scholarly journals Sedation Modulates Frontotemporal Predictive Coding Circuits and the Double Surprise Acceleration Effect

2020 ◽  
Vol 30 (10) ◽  
pp. 5204-5217
Author(s):  
Adrien Witon ◽  
Amirali Shirazibehehsti ◽  
Jennifer Cooke ◽  
Alberto Aviles ◽  
Ram Adapa ◽  
...  
Keyword(s):  
Cognitive Neuroscience ◽  
Predictive Coding ◽  
Local Effect ◽  
Second Phase ◽  
Prediction Errors ◽  
Global Effect ◽  
Third Phase ◽  
Research Task ◽  
The Brain ◽  
Short Timeframe

Abstract Two important theories in cognitive neuroscience are predictive coding (PC) and the global workspace (GW) theory. A key research task is to understand how these two theories relate to one another, and particularly, how the brain transitions from a predictive early state to the eventual engagement of a brain-scale state (the GW). To address this question, we present a source-localization of EEG responses evoked by the local-global task—an experimental paradigm that engages a predictive hierarchy, which encompasses the GW. The results of our source reconstruction suggest three phases of processing. The first phase involves the sensory (here auditory) regions of the superior temporal lobe and predicts sensory regularities over a short timeframe (as per the local effect). The third phase is brain-scale, involving inferior frontal, as well as inferior and superior parietal regions, consistent with a global neuronal workspace (GNW; as per the global effect). Crucially, our analysis suggests that there is an intermediate (second) phase, involving modulatory interactions between inferior frontal and superior temporal regions. Furthermore, sedation with propofol reduces modulatory interactions in the second phase. This selective effect is consistent with a PC explanation of sedation, with propofol acting on descending predictions of the precision of prediction errors; thereby constraining access to the GNW.

scholarly journals Sedation modulates fronto-temporal predictive coding circuits and the double surprise acceleration effect

2020 ◽  
Author(s):  
Adrien Witon ◽  
Amirali Shirazibehehsti ◽  
Jennifer Cooke ◽  
Alberto Aviles ◽  
Ram Adapa ◽  
...  
Keyword(s):  
Predictive Coding ◽  
Local Effect ◽  
Second Phase ◽  
Prediction Errors ◽  
Global Effect ◽  
Third Phase ◽  
Global Workspace ◽  
Research Task ◽  
The Brain ◽  
Short Timeframe

AbstractTwo important theories in cognitive neuroscience are predictive coding and the global workspace theory. A key research task is to understand how these two theories relate to one another, and particularly, how the brain transitions from a predictive early state to the eventual engagement of a brain-scale state (the global workspace). To address this question, we present a source-localisation of EEG responses evoked by the local-global task – an experimental paradigm that engages a predictive hierarchy, which encompasses the global workspace. The results of our source reconstruction suggest three-phases of processing. The first phase involves the sensory (here auditory) regions of the superior temporal lobe and predicts sensory regularities over a short timeframe (as per the local effect). The third phase is brain-scale, involving inferior frontal, as well as inferior and superior parietal regions; consistent with a global neuronal workspace (as per the global effect). Crucially, our analysis suggests that there is an intermediate (second) phase, involving modulatory interactions between inferior frontal and superior temporal regions. Furthermore, sedation with propofol reduces modulatory interactions in the second phase. This selective effect is consistent with a predictive coding explanation of sedation, with propofol acting on descending predictions of the precision of prediction errors; thereby constraining access to the global neuronal workspace.

Brain-to-body ratios and time of maturation of the mouse brain

1963 ◽  
Vol 204 (2) ◽  
pp. 343-346 ◽  
Author(s):  
Tsukasa Kobayashi
Keyword(s):  
Body Weight ◽  
Short Duration ◽  
Mouse Brain ◽  
Abrupt Change ◽  
Brain Weight ◽  
Second Phase ◽  
The Third ◽  
Third Phase ◽  
The Brain

Studies on the relationships of brain weight to body weight during development were conducted in 218 mice, and revealed three distinct phases. During the first phase, the ratios are relatively constant. The second phase of short duration is characterized by abrupt reductions. In the third phase, which is the most enduring, the ratios again assume more constant values. The abrupt change in the ratios took place around 14 days of age. It is suggested that the abrupt change in the ratio is, in general, an indicator of the maturation of the brain, because there are several other parameters which approach mature levels around the 15th day. A review of the data on other species supports this suggestion.

scholarly journals Backwards is the way forward: Feedback in the cortical hierarchy predicts the expected future

2013 ◽  
Vol 36 (3) ◽  
pp. 221-221 ◽  
Author(s):  
Lars Muckli ◽  
Lucy S. Petro ◽  
Fraser W. Smith
Keyword(s):  
Predictive Coding ◽  
Internal Models ◽  
Prediction Errors ◽  
Conceptual Level ◽  
Cortical Hierarchy ◽  
The Brain ◽  
Descriptive Level ◽  
Perception Action ◽  
The Way

