scholarly journals Direct evidence for prediction signals in frontal cortex independent of prediction error

2018 ◽  
Author(s):  
Stefan Dürschmid ◽  
Christoph Reichert ◽  
Hermann Hinrichs ◽  
Hans-Jochen Heinze ◽  
Heidi E. Kirsch ◽  
...  
Keyword(s):  
Frontal Cortex ◽  
High Frequency ◽  
Prediction Error ◽  
Direct Evidence ◽  
Predictive Coding ◽  
Indirect Evidence ◽  
Complex Environments ◽  
Single Trial ◽  
Stimulus Response ◽  
Task Irrelevant

AbstractPredictive coding (PC) has been suggested as one of the main mechanisms used by brains to interact with complex environments. PC theories posit top-down prediction signals, which are compared with actual outcomes, yielding in turn prediction-error signals, which are used, bottom-up, to modify the ensuing predictions. However, disentangling prediction from prediction-error signals has been challenging. Critically, while many studies found indirect evidence for predictive coding in the form of prediction-error signals, direct evidence for the prediction signal is mostly lacking. Here we provide clear evidence, obtained from intracranial cortical recordings in human surgical patients, that the human lateral prefrontal cortex generates prediction signals while anticipating an event. Patients listened to task-irrelevant sequences of repetitive tones including infrequent predictable or unpredictable pitch deviants. The amplitude of high frequency broadband (HFB) neural activity was decreased prior to the onset of expected relative to unexpected deviants in the frontal cortex only, and its amplitude was sensitive to the increasing likelihood of deviants following longer trains of standards in the unpredictable condition. Single trial HFB amplitudes predicted deviations and correlated with post-stimulus response to deviations. These results provide direct evidence for frontal cortex prediction signals independent of prediction-error signals.

scholarly journals Direct Evidence for Prediction Signals in Frontal Cortex Independent of Prediction Error

2018 ◽  
Vol 29 (11) ◽  
pp. 4530-4538 ◽  
Author(s):  
Stefan Dürschmid ◽  
Christoph Reichert ◽  
Hermann Hinrichs ◽  
Hans-Jochen Heinze ◽  
Heidi E Kirsch ◽  
...  
Keyword(s):  
Frontal Cortex ◽  
High Frequency ◽  
Prediction Error ◽  
Direct Evidence ◽  
Predictive Coding ◽  
Indirect Evidence ◽  
Surgical Patients ◽  
Complex Environments ◽  
Single Trial ◽  
Task Irrelevant

Abstract Predictive coding (PC) has been suggested as one of the main mechanisms used by brains to interact with complex environments. PC theories posit top-down prediction signals, which are compared with actual outcomes, yielding in turn prediction error (PE) signals, which are used, bottom-up, to modify the ensuing predictions. However, disentangling prediction from PE signals has been challenging. Critically, while many studies found indirect evidence for PC in the form of PE signals, direct evidence for the prediction signal is mostly lacking. Here, we provide clear evidence, obtained from intracranial cortical recordings in human surgical patients, that the human lateral prefrontal cortex evinces prediction signals while anticipating an event. Patients listened to task-irrelevant sequences of repetitive tones including infrequent predictable or unpredictable pitch deviants. The broadband high-frequency amplitude (HFA) was decreased prior to the onset of expected relative to unexpected deviants in the frontal cortex only, and its amplitude was sensitive to the increasing likelihood of deviants following longer trains of standards in the unpredictable condition. Single-trial HFA predicted deviations and correlated with poststimulus response to deviations. These results provide direct evidence for frontal cortex prediction signals independent of PE signals.

