scholarly journals Mismatch Negativity and Stimulus-Preceding Negativity in Paradigms of Increasing Auditory Complexity: A Possible Role in Predictive Coding

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 346
Author(s):  
Francisco J. Ruiz-Martínez ◽  
Antonio Arjona ◽  
Carlos M. Gómez

The auditory mismatch negativity (MMN) has been considered a preattentive index of auditory processing and/or a signature of prediction error computation. This study tries to demonstrate the presence of an MMN to deviant trials included in complex auditory stimuli sequences, and its possible relationship to predictive coding. Additionally, the transfer of information between trials is expected to be represented by stimulus-preceding negativity (SPN), which would possibly fit the predictive coding framework. To accomplish these objectives, the EEG of 31 subjects was recorded during an auditory paradigm in which trials composed of stimulus sequences with increasing or decreasing frequencies were intermingled with deviant trials presenting an unexpected ending. Our results showed the presence of an MMN in response to deviant trials. An SPN appeared during the intertrial interval and its amplitude was reduced in response to deviant trials. The presence of an MMN in complex sequences of sounds and the generation of an SPN component, with different amplitudes in deviant and standard trials, would support the predictive coding framework.

2019 ◽  
Vol 49 (12) ◽  
pp. 1597-1609 ◽  
Author(s):  
Massimo Lumaca ◽  
Niels Trusbak Haumann ◽  
Elvira Brattico ◽  
Manon Grube ◽  
Peter Vuust

2019 ◽  
Vol 49 (07) ◽  
pp. 1195-1206 ◽  
Author(s):  
Amanda McCleery ◽  
Daniel H. Mathalon ◽  
Jonathan K. Wynn ◽  
Brian J. Roach ◽  
Gerhard S. Hellemann ◽  
...  

AbstractBackgroundMismatch negativity (MMN) is an event-related potential (ERP) component reflecting auditory predictive coding. Repeated standard tones evoke increasing positivity (‘repetition positivity’; RP), reflecting strengthening of the standard's memory trace and the prediction it will recur. Likewise, deviant tones preceded by more standard repetitions evoke greater negativity (‘deviant negativity’; DN), reflecting stronger prediction error signaling. These memory trace effects are also evident in MMN difference wave. Here, we assess group differences and test-retest reliability of these indices in schizophrenia patients (SZ) and healthy controls (HC).MethodsElectroencephalography was recorded twice, 2 weeks apart, from 43 SZ and 30 HC, during a roving standard paradigm. We examined ERPs to the third, eighth, and 33rd standards (RP), immediately subsequent deviants (DN), and the corresponding MMN. Memory trace effects were assessed by comparing amplitudes associated with the three standard repetition trains.ResultsCompared with controls, SZ showed reduced MMNs and DNs, but normal RPs. Both groups showed memory trace effects for RP, MMN, and DN, with a trend for attenuated DNs in SZ. Intraclass correlations obtained via this paradigm indicated good-to-moderate reliabilities for overall MMN, DN and RP, but moderate to poor reliabilities for components associated with short, intermediate, and long standard trains, and poor reliability of their memory trace effects.ConclusionMMN deficits in SZ reflected attenuated prediction error signaling (DN), with relatively intact predictive code formation (RP) and memory trace effects. This roving standard MMN paradigm requires additional development/validation to obtain suitable levels of reliability for use in clinical trials.


2021 ◽  
Vol 15 ◽  
Author(s):  
Iria SanMiguel ◽  
Jordi Costa-Faidella ◽  
Zulay R. Lugo ◽  
Elisabet Vilella ◽  
Carles Escera

