scholarly journals Finding and Keeping the Beat: Neural Mechanisms Differ as Beat Perception Unfolds

2021 ◽  
Author(s):  
Daniel J. Cameron ◽  
Jessica A. Grahn
Keyword(s):  
Temporal Structure ◽  
Superior Temporal Gyrus ◽  
Anterior Insula ◽  
Neural Mechanisms ◽  
Prediction Errors ◽  
Dynamic Adjustment ◽  
Metrical Structure ◽  
Cortical Oscillations ◽  
Over Time

AbstractPerception of a regular beat is essential to our ability to synchronize movements to music in an anticipatory fashion. Beat perception requires multiple, distinct neural functions, corresponding to the perceptual stages that occur over time, including 1) detection that regularity is present (beat finding), 2) prediction of future regular events to enable anticipation (beat continuation), and 3) dynamic adjustment of predictions as the rhythmic stimulus changes (beat adjustment). The striatum has been shown to be crucial for beat perception generally, although it is unclear how, or whether, distinct regions of the striatum contribute to these different stages of beat perception. Here, we used fMRI to investigate the activity of striatal subregions during the different stages of beat perception. Participants listened to pairs of rhythms (polyrhythms) whose temporal structure induced distinct perceptual stages—finding, continuation, and adjustment of the beat. Dorsal putamen was preferentially active during beat finding, whereas the ventral putamen was preferentially active during beat adjustment. We also observed that anterior insula activity was sensitive to metrical structure (greater when polyrhythms were metrically incongruent than when they were congruent). These data implicate the dorsal putamen in the detection of regularity, possibly by detection of coincidences between cortical oscillations, and the ventral putamen in the adjustment of regularity perception, possibly by integration of prediction errors in ongoing beat predictions. Additionally, activity in the supramarginal and superior temporal gyri correlated with beat tapping performance, and activity in the superior temporal gyrus correlated with beat perception (performance on the Beat Alignment Test).

scholarly journals Computational and neural mechanisms of affected beliefs

2021 ◽  
Author(s):  
Laura Müller-Pinzler ◽  
Nora Czekalla ◽  
Annalina V Mayer ◽  
Alexander Schröder ◽  
David S Stolz ◽  
...  
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Neural Activity ◽  
Self Efficacy ◽  
Efficacy Beliefs ◽  
Anterior Insula ◽  
Neural Mechanisms ◽  
Belief Formation ◽  
Prediction Errors ◽  
Subjective Value

The feedback people receive on their behavior shapes the process of belief formation and self-efficacy in mastering a given task. The neural and computational mechanisms of how the subjective value of these beliefs and corresponding affect bias the learning process are yet unclear. Here we investigate this question during learning of self-efficacy beliefs using fMRI, pupillometry, computational modeling and individual differences in affective experience. Biases in the formation of self-efficacy beliefs were associated with affect, pupil dilation and neural activity within the anterior insula, amygdala, VTA/SN, and mPFC. Specifically, neural and pupil responses map the valence of the prediction errors in correspondence to the experienced affect and learning bias people show during belief formation. Together with the functional connectivity dynamics of the anterior insula within this network our results hint towards neural and computational mechanisms that integrate affect in the process of belief formation.

scholarly journals Epilepsy and Ecstatic Experiences: The Role of the Insula

2021 ◽  
Vol 11 (11) ◽  
pp. 1384
Author(s):  
Fabienne Picard ◽  
Peter Bossaerts ◽  
Fabrice Bartolomei
Keyword(s):  
Temporal Lobe Epilepsy ◽  
Temporal Lobe ◽  
Mystical Experience ◽  
Focal Epilepsy ◽  
Anterior Insula ◽  
External Signal ◽  
Prediction Errors ◽  
Rare Form ◽  
The World

Ecstatic epilepsy is a rare form of focal epilepsy in which the aura (beginning of the seizures) consists of a blissful state of mental clarity/feeling of certainty. Such a state has also been described as a “religious” or mystical experience. While this form of epilepsy has long been recognized as a temporal lobe epilepsy, we have accumulated evidence converging toward the location of the symptomatogenic zone in the dorsal anterior insula during the 10 last years. The neurocognitive hypothesis for the genesis of a mental clarity is the suppression of the interoceptive prediction errors and of the unexpected surprise associated with any incoming internal or external signal, usually processed by the dorsal anterior insula. This mimics a perfect prediction of the world and induces a feeling of certainty. The ecstatic epilepsy is thus an amazing model for the role of anterior insula in uncertainty and surprise.

scholarly journals Neural Mechanisms of Reward Prediction Error in Autism Spectrum Disorder