f max

1982 ◽  
Vol 72 (6A) ◽  
pp. 1867-1879
Author(s):  
Thomas C. Hanks
Keyword(s):  
Shear Wave ◽  
High Frequency ◽  
Direct Evidence ◽  
Narrow Band ◽  
Tectonic Setting ◽  
Indirect Evidence ◽  
Local Site ◽  
Single Station ◽  
Site Variation ◽  
Surface Geology

abstract The title of this contribution refers to the high-frequency band-limitation of the radiated field of earthquakes. For close recording distances of ≃10 km, fmax for California earthquakes is generally found within the very narrow band 10 ≲ fmax ≲ 20 Hz, without regard to source strength or tectonic setting. Direct evidence for fmax (in the form of shear-wave acceleration spectral amplitudes) and indirect evidence as well (in the form of limiting spectral corner frequencies) have often been taken as manifestations of source properties, barrier end-zones, for example. This study concludes that fmax as actually observed is more likely to be a property of local site conditions, although this does not preclude source-controlled band-limitations at still higher frequencies. Observations of fmax for a single Oroville aftershock show a small but significant site-to-site variation that correlates well with gross surface geology; this variation is “normalized” with a demonstration that fmax for seven of the principal aftershocks (4.0 ≦ ML ≦ 4.9) at a single station has a distinctly smaller variation. Moreover, there seems to be nothing fundamental about the range 10 ≲ fmax ≲ 20 Hz so typical for California earthquakes; spectral shear-wave corner frequencies observed at very close distances (≲5 km) and for especially competent propagation paths are known to occupy the band 20 ≦ fo(S) ≦ 200 Hz.

scholarly journals Hierarchy of prediction errors for auditory events in human temporal and frontal cortex

2016 ◽  
Vol 113 (24) ◽  
pp. 6755-6760 ◽  
Author(s):  
Stefan Dürschmid ◽  
Erik Edwards ◽  
Christoph Reichert ◽  
Callum Dewar ◽  
Hermann Hinrichs ◽  
...  
Keyword(s):  
Frontal Cortex ◽  
Predictive Coding ◽  
Low Frequency ◽  
Event Related Potentials ◽  
Prediction Errors ◽  
Frequency Bands ◽  
Deviance Detection ◽  
Statistical Regularities ◽  
Related Potentials ◽  
Task Irrelevant

Predictive coding theories posit that neural networks learn statistical regularities in the environment for comparison with actual outcomes, signaling a prediction error (PE) when sensory deviation occurs. PE studies in audition have capitalized on low-frequency event-related potentials (LF-ERPs), such as the mismatch negativity. However, local cortical activity is well-indexed by higher-frequency bands [high-γ band (Hγ): 80–150 Hz]. We compared patterns of human Hγ and LF-ERPs in deviance detection using electrocorticographic recordings from subdural electrodes over frontal and temporal cortices. Patients listened to trains of task-irrelevant tones in two conditions differing in the predictability of a deviation from repetitive background stimuli (fully predictable vs. unpredictable deviants). We found deviance-related responses in both frequency bands over lateral temporal and inferior frontal cortex, with an earlier latency for Hγ than for LF-ERPs. Critically, frontal Hγ activity but not LF-ERPs discriminated between fully predictable and unpredictable changes, with frontal cortex sensitive to unpredictable events. The results highlight the role of frontal cortex and Hγ activity in deviance detection and PE generation.

scholarly journals Effects of awareness and task relevance on neurocomputational models of mismatch negativity generation

2021 ◽  
Author(s):  
Insa Schlossmacher ◽  
Felix Lucka ◽  
Maximilian Bruchmann ◽  
Thomas Straube
Keyword(s):  
Mismatch Negativity ◽  
Prediction Error ◽  
Computational Models ◽  
Sensory Input ◽  
Time Window ◽  
Single Trial ◽  
Task Relevance ◽  
Task Irrelevant ◽  
Adaptation Model ◽  
And Task

AbstractDetection of regularities and their violations in sensory input is key to perception. Violations are indexed by an early EEG component called the mismatch negativity (MMN) – even if participants are distracted or unaware of the stimuli. On a mechanistic level, two dominant models have been suggested to contribute to the MMN: adaptation and prediction. Whether and how context conditions, such as awareness and task relevance, modulate the mechanisms of MMN generation is unknown. We conducted an EEG study disentangling influences of task relevance and awareness on the visual MMN. Then, we estimated different computational models for the generation of single-trial amplitudes in the MMN time window. Amplitudes were best explained by a prediction error model when stimuli were task-relevant but by an adaptation model when task-irrelevant and unaware. Thus, mismatch generation does not rely on one predominant mechanism but mechanisms vary with task relevance of stimuli.