AbstractClark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models).

scholarly journals Attention Promotes the Neural Encoding of Prediction Errors

2019 ◽  
Author(s):  
Cooper A. Smout ◽  
Matthew F. Tang ◽  
Marta I. Garrido ◽  
Jason B. Mattingley
Keyword(s):  
Information Processing ◽  
Coding Theory ◽  
Predictive Coding ◽  
Sensory Information ◽  
Neural Response ◽  
Prediction Errors ◽  
Feature Information ◽  
The Difference ◽  
Stimulus Features ◽  
The Brain

AbstractThe human brain is thought to optimise the encoding of incoming sensory information through two principal mechanisms: prediction uses stored information to guide the interpretation of forthcoming sensory events, and attention prioritizes these events according to their behavioural relevance. Despite the ubiquitous contributions of attention and prediction to various aspects of perception and cognition, it remains unknown how they interact to modulate information processing in the brain. A recent extension of predictive coding theory suggests that attention optimises the expected precision of predictions by modulating the synaptic gain of prediction error units. Since prediction errors code for the difference between predictions and sensory signals, this model would suggest that attention increases the selectivity for mismatch information in the neural response to a surprising stimulus. Alternative predictive coding models proposes that attention increases the activity of prediction (or ‘representation’) neurons, and would therefore suggest that attention and prediction synergistically modulate selectivity for feature information in the brain. Here we applied multivariate forward encoding techniques to neural activity recorded via electroencephalography (EEG) as human observers performed a simple visual task, to test for the effect of attention on both mismatch and feature information in the neural response to surprising stimuli. Participants attended or ignored a periodic stream of gratings, the orientations of which could be either predictable, surprising, or unpredictable. We found that surprising stimuli evoked neural responses that were encoded according to the difference between predicted and observed stimulus features, and that attention facilitated the encoding of this type of information in the brain. These findings advance our understanding of how attention and prediction modulate information processing in the brain, and support the theory that attention optimises precision expectations during hierarchical inference by increasing the gain of prediction errors.

scholarly journals Prediction Error and Repetition Suppression Have Distinct Effects on Neural Representations of Visual Information

2017 ◽  
Author(s):  
Matthew F. Tang ◽  
Cooper A. Smout ◽  
Ehsan Arabzadeh ◽  
Jason B. Mattingley
Keyword(s):  
Visual Information ◽  
Predictive Coding ◽  
Orientation Selectivity ◽  
Recent Experience ◽  
Prediction Errors ◽  
Neural Responses ◽  
Repetition Suppression ◽  
Neural Representations ◽  
Feature Information ◽  
The Brain

AbstractPredictive coding theories argue that recent experience establishes expectations in the brain that generate prediction errors when violated. Prediction errors provide a possible explanation for repetition suppression, where evoked neural activity is attenuated across repeated presentations of the same stimulus. The predictive coding account argues repetition suppression arises because repeated stimuli are expected, whereas non-repeated stimuli are unexpected and thus elicit larger neural responses. Here we employed electroencephalography in humans to test the predictive coding account of repetition suppression by presenting sequences of visual gratings with orientations that were expected either to repeat or change in separate blocks of trials. We applied multivariate forward modelling to determine how orientation selectivity was affected by repetition and prediction. Unexpected stimuli were associated with significantly enhanced orientation selectivity, whereas selectivity was unaffected for repeated stimuli. Our results suggest that repetition suppression and expectation have separable effects on neural representations of visual feature information.

scholarly journals Adaptive cortical processing of unattended sounds: neurocomputational underpinnings revealed by simultaneous EEG-MEG

2018 ◽  
Author(s):  
Françoise Lecaignard ◽  
Olivier Bertrand ◽  
Anne Caclin ◽  
Jérémie Mattout
Keyword(s):  
Bayesian Learning ◽  
Predictive Coding ◽  
Prediction Errors ◽  
Temporal Network ◽  
Auditory Oddball ◽  
Sensory Experiences ◽  
Automatic Adaptation ◽  
Contextual Adaptation ◽  
Sensory Prediction ◽  
The Brain

AbstractPerceptual processes are shaped by past sensory experiences, raising the question of contextual adaptation of information processing in the brain. In predictive coding, the tuning of precision weights (PW) applying to sensory prediction errors (PE) enables such adaptation. We test this hypothesis with an original auditory oddball design to recover the respective neurophysiological encoding of PW and PE. Predictability of sound sequences was manipulated without the participants’ knowledge to influence each quantity differentially. Using highly-informed EEG-MEG, trial-to-trial learning models were employed together with dynamic causal models (DCM) of deviance responses. Modelling results revealed Bayesian learning within a fronto-temporal network. Essentially, we show an automatic adaptation of learning and underlying connectivity to the contextual informational content. Predictability yielded a more informed learning with larger PW and lower PE, signaled by a decreased neuronal self-inhibition, and a decreased forward connectivity, respectively. These findings strongly support the predictive coding account of perception with automatic contextual adaptation.

