scholarly journals Enhanced mismatch negativity responses in harmonic compared to inharmonic sound sequences

2021 ◽  
Author(s):  
David Ricardo Quiroga-Martinez ◽  
Krzysztof Basinski ◽  
Jonathan Nasielski ◽  
Barbara Tillmann ◽  
Elvira Brattico ◽  
...  
Keyword(s):  
Information Entropy ◽  
Mismatch Negativity ◽  
Predictive Coding ◽  
Predictive Processing ◽  
Oddball Paradigm ◽  
Auditory Information ◽  
Prediction Errors ◽  
Frequency Spectra ◽  
Auditory Information Processing ◽  
The Difference

Many natural sounds have frequency spectra composed of integer multiples of a fundamental frequency. This property, known as harmonicity, plays an important role in auditory information processing. However, the extent to which harmonicity influences the processing of sound features beyond pitch is still unclear. This is interesting because harmonic sounds have lower information entropy than inharmonic sounds. According to predictive processing accounts of perception, this property could produce more salient neural responses due to the brain weighting of sensory signals according to their uncertainty. In the present study, we used electroencephalography to investigate brain responses to harmonic and inharmonic sounds commonly occurring in music: piano tones and hi-hat cymbal sounds. In a multi-feature oddball paradigm, we measured mismatch negativity (MMN) and P3a responses to timbre, intensity, and location deviants in listeners with and without congenital amusia, an impairment of pitch processing. As hypothesized, we observed larger amplitudes and earlier latencies for harmonic compared to inharmonic sounds for both MMN and P3a responses. These harmonicity effects were modulated by sound feature. Moreover, the difference in P3a latency between harmonic and inharmonic sounds was larger for controls than amusics. We propose an explanation of these results based on predictive coding and discuss the relationship between harmonicity, information entropy, and precision weighting of prediction errors.

scholarly journals the Effect of Quetiapine on Auditory p300 Response in Patients with Schizoaffective Disorder: Preliminary Study

2009 ◽  
Vol 24 (S1) ◽  
pp. 1-1
Author(s):  
M. Korostenskaja ◽  
K. Dapsys ◽  
A. Siurkute ◽  
A. Dudlauskaite ◽  
A. Pragaraviciene ◽  
...  
Keyword(s):  
Information Processing ◽  
Schizoaffective Disorder ◽  
Event Related Potential ◽  
Oddball Paradigm ◽  
P300 Latency ◽  
Auditory Information ◽  
Auditory Oddball ◽  
Auditory P300 ◽  
Active Attention ◽  
Auditory Information Processing

Abnormalities in attention, memory and information processing are considered to be the primary deficits in schizophrenia. Event-related potential (ERP) P300 could reflect deficits in auditory information processing related to active attention in schizophrenia patients. Atypical antipsychotics tend to ameliorate cognitive deficits, however their effects on neural aspects of cognitive dysfunction have not been consistent.Aim:To investigate the effects of quetiapine on auditory information processing by using auditory P300.Methods:We examined 7 patients with schizoaffective disorder, depressive type and 7 age- and sex-matched healthy controls. ERPs were elicited during active auditory “oddball” paradigm. P300 was recorded before and after two weeks of treatment with quetiapine (468.7±143 mg/day).Results:Baseline P300 latencies were significantly delayed in patients compared with controls. Quetiapine did not change P300 amplitudes. However, it normalized P300 latency. These results suggest that already after two weeks of treatment, quetiapine could have a beneficial effect on the active attention reflected in P300 in patients with schizoaffective disorder. Previous studies with antipsychotic drugs such as olanzapine and risperidone failed to show changes in P300 after this treatment interval.Conclusion:Quetiapine may be faster than other neuroleptics in ameliorating attentional dysfunction in patients with schizoaffective disorder. However, studies with a larger sample size must be conducted in order to confirm or reject the results of the current study.

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 Constructing the hierarchy of predictive auditory sequences in the marmoset brain

2021 ◽  
Author(s):  
Yuwei Jiang ◽  
Misako Komatsu ◽  
Yuyan Chen ◽  
Ruoying Xie ◽  
Kaiwei Zhang ◽  
...  
Keyword(s):  
Auditory Processing ◽  
Time Scale ◽  
Essential Feature ◽  
Predictive Coding ◽  
Auditory Pathway ◽  
Brain Regions ◽  
Predictive Processing ◽  
Short Time Scale ◽  
Prediction Errors ◽  
Surface Areas