scholarly journals Reduced premovement positivity during the stimulus‐response interval precedes errors: Using single‐trial and regression ERPs to understand performance deficits in ADHD

2019 ◽  
pp. e13392 ◽  
Author(s):  
Scott J. Burwell ◽  
Scott Makeig ◽  
William G. Iacono ◽  
Stephen M. Malone
Keyword(s):  
Single Trial ◽  
Performance Deficits ◽  
Stimulus Response ◽  
Response Interval

scholarly journals No Change, No Life? What We Know about Phase Variation in Staphylococcus aureus

2021 ◽  
Vol 9 (2) ◽  
pp. 244
Author(s):  
Vishal Gor ◽  
Ryosuke L. Ohniwa ◽  
Kazuya Morikawa
Keyword(s):  
Gene Expression ◽  
Staphylococcus Aureus ◽  
High Frequency ◽  
Direct Evidence ◽  
Bacterial Species ◽  
Phase Variation ◽  
Opportunistic Pathogen ◽  
Adaptation Strategy ◽  
Innate Immune ◽  
Epigenetic Mechanisms

Phase variation (PV) is a well-known phenomenon of high-frequency reversible gene-expression switching. PV arises from genetic and epigenetic mechanisms and confers a range of benefits to bacteria, constituting both an innate immune strategy to infection from bacteriophages as well as an adaptation strategy within an infected host. PV has been well-characterized in numerous bacterial species; however, there is limited direct evidence of PV in the human opportunistic pathogen Staphylococcus aureus. This review provides an overview of the mechanisms that generate PV and focuses on earlier and recent findings of PV in S. aureus, with a brief look at the future of the field.

Denoising for full-waveform inversion with expanded prediction-error filters

Geophysics ◽  
2021 ◽  
pp. 1-54
Author(s):  
Milad Bader ◽  
Robert G. Clapp ◽  
Biondo Biondi
Keyword(s):  
High Frequency ◽  
Prediction Error ◽  
Signal To Noise Ratio ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Full Waveform Inversion ◽  
Frequency Data ◽  
Signal To Noise ◽  
Frequency Signal ◽  
Full Waveform

Low-frequency data below 5 Hz are essential to the convergence of full-waveform inversion towards a useful solution. They help build the velocity model low wavenumbers and reduce the risk of cycle-skipping. In marine environments, low-frequency data are characterized by a low signal-to-noise ratio and can lead to erroneous models when inverted, especially if the noise contains coherent components. Often field data are high-pass filtered before any processing step, sacrificing weak but essential signal for full-waveform inversion. We propose to denoise the low-frequency data using prediction-error filters that we estimate from a high-frequency component with a high signal-to-noise ratio. The constructed filter captures the multi-dimensional spectrum of the high-frequency signal. We expand the filter's axes in the time-space domain to compress its spectrum towards the low frequencies and wavenumbers. The expanded filter becomes a predictor of the target low-frequency signal, and we incorporate it in a minimization scheme to attenuate noise. To account for data non-stationarity while retaining the simplicity of stationary filters, we divide the data into non-overlapping patches and linearly interpolate stationary filters at each data sample. We apply our method to synthetic stationary and non-stationary data, and we show it improves the full-waveform inversion results initialized at 2.5 Hz using the Marmousi model. We also demonstrate that the denoising attenuates non-stationary shear energy recorded by the vertical component of ocean-bottom nodes.

#3083 Fibromyalgia and myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS): an interoceptive predictive coding model of pain and fatigue expression

2021 ◽  
Vol 92 (8) ◽  
pp. A3.3-A4
Author(s):  
Harriet Sharp ◽  
Kristy Themelis ◽  
Marisa Amato ◽  
Andrew Barritt ◽  
Kevin Davies ◽  
...  
Keyword(s):  
Prediction Error ◽  
Predictive Coding ◽  
Chronic Fatigue ◽  
Self Report ◽  
Subjective Experiences ◽  
Double Blind ◽  
Pain Thresholds ◽  
Pressure Pain Thresholds ◽  
Interoceptive Accuracy ◽  
Bodily States