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Maya G. Mosner ◽  
R. Edward McLaurin ◽  
Jessica L. Kinard ◽  
Shabnam Hakimi ◽  
Jacob Parelman ◽  
...  
Keyword(s):  
Prediction Error ◽  
Autism Spectrum ◽  
Neural Mechanisms ◽  
Reward Learning ◽  
Prediction Errors ◽  
Frontal Pole ◽  
Neural Basis ◽  
Reward Prediction ◽  
Behavioral Impairments ◽  
Adults With Asd

Few studies have explored neural mechanisms of reward learning in ASD despite evidence of behavioral impairments of predictive abilities in ASD. To investigate the neural correlates of reward prediction errors in ASD, 16 adults with ASD and 14 typically developing controls performed a prediction error task during fMRI scanning. Results revealed greater activation in the ASD group in the left paracingulate gyrus during signed prediction errors and the left insula and right frontal pole during thresholded unsigned prediction errors. Findings support atypical neural processing of reward prediction errors in ASD in frontostriatal regions critical for prediction coding and reward learning. Results provide a neural basis for impairments in reward learning that may contribute to traits common in ASD (e.g., intolerance of unpredictability).

scholarly journals Functional and Neural Mechanisms of Out-of-Body Experiences: Importance of Retinogeniculo-Cortical Oscillations

2016 ◽  
Vol 06 (04) ◽  
pp. 287-302 ◽  
Author(s):  
Ravinder Jerath ◽  
Shannon M. Cearley ◽  
Vernon A. Barnes ◽  
Mike Jensen
Keyword(s):  
Neural Mechanisms ◽  
Cortical Oscillations ◽  
Body Experiences

scholarly journals Global Effects of Feature-based Attention Depend on Surprise

2019 ◽  
Author(s):  
Cooper A. Smout ◽  
Marta I. Garrido ◽  
Jason B. Mattingley
Keyword(s):  
Sensory Information ◽  
Focus Of Attention ◽  
Neural Mechanisms ◽  
Visual Modality ◽  
Recent Theory ◽  
Prediction Errors ◽  
Neural Responses ◽  
Attention Task ◽  
Feature Based ◽  
Task Irrelevant

AbstractRecent studies have shown that prediction and attention can interact under various circumstances, suggesting that the two processes are based on interdependent neural mechanisms. In the visual modality, attention can be deployed to the location of a task-relevant stimulus (‘spatial attention’) or to a specific feature of the stimulus, such as colour or shape, irrespective of its location (‘feature-based attention’). Here we asked whether predictive processes are influenced by feature-based attention outside the current spatial focus of attention. Across two experiments, we recorded neural activity with electroencephalography (EEG) as human observers performed a feature-based attention task at fixation and ignored a stream of peripheral stimuli with predictable or surprising features. Central targets were defined by a single feature (colour or orientation) and differed in salience across the two experiments. Task-irrelevant peripheral patterns usually comprised one particular conjunction of features (standards), but occasionally deviated in one or both features (deviants). Consistent with previous studies, we found reliable effects of feature-based attention and prediction on neural responses to task-irrelevant patterns in both experiments. Crucially, we observed an interaction between prediction and feature-based attention in both experiments: the neural effect of feature-based attention was larger for surprising patterns than it was for predicted patterns. These findings suggest that global effects of feature-based attention depend on surprise, and are consistent with the idea that attention optimises the precision of predictions by modulating the gain of prediction errors.Significance StatementTwo principal mechanisms facilitate the efficient processing of sensory information: prediction uses prior information to guide the interpretation of sensory events, whereas attention biases the processing of these events according to their behavioural relevance. A recent theory proposes to reconcile attention and prediction under a unifying framework, casting attention as a ‘precision optimisation’ mechanism that enhances the gain of prediction errors. Crucially, this theory suggests that attention and prediction interact to modulate neural responses, but this hypothesis remains to be tested with respect to feature-based attention mechanisms outside the spatial focus of attention. Here we show that global effects of feature-based attention are enhanced when stimuli possess surprising features, suggesting that feature-based attention and prediction are interdependent neural mechanisms.

scholarly journals Adaptive coding of pain prediction error in the anterior insula

2021 ◽  
Author(s):  
Robert Hoskin ◽  
Deborah Talmi
Keyword(s):  
Prediction Error ◽  
Contextual Information ◽  
Neural Mechanism ◽  
Axiomatic Approach ◽  
Anterior Insula ◽  
Prediction Errors ◽  
Adaptive Coding ◽  
Pain Prediction ◽  
The Brain ◽  
Behavioural Evidence