Our brains constantly generate predictions of sensory input that are compared with actual inputs, propagate the prediction-errors through a hierarchy of brain regions, and subsequently update the internal predictions of the world. However, the essential feature of predictive coding, the notion of hierarchical depth and its neural mechanisms, remains largely unexplored. Here, we investigated the hierarchical depth of predictive auditory processing by combining functional magnetic resonance imaging (fMRI) and high-density whole-brain electrocorticography (ECoG) in marmoset monkeys during an auditory local-global paradigm in which the temporal regularities of the stimuli were designed at two hierarchical levels. The prediction-errors and prediction updates were examined as neural responses to auditory mismatches and omissions. Using fMRI, we identified a hierarchical gradient along the auditory pathway: midbrain and sensory regions represented local, short-time-scale predictive processing followed by associative auditory regions, whereas anterior temporal and prefrontal areas represented global, long-time-scale sequence processing. The complementary ECoG recordings confirmed the activations at cortical surface areas and further differentiated the signals of prediction-error and update, which were transmitted via putatively bottom-up γ and top-down β oscillations, respectively. Furthermore, omission responses caused by absence of input, reflecting solely the two levels of prediction signals that are unique to the hierarchical predictive coding framework, demonstrated the hierarchical predictions in the auditory, temporal, and prefrontal areas. Thus, our findings support the hierarchical predictive coding framework, and outline how neural circuits and spatiotemporal dynamics are used to represent and arrange a hierarchical structure of auditory sequences in the marmoset brain.

Auditory information processing (oddball paradigm): An impact of COMT polymorphism

2008 ◽  
Author(s):  
I. S. Lebedeva ◽  
G. I. Korovaitseva ◽  
T. V. Lezheiko ◽  
V. G. Kaleda ◽  
L. I. Abramova ◽  
...  
Keyword(s):  
Information Processing ◽  
Oddball Paradigm ◽  
Auditory Information ◽  
Auditory Information Processing ◽  
Comt Polymorphism

scholarly journals Ketamine Affects Prediction Errors about Statistical Regularities: A Computational Single-Trial Analysis of the Mismatch Negativity

2019 ◽  
Author(s):  
Lilian A. Weber ◽  
Andreea O. Diaconescu ◽  
Christoph Mathys ◽  
André Schmidt ◽  
Michael Kometer ◽  
...  
Keyword(s):  
Mismatch Negativity ◽  
Transition Probability ◽  
Predictive Coding ◽  
Prediction Errors ◽  
Hierarchical Bayesian ◽  
Single Trial ◽  
Perceptual Inference ◽  
Trial Analysis ◽  
Statistical Regularities ◽  
High Level

AbstractThe auditory mismatch negativity (MMN) is significantly reduced in schizophrenia. Notably, a similar MMN reduction can be achieved with NMDA receptor (NMDAR) antagonists. Both phenomena have been interpreted as reflecting an impairment of predictive coding or, more generally, the “Bayesian brain” notion that the brain continuously updates a hierarchical model to infer the causes of its sensory inputs. Specifically, predictive coding views perceptual inference as an NMDAR-dependent process of minimizing hierarchical precision-weighted prediction errors (PEs). Disturbances of this putative process play a key role in hierarchical Bayesian theories of schizophrenia.Here, we provide empirical evidence for this clinical theory, demonstrating the existence of multiple, hierarchically related PEs in a “roving MMN” paradigm. We applied a computational model, the Hierarchical Gaussian Filter (HGF), to single-trial EEG data from healthy volunteers that received the NMDAR antagonist S-ketamine in a placebo-controlled, double-blind, within-subject fashion. Using an unrestricted analysis of the entire time-sensor space, our computational trial-by-trial analysis indicated that low-level PEs (about stimulus transitions) are expressed early (102-207ms post-stimulus), while high-level PEs (about transition probability) are reflected by later components (152-199ms, 215-277ms) of single-trial responses. Furthermore, we find that ketamine significantly diminished the expression of high-level PE responses, implying that NMDAR antagonism disrupts inference on abstract statistical regularities.Our findings are consistent with long-standing notions that NMDAR dysfunction may cause positive symptoms in schizophrenia by impairing hierarchical Bayesian inference about the world’s statistical structure. Beyond their relevance for schizophrenia, our results illustrate the potential of computational single-trial analyses for assessing potential disease mechanisms.

scholarly journals The Predictive Dynamics of Happiness and Well-Being

2021 ◽  
pp. 175407392110638
Author(s):  
Mark Miller ◽  
Erik Rietveld ◽  
Julian Kiverstein
Keyword(s):  
Mental Health ◽  
Well Being ◽  
Predictive Processing ◽  
Prediction Errors ◽  
Affective States ◽  
Human Flourishing ◽  
Making Sense ◽  
The Difference ◽  
Momentary Happiness ◽  
Error Dynamics

