scholarly journals Information theoretic evidence for predictive coding in the face processing system

2016 ◽  
Author(s):  
Alla Brodski-Guerniero ◽  
Georg-Friedrich Paasch ◽  
Patricia Wollstadt ◽  
Ipek Özdemir ◽  
Joseph T. Lizier ◽  
...  
Keyword(s):  
Prior Knowledge ◽  
Human Subjects ◽  
Brain Area ◽  
Predictive Coding ◽  
Sensory Information ◽  
Low Frequency ◽  
Processing System ◽  
Information Storage ◽  
Beta Band ◽  
The Brain

AbstractPredictive coding suggests that the brain infers the causes of its sensations by combining sensory evidence with internal predictions based on available prior knowledge. However, the neurophysiological correlates of (pre-)activated prior knowledge serving these predictions are still unknown. Based on the idea that such pre-activated prior knowledge must be maintained until needed we measured the amount of maintained information in neural signals via the active information storage (AIS) measure. AIS was calculated on whole-brain beamformer-reconstructed source time-courses from magnetoencephalography (MEG) recordings of 52 human subjects during the baseline of a Mooney face/house detection task. Pre-activation of prior knowledge for faces showed as alpha- and beta-band related AIS increases in content specific areas; these AIS increases were behaviourally relevant in brain area FFA. Further, AIS allowed decoding of the cued category on a trial-by-trial basis. Moreover, top-down transfer of predictions estimated by transfer entropy was associated with beta frequencies. Our results support accounts that activated prior knowledge and the corresponding predictions are signalled in low-frequency activity (<30 Hz).Significance statementOur perception is not only determined by the information our eyes/retina and other sensory organs receive from the outside world, but strongly depends also on information already present in our brains like prior knowledge about specific situations or objects. A currently popular theory in neuroscience, predictive coding theory, suggests that this prior knowledge is used by the brain to form internal predictions about upcoming sensory information. However, neurophysiological evidence for this hypothesis is rare – mostly because this kind of evidence requires making strong a-priori assumptions about the specific predictions the brain makes and the brain areas involved. Using a novel, assumption-free approach we find that face-related prior knowledge and the derived predictions are represented and transferred in low-frequency brain activity.

Changes in the Total Power of Beta and Theta Bands When Using EEG Biofeedback in Primary School-Age Children Having Difficulties with Voluntary Regulation

2020 ◽  
pp. 331-340
Author(s):  
Yuliya S. Dzhos ◽  
◽  
Irina A. Men’shikova ◽  
Keyword(s):  
Left Hemisphere ◽  
Spectral Characteristics ◽  
Low Frequency ◽  
School Age Children ◽  
Total Power ◽  
Eeg Biofeedback ◽  
Beta Band ◽  
Bioelectric Activity ◽  
Voluntary Regulation ◽  
The Brain

This article presents the results of the study on spectral electroencephalogram (EEG) characteristics in 7–10-year-old children (8 girls and 22 boys) having difficulties with voluntary regulation of activity after 10 and 20 neurofeedback sessions using beta-activating training. Brain bioelectric activity was recorded in 16 standard leads using the Neuron-Spectrum-4/VPM complex. The dynamics was assessed by EEG beta and theta bands during neurofeedback. An increase in the total power of beta band oscillations was established both after 10 and after 20 sessions of EEG biofeedback in the frontal (p ≤ 0.001), left parietal (p ≤ 0.036), and temporal (p ≤ 0.003) areas of the brain. A decrease in the spectral characteristics of theta band oscillations was detected: after 10 neurofeedback sessions in the frontal (p ≤ 0.008) and temporal (p ≤ 0.006) areas of both hemispheres, as well as in the parietal area of the left hemisphere (p ≤ 0.005); after 20 sessions, in the central (p ≤ 0.004), frontal (p ≤ 0.001) and temporal (p ≤ 0.001) areas of both hemispheres, as well as in the occipital (p ≤ 0.047) and parietal (p ≤ 0.001) areas of the left hemisphere. The study into the dynamics of bioelectric activity during biofeedback using EEG parameters in 7–10-year-old children with impaired voluntary regulation of higher mental functions allowed us to prove the advisability of 20 sessions, as the increase in high-frequency activity and decrease in low-frequency activity do not stop with the 10th session. Changes in these parameters after 10 EEG biofeedback sessions are expressed mainly in the frontotemporal areas of both hemispheres, while after a course of 20 sessions, in both the frontotemporal and central parietal areas of the brain.

