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

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.

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

Journal of Neuroscience ◽

10.1523/jneurosci.3069-19.2020 ◽

2020 ◽

Vol 40 (29) ◽

pp. 5658-5668 ◽

Cited By ~ 3

Author(s):

Lilian A. Weber ◽

Andreea O. Diaconescu ◽

Christoph Mathys ◽

André Schmidt ◽

Michael Kometer ◽

...

Keyword(s):

Mismatch Negativity ◽

Prediction Errors ◽

Single Trial ◽

Single Trial Analysis ◽

Trial Analysis ◽

Statistical Regularities

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Measurement of mismatch negativity in individuals: A study using single-trial analysis

Psychophysiology ◽

10.1111/j.1469-8986.2009.00970.x ◽

2010 ◽

Cited By ~ 10

Author(s):

D. V. M. Bishop ◽

M. J. Hardiman

Keyword(s):

Mismatch Negativity ◽

Single Trial ◽

Single Trial Analysis ◽

Trial Analysis

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T64. LINKING SUBCLINICAL PERSECUTORY IDEATION TO INFLEXIBLE SOCIAL INFERENCE UNDER UNCERTAINTY

Schizophrenia Bulletin ◽

10.1093/schbul/sbaa029.624 ◽

2020 ◽

Vol 46 (Supplement_1) ◽

pp. S255-S256

Author(s):

Katharina Wellstein ◽

Andreea Diaconescu ◽

Christoph Mathys ◽

Martin Bischof ◽

Annia Rüesch ◽

...

Keyword(s):

Probabilistic Reasoning ◽

Predictive Coding ◽

First Episode ◽

Model Parameters ◽

Prediction Errors ◽

Prior Beliefs ◽

Significant Group ◽

Social Inference ◽

Adaptive Use ◽

High Level

Abstract Background Persecutory delusions (PD) are a prominent symptom in first episode psychosis and psychosis patients. PD have been linked to abnormalities in probabilistic reasoning and social inference (e.g., attribution styles). Predictive Coding theories of delusion formation suggest that rigid delusional beliefs could be formalized as precise (i.e. held with certainty) high-level prior beliefs, which were formed to explain away overly precise low-level prediction errors (PEs). Rigid reliance on high-level prior beliefs would in turn lead to diminished updating of high-level PEs, i.e. decreased learning and updating of high-level beliefs. Methods We tested the prediction that subclinical PD ideation is related to altered social inference and beliefs about others’ intentions. To that end, N=1’145 participants from the general population were pre-screened with the Paranoia Checklist (PCL) and assigned to groups of high (“high PD”) or low PD tendencies (“low PD”). Participants with intermediate scores were excluded, participants assigned to either group filled in the PCL again after four weeks, only individuals whose score remained inside the cut-offs for either group were subsequently invited to the study. We invited 162 participants and included 151 participants in the analyses based on exclusion criteria defined in an analysis plan, which was time-stamped before the conclusion of data acquisition. Participants performed a probabilistic advice-taking task with dynamic changes in the advice-outcome mapping (volatility) under one of two experimental frames. These frames differentially emphasised possible reasons behind unhelpful advice: (i) the adviser’s possible intentions (dispositional frame) or (ii) the rules of the game (situational frame). Our design was thus 2-by-2 factorial (high vs. low delusional ideation, dispositional vs. situational frame). Participants were matched regarding age, gender, and education in years. In addition to analyses of variance on participants’ behaviour, we applied computational modeling to test the predictions regarding prior beliefs and belief updating mentioned above. Results We found significant group-by-frame interactions, indicating that in the situational frame high PD participants took advice less into account than low scorers (df = (1,150), F = 5.77, p = 0.018, partial η2= 0.04). This was also reflected in the model parameters of the model explaining participants’ learning under uncertainty best in comparison to other learning models (e.g. tonic evolution rate omega2: df = (1,150), F = 4.75, p = 0.03). Discussion Our findings suggest that social inference in individuals with subclinical PD tendencies is shaped by rigid negative prior beliefs about the intentions of others. High PD participants were less sensitive to the attributional framing and updated their beliefs less vs. low PD participants thereby preventing them to make adaptive use of social information in “safe” contexts.

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

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1525030113 ◽

2016 ◽

Vol 113 (24) ◽

pp. 6755-6760 ◽

Cited By ~ 62

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.

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Enhanced mismatch negativity responses in harmonic compared to inharmonic sound sequences

10.1101/2021.10.15.464562 ◽

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.

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Temporal Prediction Errors Affect Short-Term Memory Scanning Response Time

Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) ◽

10.1027/1618-3169/a000339 ◽

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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