Ketamine Affects Prediction Errors about Statistical Regularities: A Computational Single-Trial Analysis of the Mismatch Negativity

Weber, Lilian; Diaconescu, Andreea O.; Mathys, Christoph; Schmidt, André; Kometer, Michael; Vollenweider, Franz X.; Stephan, Klaas Enno

doi:10.1101/528372

Cited by 21 publications

(45 citation statements)

References 77 publications

(94 reference statements)

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“…Trial-wise estimates of uncertainty -or its inverse, precision -serve to scale the impact of prediction errors (PEs) on the belief updates. Consistent with previous reports (Stefanics et al, 2018;Weber et al, 2019), we found that precision-weighted PEs (pwPEs) on two hierarchical levels can explain trial-wise modulation of observed ERP responses in control participants.…”

Section: Discussionsupporting

confidence: 92%

“…Lower level pwPEs about the reward outcomes, updating beliefs about reward tendency and higher-level pwPEs about reward tendencies, updating volatility estimates, were primarily represented across frontocentral regions, with an earlier latency for ε3 (387 ms) than ε2 (453 ms). These results align with previous studies combining EEG analyses with the HGF, which revealed that multiple, hierarchically-related pwPEs are computed while learning in volatile environments; and these are represented across different brain regions specific to the task demands (Diaconescu et al, 2017a;Stefanics et al, 2018;Weber et al, 2019).…”

Section: Hierarchically-related Prediction Errors Modulate Trial-by-tsupporting

confidence: 90%

“…We used the Hierarchical Gaussian Filter (HGF) from Mathys et al (2014Mathys et al ( , 2011 to estimate each participant's individual learning characteristics and belief trajectories during our binary reward-learning task. The HGF has been applied to understand learning across diverse settings Diaconescu et al, 2017bDiaconescu et al, , 2014Iglesias et al, 2013;Marshall et al, 2016;Stefanics et al, 2018;Weber et al, 2019). It is implemented in the freely available open source software TAPAS (http://www.translationalneuromodeling.org/tapas).…”

Section: The Hierarchical Gaussian Filter (Hgf)mentioning

confidence: 99%

“…More compelling evidence linking PEs, Bayesian surprise, and belief updating to changes in ERP responses comes from studies combining computational modelling and analysis of trialwise EEG responses (Diaconescu et al, 2017a;Jepma et al, 2016;Kolossa et al, 2015;Mars et al, 2008;Stefanics et al, 2018;Weber et al, 2019). For instance, recent EEG studies on the MMN were able to spatiotemporally dissociate lower-level precision-weighted PE (pwPE) signals, which drive updates in belief estimates (Stefanics et al, 2018), and higher-level pwPEs, driving volatility updates (Weber et al, 2019). In addition, model-based single-trial analyses of the P300 identified the earlier P3a waveform of anterior distribution as an index of belief updating, whereas Bayesian surprise was represented in the later posterior P3b component (Kolossa et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…The HGF estimates individual trajectories of trial-wise belief updates governed by hierarchically related PEs based on the responses of participants. To reveal the effect of hierarchical PEs and precision weights on evoked brain responses, we used the relevant hierarchical computational quantities (pwPEs) as regressors in a general linear model (GLM) of trial-wise EEG amplitudes as done in previous studies (Diaconescu et al, 2017a(Diaconescu et al, , 2014Weber et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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State anxiety biases estimates of uncertainty during reward learning in volatile environments