scholarly journals Low-frequency electroencephalogram oscillations govern left-eye lateralization during anti-predatory responses in the music frog

2020 ◽  
Vol 223 (21) ◽  
pp. jeb232637
Author(s):  
Jiangyan Shen ◽  
Ke Fang ◽  
Ping Liu ◽  
Yanzhu Fan ◽  
Jing Yang ◽  
...  
Keyword(s):  
Visual Field ◽  
Left Visual Field ◽  
Right Hemisphere ◽  
Brain Area ◽  
Power Spectra ◽  
Low Frequency ◽  
The Right ◽  
Electroencephalogram Eeg ◽  
The Brain ◽  
Steady Velocity

ABSTRACTVisual lateralization is widespread for prey and anti-predation in numerous taxa. However, it is still unknown how the brain governs this asymmetry. In this study, we conducted behavioral and electrophysiological experiments to evaluate anti-predatory behaviors and dynamic brain activities in Emei music frogs (Nidirana daunchina), to explore the potential eye bias for anti-predation and the underlying neural mechanisms. To do this, predator stimuli (a model snake head and a leaf as a control) were moved around the subjects in clockwise and anti-clockwise directions at steady velocity. We counted the number of anti-predatory responses and measured electroencephalogram (EEG) power spectra for each band and brain area (telencephalon, diencephalon and mesencephalon). Our results showed that (1) no significant eye preferences could be found for the control (leaf); however, the laterality index was significantly lower than zero when the predator stimulus was moved anti-clockwise, suggesting that left-eye advantage exists in this species for anti-predation; (2) compared with no stimulus in the visual field, the power spectra of delta and alpha bands were significantly greater when the predator stimulus was moved into the left visual field anti-clockwise; and, (3) generally, the power spectra of each band in the right-hemisphere for the left visual field were higher than those in the left counterpart. These results support that the left eye mediates the monitoring of a predator in music frogs and lower-frequency EEG oscillations govern this visual lateralization.

scholarly journals Predictive Coding Over the Lifespan: Increased Reliance on Perceptual Priors in Older Adults—A Magnetoencephalography and Dynamic Causal Modeling Study

2021 ◽  
Vol 13 ◽  
Author(s):  
Jason S. Chan ◽  
Michael Wibral ◽  
Cerisa Stawowsky ◽  
Mareike Brandl ◽  
Saskia Helbling ◽  
...  
Keyword(s):  
Older Adults ◽  
Life Experience ◽  
Predictive Coding ◽  
Sensory Information ◽  
Causal Modeling ◽  
Beta Band ◽  
Dynamic Causal Modeling ◽  
Younger Adults ◽  
Illusory Percept ◽  
Band Activity

Aging is accompanied by unisensory decline. To compensate for this, two complementary strategies are potentially relied upon increasingly: first, older adults integrate more information from different sensory organs. Second, according to the predictive coding (PC) model, we form “templates” (internal models or “priors”) of the environment through our experiences. It is through increased life experience that older adults may rely more on these templates compared to younger adults. Multisensory integration and predictive coding would be effective strategies for the perception of near-threshold stimuli, which may however come at the cost of integrating irrelevant information. Both strategies can be studied in multisensory illusions because these require the integration of different sensory information, as well as an internal model of the world that can take precedence over sensory input. Here, we elicited a classic multisensory illusion, the sound-induced flash illusion, in younger (mean: 27 years, N = 25) and older (mean: 67 years, N = 28) adult participants while recording the magnetoencephalogram. Older adults perceived more illusions than younger adults. Older adults had increased pre-stimulus beta-band activity compared to younger adults as predicted by microcircuit theories of predictive coding, which suggest priors and predictions are linked to beta-band activity. Transfer entropy analysis and dynamic causal modeling of pre-stimulus magnetoencephalography data revealed a stronger illusion-related modulation of cross-modal connectivity from auditory to visual cortices in older compared to younger adults. We interpret this as the neural correlate of increased reliance on a cross-modal predictive template in older adults leading to the illusory percept.

