal expectation is present in “no-go” conditions, demonstrating that the effect is not a consequence of motor output. Finally, the encoding is not dependent on modality, operating in the same manner with auditory or visual signals. This finding extends our understanding of the relationship between temporal expectancy and measurable neural signals.Author Summary: Like the sprinter waiting for the starting pistol, all animals develop expectations about when events will occur in time. We explored the neural correlates of readiness and expectation using functional magnetic resonance imaging (fMRI), and found areas of the brain in which the fMRI signal remains at baseline during the waiting period and rises sharply after a cue to react (a “go” cue). Strikingly, the amplitude of the rise reflects a function of the probability of an event occurring at that time. The dependence on probability remains even in the absence of a motor act (that is, not pressing a button when the go cue appears). When the arrival time of the go cue is known in advance, the expectation-dependent signal disappears, indicating that this brain response reflects expectation, not simply elapsed time. These results match up with prior studies of expectation in the brain, with one important difference: previously, electrophysiology experiments showed that expectation is encoded by a build-up of spiking activity as the waiting period progresses, while our fMRI data reveal a signature of expectation that becomes apparent after the waiting concludes. We discuss the apparent mismatch between these different technologies for measuring expectation-related activity in the brain.

Suggested Citation

  • Xu Cui & Chess Stetson & P Read Montague & David M Eagleman, 2009. "Ready…Go: Amplitude of the fMRI Signal Encodes Expectation of Cue Arrival Time," PLOS Biology, Public Library of Science, vol. 7(8), pages 1-11, August.
  • Handle: RePEc:plo:pbio00:1000167
    DOI: 10.1371/journal.pbio.1000167
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1000167
    Download Restriction: no