Background: To reduce the computational demands of the task of determining values, the brain is thought to engage in adaptive coding, where the sensitivity of some neurons to value is modulated by contextual information. There is good behavioural evidence that pain is coded adaptively, but controversy regarding the underlying neural mechanism. Additionally, there is evidence that reward prediction errors are coded adaptively, but no parallel evidence regarding pain prediction errors. Methods: We tested the hypothesis that pain prediction errors are coded adaptively by scanning 19 healthy adults with fMRI while they performed a cued pain task. Our analysis followed an axiomatic approach. Results: We found that the left anterior insula was the only region which was sensitive both to predicted pain magnitudes and the unexpectedness of pain delivery, but not to the magnitude of delivered pain. Conclusions: This pattern suggests that the left anterior insula is part of a neural mechanism that serves the adaptive prediction error of pain.

scholarly journals Neural mechanisms of economic commitment in the human medial prefrontal cortex

eLife ◽  
2014 ◽  
Vol 3 ◽  
Author(s):  
Konstantinos Tsetsos ◽  
Valentin Wyart ◽  
S Paul Shorkey ◽  
Christopher Summerfield
Keyword(s):  
Prefrontal Cortex ◽  
Medial Prefrontal Cortex ◽  
Neural Mechanisms ◽  
Anterior Cingulate ◽  
Dorsal Anterior Cingulate Cortex ◽  
Financial Return ◽  
Economic Decisions ◽  
Decision Biases ◽  
Stimulus Value ◽  
Over Time

Neurobiologists have studied decisions by offering successive, independent choices between goods or gambles. However, choices often have lasting consequences, as when investing in a house or choosing a partner. Here, humans decided whether to commit (by acceptance or rejection) to prospects that provided sustained financial return. BOLD signals in the rostral medial prefrontal cortex (rmPFC) encoded stimulus value only when acceptance or rejection was deferred into the future, suggesting a role in integrating value signals over time. By contrast, the dorsal anterior cingulate cortex (dACC) encoded stimulus value only when participants rejected (or deferred accepting) a prospect. dACC BOLD signals reflected two decision biases–to defer commitments to later, and to weight potential losses more heavily than gains–that (paradoxically) maximised reward in this task. These findings offer fresh insights into the pressures that shape economic decisions, and the computation of value in the medial prefrontal cortex.

scholarly journals Detecting Transient Trapping from a Single Trajectory: A Structural Approach

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 1044
Author(s):  
Yann Lanoiselée ◽  
Jak Grimes ◽  
Zsombor Koszegi ◽  
Davide Calebiro
Keyword(s):  
Plasma Membrane ◽  
Single Particle ◽  
Adrenergic Receptors ◽  
Particle Trajectory ◽  
Temporal Structure ◽  
Structural Approach ◽  
New Method ◽  
Trajectory Data ◽  
Receptor Stimulation ◽  
Over Time

In this article, we introduce a new method to detect transient trapping events within a single particle trajectory, thus allowing the explicit accounting of changes in the particle’s dynamics over time. Our method is based on new measures of a smoothed recurrence matrix. The newly introduced set of measures takes into account both the spatial and temporal structure of the trajectory. Therefore, it is adapted to study short-lived trapping domains that are not visited by multiple trajectories. Contrary to most existing methods, it does not rely on using a window, sliding along the trajectory, but rather investigates the trajectory as a whole. This method provides useful information to study intracellular and plasma membrane compartmentalisation. Additionally, this method is applied to single particle trajectory data of β2-adrenergic receptors, revealing that receptor stimulation results in increased trapping of receptors in defined domains, without changing the diffusion of free receptors.

scholarly journals Higher-Order Conditioning With Simultaneous and Backward Conditioned Stimulus: Implications for Models of Pavlovian Conditioning

2021 ◽  
Vol 15 ◽  
Author(s):  
Arthur Prével ◽  
Ruth M. Krebs
Keyword(s):  
Conditioned Stimulus ◽  
Pavlovian Conditioning ◽  
Temporal Structure ◽  
Higher Order ◽  
Prediction Errors ◽  
Temporal Difference ◽  
Conditioned Stimuli ◽  
Form Complex ◽  
Reward Prediction ◽  
Order Conditioning

In a new environment, humans and animals can detect and learn that cues predict meaningful outcomes, and use this information to adapt their responses. This process is termed Pavlovian conditioning. Pavlovian conditioning is also observed for stimuli that predict outcome-associated cues; a second type of conditioning is termed higher-order Pavlovian conditioning. In this review, we will focus on higher-order conditioning studies with simultaneous and backward conditioned stimuli. We will examine how the results from these experiments pose a challenge to models of Pavlovian conditioning like the Temporal Difference (TD) models, in which learning is mainly driven by reward prediction errors. Contrasting with this view, the results suggest that humans and animals can form complex representations of the (temporal) structure of the task, and use this information to guide behavior, which seems consistent with model-based reinforcement learning. Future investigations involving these procedures could result in important new insights on the mechanisms that underlie Pavlovian conditioning.

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