Hein

Weber

Fockert

et al. 2019

Preprint

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Previous research established that clinical anxiety impairs decision making and that high trait anxiety interferes with learning rates. Less understood are the effects of temporary anxious states on learning and decision making in healthy populations. Here we follow proposals that anxious states in healthy individuals elicit a pattern of aberrant behavioural, neural, and physiological responses comparable with those found in anxiety disorders, particularly when processing uncertainty in unstable environments. In our study, both a state anxious and a control group learned probabilistic stimulus-outcome mappings in a volatile task environment while we recorded their electrophysiological (EEG) signals. By using a hierarchical Bayesian model, we assessed the effect of state anxiety on Bayesian belief updating with a focus on uncertainty estimates. State anxiety was associated with an underestimation of environmental and informational uncertainty, and an increase in uncertainty about volatility estimates. Anxious individuals deemed their beliefs about reward contingencies to be more precise and to require less updating, ultimately leading to impaired reward-based learning. We interpret this pattern as evidence that state anxious individuals are less tolerant to informational uncertainty about the contingencies governing their environment and more uncertain about the level of stability of the world itself. Further, we tracked the neural representation of belief update signals in the trial-bytrial EEG amplitudes. In control participants, both lower-level precision-weighted prediction errors (pwPEs) about the reward outcomes and higher-level volatility-pwPEs were represented in the ERP signals with an anterior distribution. A different pattern emerged under state anxiety, where a neural representation of pwPEs was only found for updates about volatility. Expanding previous computational work on trait anxiety, our findings establish that temporary anxious states in healthy individuals impair reward-based learning in volatile environments, primarily through changes in uncertainty estimates and potentially a degradation of the neuronal representation of hierarchically-related pwPEs, considered to play a central role in current Bayesian accounts of perceptual inference and learning.

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Section: Discussionsupporting

confidence: 92%

Section: Hierarchically-related Prediction Errors Modulate Trial-by-tsupporting

confidence: 90%

Section: The Hierarchical Gaussian Filter (Hgf)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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State anxiety biases estimates of uncertainty during reward learning in volatile environments

Hein

Weber

Fockert

et al. 2019

Preprint

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Glutamatergic modulation of auditory cortex connectivity with attentional brain networks in unpredictable perceptual environment

Kompus

Volehaugen

Craven

et al. 2020

Sci Rep

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In a stable environment the brain can minimize processing required for sensory input by forming a predictive model of the surrounding world and suppressing neural response to predicted stimuli. Unpredicted stimuli lead to a prediction error signal propagation through the perceptual network, and resulting adjustment to the predictive model. The inter-regional plasticity which enables the model-building and model-adjustment is hypothesized to be mediated via glutamatergic receptors. While pharmacological challenge studies with glutamate receptor ligands have demonstrated impact on prediction-error indices, it is not clear how inter-individual differences in the glutamate system affect the prediction-error processing in non-medicated state. In the present study we examined 20 healthy young subjects with resting-state proton MRS spectroscopy to characterize glutamate + glutamine (rs-Glx) levels in their Heschl’s gyrus (HG), and related this to HG functional connectivity during a roving auditory oddball protocol. No rs-Glx effects were found within the frontotemporal prediction-error network. Larger rs-Glx signal was related to stronger connectivity between HG and bilateral inferior parietal lobule during unpredictable auditory stimulation. We also found effects of rs-Glx on the coherence of default mode network and frontoparietal network during unpredictable auditory stimulation. Our results demonstrate the importance of Glx in modulating long-range connections and wider networks in the brain during perceptual inference.

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Shared attention in virtual immersive reality enhances electrophysiological correlates of implicit sensory learning

Sarasso,

Ronga,

Piovesan

et al. 2024

Sci Rep

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Shared attention effects on learning and memory demonstrate that experiences are amplified when we are not alone. Virtual reality poses new challenges to the study of co-presence. Above all, is coattending together with someone else’s avatar in an immersive VR setting comparable with shared experiences at a neural processing level? In the present study we investigate shared attention effects in VR for the first time. We recorded mismatch negativities (MMN) during an auditory roving paradigm, a well-known index of implicit perceptual learning. EEG responses to deviant and standard sounds were registered while subjects were alone (Solo condition) or together (Other condition) with a virtual avatar (Virtual scenario) or physically present confederate (Physical scenario). We found an overall main effect of co-presence on MMN revealed by a point-by-point 2 × 2 ANOVA, thereby replicating previous studies on physical co-presence. Additionally, we found no significant interaction between the scenario (Physical vs. Virtual) and co-presence (Solo vs. Other). Our results indicate that virtual immersive co-presence mimics physical co-presence.

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Ketamine Affects Prediction Errors about Statistical Regularities: A Computational Single-Trial Analysis of the Mismatch Negativity

Cited by 21 publications

References 77 publications

State anxiety biases estimates of uncertainty during reward learning in volatile environments

State anxiety biases estimates of uncertainty during reward learning in volatile environments

Glutamatergic modulation of auditory cortex connectivity with attentional brain networks in unpredictable perceptual environment

Shared attention in virtual immersive reality enhances electrophysiological correlates of implicit sensory learning

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