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 Neural correlates of goal-directed and non–goal-directed movements

2021 ◽  
Vol 118 (6) ◽  
pp. e2006372118
Author(s):  
Naveen Sendhilnathan ◽  
Debaleena Basu ◽  
Michael E. Goldberg ◽  
Jeffrey D. Schall ◽  
Aditya Murthy
Keyword(s):  
Brain Area ◽  
Field Potential ◽  
Neural Correlates ◽  
Voluntary Control ◽  
Frontal Eye Field ◽  
Beta Band ◽  
Saccade Onset ◽  
Eye Field ◽  
Saccade Direction ◽  
The Brain

What are the cortical neural correlates that distinguish goal-directed and non–goal-directed movements? We investigated this question in the monkey frontal eye field (FEF), which is implicated in voluntary control of saccades. Here, we compared FEF activity associated with goal-directed (G) saccades and non–goal-directed (nG) saccades made by the monkey. Although the FEF neurons discharged before these nG saccades, there were three major differences in the neural activity: First, the variability in spike rate across trials decreased only for G saccades. Second, the local field potential beta-band power decreased during G saccades but did not change during nG saccades. Third, the time from saccade direction selection to the saccade onset was significantly longer for G saccades compared with nG saccades. Overall, our results reveal unexpected differences in neural signatures for G versus nG saccades in a brain area that has been implicated selectively in voluntary control. Taken together, these data add critical constraints to the way we think about saccade generation in the brain.

scholarly journals Spontaneous Beta Band Rhythms in the Predictive Coding of Natural Stimuli

2020 ◽  
pp. 107385842092898 ◽  
Author(s):  
Viviana Betti ◽  
Stefania Della Penna ◽  
Francesco de Pasquale ◽  
Maurizio Corbetta
Keyword(s):  
Predictive Coding ◽  
Physical World ◽  
Brain Regions ◽  
Temporal Coding ◽  
Strong Interactions ◽  
Beta Band ◽  
Cognitive Representations ◽  
Band Power ◽  
The Brain

The regularity of the physical world and the biomechanics of the human body movements generate distributions of highly probable states that are internalized by the brain in the course of a lifetime. In Bayesian terms, the brain exploits prior knowledge, especially under conditions when sensory input is unavailable or uncertain, to predictively anticipate the most likely outcome of upcoming stimuli and movements. These internal models, formed during development, yet still malleable in adults, continuously adapt through the learning of novel stimuli and movements. Traditionally, neural beta (β) oscillations are considered essential for maintaining sensorimotor and cognitive representations, and for temporal coding of expectations. However, recent findings show that fluctuations of β band power in the resting state strongly correlate between cortical association regions. Moreover, central (hub) regions form strong interactions over time with different brain regions/networks (dynamic core). β band centrality fluctuations of regions of the dynamic core predict global efficiency peaks suggesting a mechanism for network integration. Furthermore, this temporal architecture is surprisingly stable, both in topology and dynamics, during the observation of ecological natural visual scenes, whereas synthetic temporally scrambled stimuli modify it. We propose that spontaneous β rhythms may function as a long-term “prior” of frequent environmental stimuli and behaviors.

scholarly journals The N300: An Index For Predictive Coding Of Complex Visual Objects and Scenes

2020 ◽  
Author(s):  
Manoj Kumar ◽  
Kara D. Federmeier ◽  
Diane M. Beck
Keyword(s):  
Prior Knowledge ◽  
Prediction Error ◽  
Predictive Coding ◽  
Sensory Information ◽  
Stimulus Onset ◽  
Event Related Potential ◽  
Context Dependency ◽  
Visual Objects ◽  
Complex Scenes ◽  
Neural Signature