Electrophysiological sensory deviance detection signals, such as the mismatch negativity (MMN), have been interpreted from the predictive coding framework as manifestations of prediction error (PE). From a frequentist perspective of the classic oddball paradigm, deviant stimuli are unexpected because of their low probability. However, the amount of PE elicited by a stimulus can be dissociated from its probability of occurrence: when the observer cannot make confident predictions, any event holds little surprise value, no matter how improbable. Here we tested the hypothesis that the magnitude of the neural response elicited to an improbable sound (D) would scale with the precision of the prediction derived from the repetition of another sound (S), by manipulating repetition stability. We recorded the Electroencephalogram (EEG) from 20 participants while passively listening to 4 types of isochronous pure tone sequences differing in the probability of the S tone (880 Hz) while holding constant the probability of the D tone [1,046 Hz; p(D) = 1/11]: Oddball [p(S) = 10/11]; High confidence (7/11); Low confidence (4/11); and Random (1/11). Tones of 9 different frequencies were equiprobably presented as fillers [p(S) + p(D) + p(F) = 1]. Using a mass-univariate non-parametric, cluster-based correlation analysis controlling for multiple comparisons, we found that the amplitude of the deviant-elicited ERP became more negative with increasing S probability, in a time-electrode window consistent with the MMN (ca. 120–200 ms; frontal), suggesting that the strength of a PE elicited to an improbable event indeed increases with the precision of the predictive model.


2007 ◽  
Vol 21 (3-4) ◽  
pp. 204-213 ◽  
Author(s):  
Torsten Baldeweg

Neuronal adaptation is a ubiquitous property of the cortex. This review presents evidence from MMN studies that show ERP components with similar adaptive properties. Specifically, I consider the empirical evidence from the perspective of a predictive coding model of perceptual learning and inference. Within this framework, ERP and neuronal repetition effects (repetition suppression) are seen as reductions in prediction error, a process that requires synaptic modifications. Repetition positivity is a human auditory ERP component, which shows similar properties to stimulus-specific adaptation of auditory cortex neurons; a candidate mechanism for auditory trace formation.


2004 ◽  
Vol 25 (3) ◽  
pp. 284-301 ◽  
Author(s):  
Catharine M. Pettigrew ◽  
Bruce E. Murdoch ◽  
Curtis W. Ponton ◽  
Simon Finnigan ◽  
Paavo Alku ◽  
...  

2021 ◽  
Vol 92 (8) ◽  
pp. A3.3-A4
Author(s):  
Harriet Sharp ◽  
Kristy Themelis ◽  
Marisa Amato ◽  
Andrew Barritt ◽  
Kevin Davies ◽  
...  

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.


2020 ◽  
Author(s):  
Pramod Kaushik ◽  
Jérémie Naudé ◽  
Surampudi Bapi Raju ◽  
Frédéric Alexandre

AbstractClassical Conditioning is a fundamental learning mechanism where the Ventral Striatum is generally thought to be the source of inhibition to Ventral Tegmental Area (VTA) Dopamine neurons when a reward is expected. However, recent evidences point to a new candidate in VTA GABA encoding expectation for computing the reward prediction error in the VTA. In this system-level computational model, the VTA GABA signal is hypothesised to be a combination of magnitude and timing computed in the Peduncolopontine and Ventral Striatum respectively. This dissociation enables the model to explain recent results wherein Ventral Striatum lesions affected the temporal expectation of the reward but the magnitude of the reward was intact. This model also exhibits other features in classical conditioning namely, progressively decreasing firing for early rewards closer to the actual reward, twin peaks of VTA dopamine during training and cancellation of US dopamine after training.


2021 ◽  
Vol 11 (12) ◽  
pp. 1581
Author(s):  
Alexis E. Whitton ◽  
Kathryn E. Lewandowski ◽  
Mei-Hua Hall

Motivational and perceptual disturbances co-occur in psychosis and have been linked to aberrations in reward learning and sensory gating, respectively. Although traditionally studied independently, when viewed through a predictive coding framework, these processes can both be linked to dysfunction in striatal dopaminergic prediction error signaling. This study examined whether reward learning and sensory gating are correlated in individuals with psychotic disorders, and whether nicotine—a psychostimulant that amplifies phasic striatal dopamine firing—is a common modulator of these two processes. We recruited 183 patients with psychotic disorders (79 schizophrenia, 104 psychotic bipolar disorder) and 129 controls and assessed reward learning (behavioral probabilistic reward task), sensory gating (P50 event-related potential), and smoking history. Reward learning and sensory gating were correlated across the sample. Smoking influenced reward learning and sensory gating in both patient groups; however, the effects were in opposite directions. Specifically, smoking was associated with improved performance in individuals with schizophrenia but impaired performance in individuals with psychotic bipolar disorder. These findings suggest that reward learning and sensory gating are linked and modulated by smoking. However, disorder-specific associations with smoking suggest that nicotine may expose pathophysiological differences in the architecture and function of prediction error circuitry in these overlapping yet distinct psychotic disorders.