Pupil Drift Rate Indexes Groove Ratings

2021 ◽  
Author(s):  
Connor Spiech ◽  
George Sioros ◽  
Tor Endestad ◽  
Anne Danielsen ◽  
Bruno Laeng
Keyword(s):  
Prediction Error ◽  
Drift Rate ◽  
Temporal Structure ◽  
Predictive Coding ◽  
Prediction Errors ◽  
Musical Ability ◽  
Moderate Complexity ◽  
Musical Rhythms ◽  
Sensory Prediction ◽  
The Brain

Groove, understood as a pleasurable compulsion to move to musical rhythms, typically varies along an inverted U-curve with increasing rhythmic complexity (e.g., syncopation, pickups). Predictive coding accounts posit that moderate complexity drives us to move to reduce sensory prediction errors and model the temporal structure. While musicologists generally distinguish the effects of pickups (anacruses) and syncopations, their difference remains unexplored in groove. We used pupillometry as an index to noradrenergic arousal while subjects listened to and rated drumbeats varying in rhythmic complexity. We replicated the inverted U-shaped relationship between rhythmic complexity and groove and showed this is modulated by musical ability, based on a psychoacoustic beat perception test. The pupil drift rates suggest that groovier rhythms hold attention longer than ones rated less groovy. Moreover, we found complementary effects of syncopations and pickups on groove ratings and pupil size, respectively, discovering a distinct predictive process related to pickups. We suggest that the brain deploys attention to pickups to sharpen subsequent strong beats, augmenting the predictive scaffolding’s focus on beats that reduce syncopations’ prediction errors. This interpretation is in accordance with groove envisioned as an embodied resolution of precision-weighted prediction error.

scholarly journals Prediction error and repetition suppression have distinct effects on neural representations of visual information

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Matthew F Tang ◽  
Cooper A Smout ◽  
Ehsan Arabzadeh ◽  
Jason B Mattingley
Keyword(s):  
Visual Information ◽  
Predictive Coding ◽  
Orientation Selectivity ◽  
Recent Experience ◽  
Prediction Errors ◽  
Neural Responses ◽  
Repetition Suppression ◽  
Neural Representations ◽  
Feature Information ◽  
The Brain

Predictive coding theories argue that recent experience establishes expectations in the brain that generate prediction errors when violated. Prediction errors provide a possible explanation for repetition suppression, where evoked neural activity is attenuated across repeated presentations of the same stimulus. The predictive coding account argues repetition suppression arises because repeated stimuli are expected, whereas non-repeated stimuli are unexpected and thus elicit larger neural responses. Here, we employed electroencephalography in humans to test the predictive coding account of repetition suppression by presenting sequences of visual gratings with orientations that were expected either to repeat or change in separate blocks of trials. We applied multivariate forward modelling to determine how orientation selectivity was affected by repetition and prediction. Unexpected stimuli were associated with significantly enhanced orientation selectivity, whereas selectivity was unaffected for repeated stimuli. Our results suggest that repetition suppression and expectation have separable effects on neural representations of visual feature information.

Temporal Prediction Errors Affect Short-Term Memory Scanning Response Time

2016 ◽  
Vol 63 (6) ◽  
pp. 333-342
Author(s):  
Roberto Limongi ◽  
Angélica M. Silva
Keyword(s):  
Response Time ◽  
Short Term Memory ◽  
Estimation Error ◽  
Predictive Coding ◽  
Time Estimation ◽  
Prediction Errors ◽  
Memory Scanning ◽  
Short Term ◽  
Term Memory ◽  
Temporal Prediction

Abstract. The Sternberg short-term memory scanning task has been used to unveil cognitive operations involved in time perception. Participants produce time intervals during the task, and the researcher explores how task performance affects interval production – where time estimation error is the dependent variable of interest. The perspective of predictive behavior regards time estimation error as a temporal prediction error (PE), an independent variable that controls cognition, behavior, and learning. Based on this perspective, we investigated whether temporal PEs affect short-term memory scanning. Participants performed temporal predictions while they maintained information in memory. Model inference revealed that PEs affected memory scanning response time independently of the memory-set size effect. We discuss the results within the context of formal and mechanistic models of short-term memory scanning and predictive coding, a Bayes-based theory of brain function. We state the hypothesis that our finding could be associated with weak frontostriatal connections and weak striatal activity.

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