Electroencephalographic Correlates of Temporal Bayesian Belief Updating and Surprise

Visalli, Antonino; Capizzi, Mariagrazia; Ambrosini, Ettore; Kopp, Bruno; Vallesi, Antonino

doi:10.31234/osf.io/8bq2t

Cited by 5 publications

(14 citation statements)

References 66 publications

(104 reference statements)

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“…This fine-grained characterization of the development and persistence of memory-guided effects can help constrain models of temporal preparation. The gradual acquisition of differential preparation and its longevity throughout the Transfer phase illustrate how temporal preparation is affected by long-term memory and sluggishly adapts to changing environmental statistics (see also Crowe & Kent, 2019;Crowe et al, 2021;Los et al, 2017;Mattiesing et al, 2017;Visalli et al, 2021;Visalli et al, 2019). Many probability-driven models characterize preparation as guided by static representations of the current FP distribution (Grabenhorst et al, 2019;Janssen & Shadlen, 2005;Trillenberg et al, 2000;Vangkilde et al, 2013), foregoing the role of memory and learning.…”

Section: Discussionmentioning

confidence: 99%

Implicitly learning when to be ready: from instances to categories

Kruijne¹,

Galli²,

Los³

2021

Preprint

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[Manuscript submitted for review]There is growing appreciation for the role of long-term memory in guiding temporal preparation. In experiments with variable foreperiods between a warning stimulus (S1) and a target stimulus (S2), preparation is affected by foreperiod distributions experienced in the past, long after the distribution has changed. Such memory-guided preparation shapes preparation largely implicitly and outside of a participants’ control. Recent studies have demonstrated the associative nature of such memory-guided preparation. When distinct S1s predict different foreperiods, they can trigger dissociative preparation accordingly. Here, we demonstrate that memory-guided preparation allows for another key feature of learning: the ability to generalize across acquired associations and apply them to novel situations. Participants completed a foreperiod task where S1 was a unique image of either a face or a scene on each trial. Images of either category were paired with different distributions with predominantly shorter versus predominantly longer foreperiods. Participants displayed dissociative preparation to never-before seen images of either category, without being aware of the predictive nature of these categories. They continued doing so in a subsequent transfer phase, after they had been informed that these contingencies no longer held. A novel rolling regression analysis revealed at a fine timescale how category-guided preparation gradually developed throughout the task, and illustrated how instructions at the start of the transfer phase interacted with these influences from long-term memory. These results offer new insights into temporal preparation as the product of a largely implicit process governed by associative learning from past experiences.

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Section: Discussionmentioning

confidence: 99%

Implicitly learning when to be ready: from instances to categories

Kruijne¹,

Galli²,

Los³

2021

Preprint

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“…The sequence of events was the same as in our previous study (Visalli et al, 2021). The task was divided in 73 blocks (the first block was used in computational modeling but excluded from the inferential analyses), each one including a number of trials ranging between 8 and 13 (mean: 10.5).…”

Section: Task and Proceduresmentioning

confidence: 99%

“…To fill that gap, we combined a Bayesian computational approach with functional magnetic resonance imaging (fMRI) (Visalli et al, 2019) and electroencephalography (EEG) (Visalli et al, 2021) to elucidate the neural correlates of temporal belief updating. Crucially, since measures of belief updating (e.g., Kullbach-Leibler divergence, D KL ) are often confounded with measures of surprise (e.g., Shannon information, I S ), our studies adapted a task manipulation from O'Reilly and colleagues (2013) that allowed us to differentiate updating and surprise correlates (see Baldi & Itti, 2010;Itti & Baldi, 2009 for a deeper discussion on the difference between updating and surprise measures).…”

Section: Introductionmentioning

confidence: 99%

“…Briefly, the fMRI study identified two networks in part distinctly associated with updating and surprise: the fronto-parietal network, which was mainly involved in updating prior beliefs about target timing, and the cingulo-opercular network, which was related to the surprise elicited by target onset (Visalli et al, 2019). The EEG study successfully isolated two dissociable P3-like modulations specifically indexing belief updating and surprise about the timing of events (Visalli et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…The present study aimed to investigate electrophysiological correlates underlying incidental Bayesian updating of temporal expectations. To this aim, we employed the same task as in our previous EEG study (Visalli et al, 2021), but without the color manipulation. Moreover, as detailed below, participants were not informed about the probabilistic structure of the task.…”

Section: Introductionmentioning

confidence: 99%

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P3-like signatures of temporal predictions: a computational EEG study

Visalli¹,

Capizzi²,

Ambrosini³

et al. 2022

Preprint

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Many cognitive processes, ranging from perception to action, depend on the ability to predict the timing of forthcoming events. Yet, how the brain uses predictive models in the temporal domain is still an unsolved question. In previous work, we began to explore the neural correlates of temporal predictions by using a computational approach in which an ideal Bayesian observer learned the temporal probabilities of target onsets in a simple reaction time task. Because the task was specifically designed to disambiguate updating of predictive models and surprise, changes in temporal probabilities were explicitly cued. However, in the real world, we are usually incidentally exposed to changes in the statistics of the environment. Here, we thus aimed to further investigate the electroencephalographic (EEG) correlates of Bayesian belief updating and surprise associated with incidental learning of temporal probabilities. In line with our previous EEG study, results showed distinct P3-like modulations for updating and surprise. While surprise was indexed by an early fronto-central P3-like modulation, updating was associated with a later and more posterior P3 modulation. Moreover, updating was associated with a P2-like potential at centro-parietal electrodes, likely capturing integration processes between prior beliefs and likelihood of the observed event. These findings support previous evidence of trial-by-trial variability of P3 amplitudes as an index of dissociable inferential processes. Coupled with our previous findings, the present study strongly bolsters the view of the P3 as a key brain signature of temporal Bayesian inference. Data and scripts are shared on OSF: osf.io/sdy8j/

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