2018 ◽  
Author(s):  
Jonathan E. Robinson ◽  
Will Woods ◽  
Sumie Leung ◽  
Jordy Kaufman ◽  
Michael Breakspear ◽  
...  

AbstractPredictive coding theories of perception suggest the importance of constantly updated internal models of the world in predicting future sensory inputs. One implication of such models is that cortical regions whose function is to resolve particular stimulus attributes should also signal prediction violations with respect to those same stimulus attributes. Previously, through carefully designed experiments, we have demonstrated early-mid latency EEG/MEG prediction-error signals in the dorsal visual stream to violated expectations about stimulus orientation/trajectory, with localisations consistent with cortical areas processing motion and orientation. Here we extend those methods to simultaneously investigate the predictive processes in both dorsal and ventral visual streams. In this MEG study we employed a contextual trajectory paradigm that builds expectations using a series of image presentations. We created expectations about both face orientation and identity, either of which can subsequently be violated. Crucially this paradigm allows us to parametrically test double dissociations between these different types of violations. The study identified double dissociations across the type of violation in the dorsal and ventral visual streams, such that the right fusiform gyrus showed greater evidence of prediction-error signals to Identity violations than to Orientation violations, whereas the left angular gyrus and postcentral gyrus showed the opposite pattern of results. Our results suggest comparable processes for error checking and context updating in high-level expectations instantiated across both perceptual streams. Perceptual prediction-error signalling is initiated in regions associated with the processing of different stimulus properties.Significance StatementVisual processing occurs along ‘what’ and ‘where’ information streams that run, respectively along the ventral and dorsal surface of the posterior brain. Predictive coding models of perception imply prediction-error detection processes that are instantiated at the level where particular stimulus attributes are parsed. This implies that, for instance, when considering face stimuli, signals arising through violated expectations about the person identity of the stimulus should localise to the ventral stream, whereas signals arising through violated expectations about head orientation should localise to the dorsal stream. We test this in a magnetoencephalography source localisation study. The analysis confirmed that prediction-error signals to identity versus head-orientation occur with similar latency, but activate doubly-dissociated brain regions along ventral and dorsal processing streams.


2017 ◽  
Vol 118 (1) ◽  
pp. 374-382 ◽  
Author(s):  
Suchitra Ramachandran ◽  
Travis Meyer ◽  
Carl R. Olson

Exposing monkeys, over the course of days and weeks, to pairs of images presented in fixed sequence, so that each leading image becomes a predictor for the corresponding trailing image, affects neuronal visual responsiveness in area TE. At the end of the training period, neurons respond relatively weakly to a trailing image when it appears in a trained sequence and, thus, confirms prediction, whereas they respond relatively strongly to the same image when it appears in an untrained sequence and, thus, violates prediction. This effect could arise from prediction suppression (reduced firing in response to the occurrence of a probable event) or surprise enhancement (elevated firing in response to the omission of a probable event). To identify its cause, we compared firing under the prediction-confirming and prediction-violating conditions to firing under a prediction-neutral condition. The results provide strong evidence for prediction suppression and limited evidence for surprise enhancement. NEW & NOTEWORTHY In predictive coding models of the visual system, neurons carry signed prediction error signals. We show here that monkey inferotemporal neurons exhibit prediction-modulated firing, as posited by these models, but that the signal is unsigned. The response to a prediction-confirming image is suppressed, and the response to a prediction-violating image may be enhanced. These results are better explained by a model in which the visual system emphasizes unpredicted events than by a predictive coding model.


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