Bayesian modeling of temporal expectations in the human brain

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

doi:10.1016/j.neuroimage.2019.116097

Cited by 41 publications

(53 citation statements)

References 134 publications

(225 reference statements)

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“…In a previous study, we combined a Bayesian computational approach with functional magnetic resonance imaging (fMRI) in an attempt to understand neural correlates of temporal belief updating (Visalli et al, 2019). To that aim, we implemented a FP paradigm (adapted from O'Reilly et al, 2013) that was designed to identify separable neural correlates of belief updating and surprise.…”

Section: Introductionmentioning

confidence: 99%

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Electroencephalographic Correlates of Temporal Bayesian Belief Updating and Surprise

Visalli¹,

Capizzi²,

Ambrosini³

et al. 2020

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The brain predicts the timing of forthcoming events to optimize responses to them. Temporal predictions have been formalized in terms of the hazard function, which integrates prior beliefs on the likely timing of stimulus occurrence with information conveyed by the passage of time. However, how the human brain updates prior temporal beliefs is still elusive. Here we investigated electroencephalographic (EEG) signatures associated with Bayes-optimal updating of temporal beliefs. Given that updating usually occurs in response to surprising events, we sought to disentangle EEG correlates of updating from those associated with surprise. Twenty-six participants performed a temporal foreperiod task, which comprised a subset of surprising events not eliciting updating. EEG data were analyzed through a regression-based massive approach in the electrode and source space. Distinct late positive, centro-parietally distributed, event-related potentials (ERPs) were associated with surprise and belief updating in the electrode space. While surprise modulated the commonly observed P3b, updating was associated with a later and more sustained P3b-like waveform deflection. Results from source analyses revealed that surprise encoding comprises neural activity in the cingulo-opercular network (CON). These data provide evidence that temporal predictions are computed in a Bayesian manner, and that this is reflected in P3 modulations, akin to other cognitive domains. Overall, our study revealed that analyzing P3 modulations provides an important window into the Bayesian brain. Data and scripts are shared on OSF: https://osf.io/ckqa5/?view_only=f711e6f878784d4ab94f4b14b31eef46

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Section: Introductionmentioning

confidence: 99%

“…Of note, none of these previous Bayesian P3 studies was designed to differentiate between belief updating and surprise. Using the same task employed in our previous study (Visalli et al, 2019), we addressed this limitation in the present EEG study by applying an appropriate experimental manipulation.…”

Section: Introductionmentioning

confidence: 99%

Electroencephalographic Correlates of Temporal Bayesian Belief Updating and Surprise

Visalli¹,

Capizzi²,

Ambrosini³

et al. 2020

Preprint

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“…This recent finding supports the interpretation given in our schematic model of spatial processing (Cona & Scarpazza, 2019), according to which representations of space are prioritized by frontal operculum and the insula on the basis of the relevance of the individuals' goals and the salience of the external stimuli. As these regions are shared between the two domains, and based on the recent evidence in the literature (Dosenbach et al, 2008;Myers et al, 2017;Seeley et al, 2007;Visalli et al, 2019), such interpretation can be extended to time processes. The 'prioritizing'…”

Section: Common Neural Network For Space and Timementioning

confidence: 92%

“…More specifically, the anterior insula is involved in the transient identification of salient and/or relevant (either internal or external) stimuli in order to guide thoughts and behaviour (Seeley et al, 2007). Activity in frontal operculum has been instead associated with updating and prioritizing processes (Myers, Stokes, & Nobre, 2017;Visalli, Capizzi, Ambrosini, Mazzonetto, & Vallesi, 2019). In particular, an elegant study carried out by Visalli and collaborators (2019) was able to decouple the updating and surprise components of temporal expectations and to disentangle their related neural substrates.…”

Section: Common Neural Network For Space and Timementioning

confidence: 99%

From ATOM to GradiATOM: Cortical gradients support time and space processing as revealed by a meta-analysis of neuroimaging studies