IntroductionThe aetiology and pathophysiology of fibromyalgia and ME/CFS are poorly characterised but altered inflammatory, autonomic and interoceptive processes have been implicated. Interoception has been conceptualised as a predictive coding process; where top-down prediction signals compare to bottom-up afferents, resulting in prediction error signals indicating mismatch between expected and actual bodily states. Chronic dyshomeostasis and elevated interoceptive prediction error signals have been theorised to contribute to the expression of pain and fatigue in fibromyalgia and ME/CFS.Objectives/AimsTo investigate how altered interoception and prediction error relates to baseline expression of pain and fatigue in fibromyalgia and ME/CFS and in response to an inflammatory challenge.MethodsSixty-five patients with fibromyalgia and/or ME/CFS diagnosis and 26 matched controls underwent baseline assessment: self-report questionnaires assessing subjective pain and fatigue and objective measurements of pressure-pain thresholds. Participants received injections of typhoid (inflammatory challenge) or saline (placebo) in a randomised, double-blind, crossover design, then completed heartbeat tracking task (assessing interoceptive accuracy). Porges Body Questionnaire assessed interoceptive sensibility. Interoceptive prediction error (IPE) was calculated as discrepancy between objective accuracy and subjective sensibility.ResultsPatients with fibromyalgia and ME/CFS had significantly higher IPE (suggesting tendency to over-estimate interoceptive ability) and interoceptive sensibility, despite no differences in interoceptive accuracy. IPE and sensibility correlated positively with all self-report fatigue and pain measures, and negatively with pain thresholds. Following inflammatory challenge, IPE correlated negatively with the mismatch between subjective and objective measures of pain induced by inflammation.ConclusionsThis is the first study to reveal altered interoception processes in patients with fibromyalgia and ME/CFS, who are known to have dysregulated autonomic function. Notably, we found elevated IPE in patients, correlating with their subjective experiences of pain and fatigue. We hypothesise a predictive coding model, where mismatch between expected and actual internal bodily states (linked to autonomic dysregulation) results in prediction error signalling which could be metacognitively interpreted as chronic pain and fatigue. Our results demonstrate potential for further exploration of interoceptive processing in patients with fibromyalgia and ME/CFS, aiding understanding of these poorly defined conditions and providing potential new targets for diagnostic and therapeutic intervention.

Mechanisms underlying the increase in young-of-the-year Atlantic salmon (Salmo salar) density with habitat complexity

2008 ◽  
Vol 65 (9) ◽  
pp. 1956-1964 ◽  
Author(s):  
Oscar Venter ◽  
James W.A. Grant ◽  
Michelle V. Noël ◽  
Jae-woo Kim
Keyword(s):  
Atlantic Salmon ◽  
Salmo Salar ◽  
Habitat Complexity ◽  
Indirect Evidence ◽  
Growth Potential ◽  
Territory Size ◽  
Complex Environments ◽  
Atlantic Salmon Salmo Salar ◽  
Direct Support ◽  
Young Of The Year

We tested three hypotheses used to explain the increase in young-of-the-year (YOY) Atlantic salmon ( Salmo salar ) density with habitat complexity: the territory-size, predator-refuge, and foraging-benefits hypotheses. We manipulated habitat complexity in three different treatments (boulder-removed, control, and boulder-added) at eight sites in Catamaran Brook and the Little Southwest Miramichi River, New Brunswick. The density of juvenile salmon was two times higher in the boulder-added treatment than in the other treatments. Our data were consistent with the territory-size hypothesis; visual isolation was highest and territory size was smallest in the boulder-added treatment, and salmon selected microhabitats to maximize their field of view. Our results showed partial support for the predator-refuge hypothesis; salmon in the boulder-added sites were closer to cover and showed a reduced reaction distance to a novel stimulus, but did not preferentially select microhabitats closer to cover. We found no direct support for the foraging-benefits hypothesis; however, there is indirect evidence that boulders may improve the growth potential of instream habitat. Our results suggest that YOY Atlantic salmon may be attracted to complex environments for the increased cover and that the decreased visibility of these sites causes a reduction in territory size, allowing a higher density of fish.

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