Bayesian modeling of temporal expectations in the human brain

Antonino Visalli, Mariagrazia Capizzi, Ettore Ambrosini, Ilaria Mazzonetto, Antonino Vallesi

Research output: Contribution to journalArticle

Abstract

The brain predicts the timing of forthcoming events to optimize processes in response to them. Temporal predictions are driven by both our prior expectations on the likely timing of stimulus occurrence and the information conveyed by the passage of time. Specifically, such predictions can be described in terms of the hazard function, that is, the conditional probability that an event will occur, given it has not yet occurred. Events violating expectations cause surprise and often induce updating of prior expectations. While it is well-known that the brain is able to track the temporal hazard of event occurrence, the question of how prior temporal expectations are updated is still unsettled. Here we combined a Bayesian computational approach with brain imaging to map updating of temporal expectations in the human brain. Moreover, since updating is usually highly correlated with surprise, participants performed a task that allowed partially differentiating between the two processes. Results showed that updating and surprise differently modulated activity in areas belonging to two critical networks for cognitive control, the fronto-parietal (FPN) and the cingulo-opercular network (CON). Overall, these data provide a first computational characterization of the neural correlates associated with updating and surprise related to temporal expectation.

Original languageEnglish
Article number116097
JournalNeuroImage
Volume202
DOIs
Publication statusPublished - Nov 15 2019

Fingerprint

Brain
Bayes Theorem
Neuroimaging

Keywords

  • Bayesian brain
  • fMRI
  • Surprise
  • Temporal prediction
  • Updating

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

Cite this

Bayesian modeling of temporal expectations in the human brain. / Visalli, Antonino; Capizzi, Mariagrazia; Ambrosini, Ettore; Mazzonetto, Ilaria; Vallesi, Antonino.

In: NeuroImage, Vol. 202, 116097, 15.11.2019.

Research output: Contribution to journalArticle

Visalli, Antonino ; Capizzi, Mariagrazia ; Ambrosini, Ettore ; Mazzonetto, Ilaria ; Vallesi, Antonino. / Bayesian modeling of temporal expectations in the human brain. In: NeuroImage. 2019 ; Vol. 202.
@article{51d685bae7a44df2a1e949aca67d92f8,
title = "Bayesian modeling of temporal expectations in the human brain",
abstract = "The brain predicts the timing of forthcoming events to optimize processes in response to them. Temporal predictions are driven by both our prior expectations on the likely timing of stimulus occurrence and the information conveyed by the passage of time. Specifically, such predictions can be described in terms of the hazard function, that is, the conditional probability that an event will occur, given it has not yet occurred. Events violating expectations cause surprise and often induce updating of prior expectations. While it is well-known that the brain is able to track the temporal hazard of event occurrence, the question of how prior temporal expectations are updated is still unsettled. Here we combined a Bayesian computational approach with brain imaging to map updating of temporal expectations in the human brain. Moreover, since updating is usually highly correlated with surprise, participants performed a task that allowed partially differentiating between the two processes. Results showed that updating and surprise differently modulated activity in areas belonging to two critical networks for cognitive control, the fronto-parietal (FPN) and the cingulo-opercular network (CON). Overall, these data provide a first computational characterization of the neural correlates associated with updating and surprise related to temporal expectation.",
keywords = "Bayesian brain, fMRI, Surprise, Temporal prediction, Updating",
author = "Antonino Visalli and Mariagrazia Capizzi and Ettore Ambrosini and Ilaria Mazzonetto and Antonino Vallesi",
year = "2019",
month = "11",
day = "15",
doi = "10.1016/j.neuroimage.2019.116097",
language = "English",
volume = "202",
journal = "NeuroImage",
issn = "1053-8119",
publisher = "Academic Press Inc.",

}

TY - JOUR

T1 - Bayesian modeling of temporal expectations in the human brain

AU - Visalli, Antonino

AU - Capizzi, Mariagrazia

AU - Ambrosini, Ettore

AU - Mazzonetto, Ilaria

AU - Vallesi, Antonino

PY - 2019/11/15

Y1 - 2019/11/15

N2 - The brain predicts the timing of forthcoming events to optimize processes in response to them. Temporal predictions are driven by both our prior expectations on the likely timing of stimulus occurrence and the information conveyed by the passage of time. Specifically, such predictions can be described in terms of the hazard function, that is, the conditional probability that an event will occur, given it has not yet occurred. Events violating expectations cause surprise and often induce updating of prior expectations. While it is well-known that the brain is able to track the temporal hazard of event occurrence, the question of how prior temporal expectations are updated is still unsettled. Here we combined a Bayesian computational approach with brain imaging to map updating of temporal expectations in the human brain. Moreover, since updating is usually highly correlated with surprise, participants performed a task that allowed partially differentiating between the two processes. Results showed that updating and surprise differently modulated activity in areas belonging to two critical networks for cognitive control, the fronto-parietal (FPN) and the cingulo-opercular network (CON). Overall, these data provide a first computational characterization of the neural correlates associated with updating and surprise related to temporal expectation.

AB - The brain predicts the timing of forthcoming events to optimize processes in response to them. Temporal predictions are driven by both our prior expectations on the likely timing of stimulus occurrence and the information conveyed by the passage of time. Specifically, such predictions can be described in terms of the hazard function, that is, the conditional probability that an event will occur, given it has not yet occurred. Events violating expectations cause surprise and often induce updating of prior expectations. While it is well-known that the brain is able to track the temporal hazard of event occurrence, the question of how prior temporal expectations are updated is still unsettled. Here we combined a Bayesian computational approach with brain imaging to map updating of temporal expectations in the human brain. Moreover, since updating is usually highly correlated with surprise, participants performed a task that allowed partially differentiating between the two processes. Results showed that updating and surprise differently modulated activity in areas belonging to two critical networks for cognitive control, the fronto-parietal (FPN) and the cingulo-opercular network (CON). Overall, these data provide a first computational characterization of the neural correlates associated with updating and surprise related to temporal expectation.

KW - Bayesian brain

KW - fMRI

KW - Surprise

KW - Temporal prediction

KW - Updating

UR - http://www.scopus.com/inward/record.url?scp=85070884546&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85070884546&partnerID=8YFLogxK

U2 - 10.1016/j.neuroimage.2019.116097

DO - 10.1016/j.neuroimage.2019.116097

M3 - Article

AN - SCOPUS:85070884546

VL - 202

JO - NeuroImage

JF - NeuroImage

SN - 1053-8119

M1 - 116097

ER -