Cona

Wiener

Scarpazza

2020

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According to the ATOM (A Theory Of Magnitude), formulated by Walsh more than fifteen years ago, there is a general system of magnitude in the brain that comprises regions, such as the parietal cortex, shared by space, time and other magnitudes (Walsh, 2003).The present meta-analysis of neuroimaging studies used the Activation Likelihood Estimation (ALE) method in order to determine the set of regions commonly activated in space and time processing and to establish the neural activations specific to each magnitude domain. Following PRISMA guidelines, we included in the analysis a total of 112 and 114 experiments, exploring space and time processing, respectively.We clearly identified the presence of a system of brain regions commonly recruited in both space and time and that includes: bilateral insula, the pre-supplementary motor area (SMA), the right frontal operculum and the intraparietal sulci. These regions might be the best candidates to form the core magnitude neural system. Surprisingly, along each of these regions but the insula, ALE values progressed in a cortical gradient from time to space. The SMA exhibited an anterior-posterior gradient, with space activating more-anterior regions (i.e., pre-SMA) and time activating moreposterior regions (i.e., SMA-proper). Frontal and parietal regions showed a dorsal-ventral gradient: space is mediated by dorsal frontal and parietal regions, and time recruits ventral frontal and parietal regions.Our study supports but also expands the ATOM theory. Therefore, we here re-named it the 'GradiATOM' theory (Gradient Theory of Magnitude), proposing that gradient organization can facilitate the transformations and integrations of magnitude representations by allowing space-and time-related neural populations to interact with each other over minimal distances.

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“…Bayesian surprise, also called shifts in beliefs or model updating, is believed to map 77 to a completely different region of the human brain from Shannon surprise. Several 78 recent reports based on fMRI (Functional Magnetic Resonance Imaging) data posit 79 that Shannon and Bayesian surprises modulate different brain regions [21][22][23]. In 80 other words, Shannon and Bayesian surprise definitions model distinct cognitive 81 processes in the brain, with Shannon surprise reflecting the unlikeliness of the input, 82 and Bayesian surprise representing model updating.…”

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confidence: 99%

Brain signatures of surprise in EEG and MEG data

Mousavi

Kiani

Aghajan

2020

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10The brain is constantly anticipating the future of sensory inputs based on past 11 experiences. When new sensory data is different from predictions shaped by recent 12 trends, neural signals are generated to report this surprise. Existing models for 13 quantifying surprise are based on an ideal observer assumption operating under one 14 of the three definitions of surprise set forth as the Shannon, Bayesian, and 15 Confidence-corrected surprise. In this paper, we analyze both visual and auditory 16 EEG and auditory MEG signals recorded during oddball tasks to examine which 17 temporal components in these signals are sufficient to decode the brain's surprise 18 based on each of these three definitions. We found that for both recording systems 19 the Shannon surprise is always significantly better decoded than the Bayesian 20 surprise regardless of the sensory modality and the selected temporal features used 21 for decoding. 22 squared of regression model, ERP temporal components, generative distribution, 24 decoding power, middle temporal components, late temporal components, mismatch 25 negativity (MMN), P300, oddball task 26 Author summary 27 A regression model is proposed for decoding the level of the brain's surprise in 28 response to sensory sequences using selected temporal components of recorded EEG 3 29 and MEG data. Three surprise quantification definitions (Shannon, Bayesian,and 30 Confidence-corrected surprise) are compared in offering decoding power. Four 31 different regimes for selecting temporal samples of EEG and MEG data are used to 32 evaluate which part of the recorded data may contain signatures that represent the 33 brain's surprise in terms of offering a high decoding power. We found that both the 34 middle and late components of the EEG response offer strong decoding power for 35 surprise while the early components are significantly weaker in decoding surprise. In 36 the MEG response, we found that the middle components have the highest decoding 37 power while the late components offer moderate decoding powers. When using a 38 single temporal sample for decoding surprise, samples of the middle segment possess 39 the highest decoding power. Shannon surprise is always better decoded than the 40 other definitions of surprise for all the four temporal feature selection regimes. 41 Similar superiority for Shannon surprise is observed for the EEG and MEG data 42 across the entire range of temporal sample regimes used in our analysis. 43 45 next sensory input. Past inputs are used by the brain to form prior knowledge in the 46 Bayesian brain model [2,3]. In fact, the results of brain functions such as perception, 47 eliminating ambiguity, attention, and decision making are dependent on the way the 48 current sensory input and the knowledge gained from previous experiences are 49 combined in the hierarchical inference model of the brain [1] (for review see [4]). 50An input different from what the brain has predicted will be surprising in that it 51 generates a form of response measurable by brain ...

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Bayesian modeling of temporal expectations in the human brain

Cited by 41 publications

References 134 publications

Electroencephalographic Correlates of Temporal Bayesian Belief Updating and Surprise

Electroencephalographic Correlates of Temporal Bayesian Belief Updating and Surprise

From ATOM to GradiATOM: Cortical gradients support time and space processing as revealed by a meta-analysis of neuroimaging studies

Brain signatures of surprise in EEG and MEG data

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