    File URL: https://journals.plos.org/plosbiology/article/file?id=10.1371/journal.pbio.1000167&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pbio.1000167?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Nikos K. Logothetis, 2008. "What we can do and what we cannot do with fMRI," Nature, Nature, vol. 453(7197), pages 869-878, June.
    2. David A. Leopold, 2009. "Pre-emptive blood flow," Nature, Nature, vol. 457(7228), pages 387-388, January.
    3. Yutaka Komura & Ryoi Tamura & Teruko Uwano & Hisao Nishijo & Kimitaka Kaga & Taketoshi Ono, 2001. "Retrospective and prospective coding for predicted reward in the sensory thalamus," Nature, Nature, vol. 412(6846), pages 546-549, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Assaf Breska & Leon Y Deouell, 2017. "Neural mechanisms of rhythm-based temporal prediction: Delta phase-locking reflects temporal predictability but not rhythmic entrainment," PLOS Biology, Public Library of Science, vol. 15(2), pages 1-30, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. repec:hum:wpaper:sfb649dp2014-036 is not listed on IDEAS
    2. Alejandro Morán & Miguel C Soriano, 2018. "Improving the quality of a collective signal in a consumer EEG headset," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-21, May.
    3. Adam S. Tuzolele Mbuku, 2024. "Evolution of the concept of Homo Economicus in light of advances in Neuroeconomics: towards a more realistic model of economic decision-making [Evolution du concept de l'Homo Economicus à la lumièr," Post-Print hal-04564775, HAL.
    4. Pérez-Centeno, Victor, 2018. "Brain-driven entrepreneurship research: Expanded review and research agenda towards entrepreneurial enhancement," Working Papers 02/18, Institut für Mittelstandsforschung (IfM) Bonn.
    5. Olsen, Carmen & Gold, Anna, 2018. "Future research directions at the intersection between cognitive neuroscience research and auditors’ professional skepticism," Journal of Accounting Literature, Elsevier, vol. 41(C), pages 127-141.
    6. Masashi Hasegawa & Ziyan Huang & Ricardo Paricio-Montesinos & Jan Gründemann, 2024. "Network state changes in sensory thalamus represent learned outcomes," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    7. Eleonora Maggioni & Jorge Arrubla & Tracy Warbrick & Jürgen Dammers & Anna M Bianchi & Gianluigi Reni & Michela Tosetti & Irene Neuner & N Jon Shah, 2014. "Removal of Pulse Artefact from EEG Data Recorded in MR Environment at 3T. Setting of ICA Parameters for Marking Artefactual Components: Application to Resting-State Data," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-15, November.
    8. Daniella Laureiro-Martínez & Stefano Brusoni & Nicola Canessa & Maurizio Zollo, 2015. "Understanding the exploration–exploitation dilemma: An fMRI study of attention control and decision-making performance," Strategic Management Journal, Wiley Blackwell, vol. 36(3), pages 319-338, March.
    9. John A Clithero & Dharol Tankersley & Scott A Huettel, 2008. "Foundations of Neuroeconomics: From Philosophy to Practice," PLOS Biology, Public Library of Science, vol. 6(11), pages 1-6, November.
    10. Federico Rocchi & Carola Canella & Shahryar Noei & Daniel Gutierrez-Barragan & Ludovico Coletta & Alberto Galbusera & Alexia Stuefer & Stefano Vassanelli & Massimo Pasqualetti & Giuliano Iurilli & Ste, 2022. "Increased fMRI connectivity upon chemogenetic inhibition of the mouse prefrontal cortex," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    11. Hadi Vafaii & Francesca Mandino & Gabriel Desrosiers-Grégoire & David O’Connor & Marija Markicevic & Xilin Shen & Xinxin Ge & Peter Herman & Fahmeed Hyder & Xenophon Papademetris & Mallar Chakravarty , 2024. "Multimodal measures of spontaneous brain activity reveal both common and divergent patterns of cortical functional organization," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    12. Munro, Eileen & Musholt, Kristina, 2014. "Neuroscience and the risks of maltreatment," Children and Youth Services Review, Elsevier, vol. 47(P1), pages 18-26.
    13. Macauley Smith Breault & Pierre Sacré & Zachary B. Fitzgerald & John T. Gale & Kathleen E. Cullen & Jorge A. González-Martínez & Sridevi V. Sarma, 2023. "Internal states as a source of subject-dependent movement variability are represented by large-scale brain networks," Nature Communications, Nature, vol. 14(1), pages 1-20, December.
    14. Nicos Nicolaou & Phillip H. Phan & Ute Stephan, 2021. "The Biological Perspective in Entrepreneurship Research," Entrepreneurship Theory and Practice, , vol. 45(1), pages 3-17, January.
    15. Eva R. Pool & Wolfgang M. Pauli & Logan Cross & John P. O’Doherty, 2023. "Neural substrates of parallel devaluation-sensitive and devaluation-insensitive Pavlovian learning in humans," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    16. Luis Manssuer & Qiong Ding & Yashu Feng & Ruoqi Yang & Wei Liu & Bomin Sun & Shikun Zhan & Valerie Voon, 2024. "Reward recalibrates rule representations in human amygdala and hippocampus intracranial recordings," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    17. Angelika Dimoka & Paul A. Pavlou & Fred D. Davis, 2011. "Research Commentary ---NeuroIS: The Potential of Cognitive Neuroscience for Information Systems Research," Information Systems Research, INFORMS, vol. 22(4), pages 687-702, December.
    18. Domenic H. Cerri & Daniel L. Albaugh & Lindsay R. Walton & Brittany Katz & Tzu-Wen Wang & Tzu-Hao Harry Chao & Weiting Zhang & Randal J. Nonneman & Jing Jiang & Sung-Ho Lee & Amit Etkin & Catherine N., 2024. "Distinct neurochemical influences on fMRI response polarity in the striatum," Nature Communications, Nature, vol. 15(1), pages 1-23, December.
    19. Wei Li & Miao Wang & Wen Wen & Yue Huang & Xi Chen & Wenliang Fan & The Alzheimer's Disease Neuroimaging Initiative, 2018. "Neural Dynamics during Resting State: A Functional Magnetic Resonance Imaging Exploration with Reduction and Visualization," Complexity, Hindawi, vol. 2018, pages 1-10, June.
    20. Hubert, Mirja, 2010. "Does neuroeconomics give new impetus to economic and consumer research?," Journal of Economic Psychology, Elsevier, vol. 31(5), pages 812-817, October.
    21. Witt, Ulrich & Binder, Martin, 2013. "Disentangling motivational and experiential aspects of “utility” – A neuroeconomics perspective," Journal of Economic Psychology, Elsevier, vol. 36(C), pages 27-40.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pbio00:1000167. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosbiology (email available below). General contact details of provider: https://journals.plos.org/plosbiology/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.