We offer an account of mental health and well-being using the predictive processing framework (PPF). According to this framework, the difference between mental health and psychopathology can be located in the goodness of the predictive model as a regulator of action. What is crucial for avoiding the rigid patterns of thinking, feeling and acting associated with psychopathology is the regulation of action based on the valence of affective states. In PPF, valence is modelled as error dynamics—the change in prediction errors over time . Our aim in this paper is to show how error dynamics can account for both momentary happiness and longer term well-being. What will emerge is a new neurocomputational framework for making sense of human flourishing.

scholarly journals Oversampled and undersolved: Depressive rumination from a predictive coding perspective

2021 ◽  
Author(s):  
Max Berg ◽  
Matthias Feldmann ◽  
Tobias Kube
Keyword(s):  
Problem Solving ◽  
Predictive Coding ◽  
Instrumental Learning ◽  
Mental Simulation ◽  
Clinical Findings ◽  
Predictive Processing ◽  
Prediction Errors ◽  
Depressive Rumination ◽  
Bayesian Sampling ◽  
Integrative View

Rumination is a widely recognized cognitive deviation in depression. An integrative view that combines clinical findings on rumination with theories of mental simulation and cognitive problem-solving could help explain the development and maintenance of rumination in a computationally and biologically plausible framework. In this review, we connect insights from neuroscience and computational psychiatry to elucidate rumination as repetitive but unsuccessful attempts at mental problem-solving. Appealing to a predictive processing account, we suggest that problem-solving is based on an algorithm that generates candidate behavior (policy primitives for problem solutions) using a Bayesian sampling approach, evaluates resulting policies for action, and then engages in instrumental learning to reduce prediction errors. We present evidence suggesting that this problem-solving algorithm is distorted in depression: Specifically, depressive rumination is regarded as excessive Bayesian sampling of candidates that is associated with high prediction errors without activation of the successive steps (policy evaluation, instrumental learning) of the algorithm. Thus, prediction errors cannot be decreased, and excessive resampling of the same problems occur. This then leads to reduced precision weighting attributed to external, “online” stimuli, low behavioral output and high opportunity costs due to the time-consuming nature of the sampling process itself. We review different computational reasons that make the proposed Bayesian sampling algorithm vulnerable to a ruminative „halting problem”. We also identify neurophysiological correlates of these deviations in pathological connectivity patterns of different brain networks. We conclude by suggesting future directions for research into behavioral and neurophysiological features of the model and point to clinical implications.

scholarly journals Learning of complex auditory patterns changes intrinsic and feedforward effective connectivity between Heschl’s gyrus and planum temporale

2019 ◽  
Author(s):  
Massimo Lumaca ◽  
Martin J. Dietz ◽  
Niels Chr. Hansen ◽  
David R. Quiroga-Martinez ◽  
Peter Vuust
Keyword(s):  
Prediction Error ◽  
Predictive Coding ◽  
Fmri Data ◽  
Oddball Paradigm ◽  
Prediction Errors ◽  
Planum Temporale ◽  
Causal Modelling ◽  
Heschl’S Gyrus ◽  
Heschl's Gyrus ◽  
Dynamic Causal Modelling

AbstractLearning of complex auditory sequences such as language and music can be thought of as the continuous optimisation of internal predictive representations of sound-pattern regularities, driven by prediction errors. In predictive coding (PC), this occurs through changes in the intrinsic and extrinsic connectivity of the relevant cortical networks, whereby minimization of precision-weighted prediction error signals improves the accuracy of future predictions. Here, we employed Dynamic Causal Modelling (DCM) on functional magnetic resonance (fMRI) data acquired during the presentation of complex auditory patterns. In an oddball paradigm, we presented 52 volunteers (non-musicians) with isochronous 5-tone melodic patterns (standards), randomly interleaved with rare novel patterns comprising contour or pitch interval changes (deviants). Here, listeners must update their standard melodic models whenever they encounter unexpected deviant stimuli. Contour deviants induced an increased BOLD response, as compared to standards, in primary (Heschl’s gyrus, HG) and secondary auditory cortices (planum temporale, PT). Within this network, we found a left-lateralized increase in feedforward connectivity from HG to PT for deviant responses and a concomitant disinhibition within left HG. Consistent with PC, our results suggest that model updating in auditory pattern perception and learning is associated with specific changes in the excitatory feedforward connections encoding prediction errors and in the intrinsic connections that encode the precision of these errors and modulate their gain.Significance statementThe learning of complex auditory stimuli such as music and speech can be thought of as the continuous optimisation of brain predictive models driven by prediction errors. Using dynamic causal modelling on fMRI data acquired during a melodic oddball paradigm, we here show that brain responses to unexpected sounds were best explained by an increase in excitation within Heschl’s gyrus and an increase in forward connections from Heschl’s gyrus to planum temporale. Our results are consistent with a predictive coding account of sensory learning, whereby prediction error responses to new sounds drive model adjustments via feedforward connections and intrinsic connections encode the confidence of these prediction errors.

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