AbstractThe bulk of support for predictive coding models has come from the models’ ability to simulate known perceptual or neuronal phenomena, but there have been fewer attempts to identify a reliable neural signature of predictive coding. Here we propose that the N300 component of the event-related potential (ERP), occurring 250-350 ms post-stimulus-onset, may be such a signature of perceptual hypothesis testing operating at the scale of whole objects and scenes. We show that N300 amplitudes are smaller to representative (“good exemplars”) compared to less representative (“bad exemplars”) items from natural scene categories. Integrating these results with patterns observed for objects, we establish that, across a variety of visual stimuli, the N300 is responsive to statistical regularity, or the degree to which the input is “expected” (either explicitly or implicitly) by the system based on prior knowledge, with statistically regular images, which entail reduced prediction error, evoking a reduced response. Moreover, we show that the measure exhibits context-dependency; that is, we find the N300 sensitivity to category representativeness only when stimuli are congruent with and not when they are incongruent with a category pre-cue, suggesting that the component may reflect the ease with which an image matches the current hypothesis generated by the visual system. Thus, we argue that the N300 ERP component is the best candidate to date for an index of perceptual hypotheses testing, whereby incoming sensory information for complex visual objects and scenes is accessed against contextual predictions generated in mid-level visual areas.Significance StatementPredictive coding models postulate that our perception of visual sensory input is guided by prior knowledge and the situational context, such that it is facilitated when the input matches expectation and hence produces less prediction error. Here, we show that an electrophysiological measure, the N300, matches the features hypothesized for a measure of predictive coding: complex scenes (like objects) elicit less N300 activity when they are statistically regular (e.g., more representative of their categories), in a manner that itself is context dependent. We thus show that the N300 provides a window into the interaction of context, prediction, and visual perception.

scholarly journals Expectation and attention increase the integration of top-down and bottom-up signals in perception through different pathways

2018 ◽  
Author(s):  
Noam Gordon ◽  
Naotsugu Tsuchiya ◽  
Roger Koenig-Robert ◽  
Jakob Hohwy
Keyword(s):  
Predictive Coding ◽  
Sensory Information ◽  
Phase Coherence ◽  
Signal Integration ◽  
Physiological Measures ◽  
Top Down ◽  
Bottom Up ◽  
Visual Hierarchy ◽  
Linear Signal ◽  
The Brain

AbstractPerception results from the integration of incoming sensory information with pre-existing information available in the brain. In this EEG (electroencephalography) study we utilised the Hierarchical Frequency Tagging method to examine how such integration is modulated by expectation and attention. Using intermodulation (IM) components as a measure of non-linear signal integration, we show in three different experiments that both expectation and attention enhance integration between top-down and bottom-up signals. Based on multispectral phase coherence, we present two direct physiological measures to demonstrate the distinct yet related mechanisms of expectation and attention. Specifically, our results link expectation to the modulation of prediction signals and the integration of top-down and bottom-up information at lower levels of the visual hierarchy. Meanwhile, they link attention to the propagation of ascending signals and the integration of information at higher levels of the visual hierarchy. These results are consistent with the predictive coding account of perception.

scholarly journals The Adaptation Model Offers a Challenge for the Predictive Coding Account of Mismatch Negativity

2021 ◽  
Vol 15 ◽  
Author(s):  
Patrick J. C. May
Keyword(s):  
Mismatch Negativity ◽  
Predictive Coding ◽  
Sensory Information ◽  
Simulation Models ◽  
Short Term ◽  
The Past ◽  
Control Paradigm ◽  
Set Up ◽  
The Brain ◽  
Adaptation Model

An unpredictable stimulus elicits a stronger event-related response than a high-probability stimulus. This differential in response magnitude is termed the mismatch negativity (MMN). Over the past decade, it has become increasingly popular to explain the MMN terms of predictive coding, a proposed general principle for the way the brain realizes Bayesian inference when it interprets sensory information. This perspective article is a reminder that the issue of MMN generation is far from settled, and that an alternative model in terms of adaptation continues to lurk in the wings. The adaptation model has been discounted because of the unrealistic and simplistic fashion in which it tends to be set up. Here, simulations of auditory cortex incorporating a modern version of the adaptation model are presented. These show that locally operating short-term synaptic depression accounts both for adaptation due to stimulus repetition and for MMN responses. This happens even in cases where adaptation has been ruled out as an explanation of the MMN (e.g., in the stimulus omission paradigm and the multi-standard control paradigm). Simulation models that would demonstrate the viability of predictive coding in a similarly multifaceted way are currently missing from the literature, and the reason for this is discussed in light of the current results.

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