Hierarchical timescales of statistical learning revealed by mismatch negativity to auditory pattern deviations

Conditional deviant repetition in the oddball paradigm modulates processing at the level of P3a but not MMN

The auditory system has an amazing ability to rapidly encode auditory regularities. Evidence comes from the popular oddball paradigm, in which frequent (standard) sounds are occasionally exchanged for rare deviant sounds, which then elicit signs of prediction error based on their unexpectedness (e.g., MMN and P3a). Here, we examine the widely neglected characteristics of deviants being bearers of predictive information themselves; naive participants listened to sound sequences constructed according to a new, modified version of the oddball paradigm including two types of deviants that followed diametrically opposed rules: one deviant sound occurred mostly in pairs (repetition rule), the other deviant sound occurred mostly in isolation (non-repetition rule). Due to this manipulation, the sound following a first deviant (either the same deviant or a standard) was either predictable or unpredictable based on its conditional probability associated with the preceding deviant sound. Our behavioral results from an active deviant detection task replicate previous findings that deviant repetition rules (based on conditional probability) can be extracted when behaviorally relevant. Our electrophysiological findings obtained in a passive listening setting indicate that conditional probability also translates into differential processing at the P3a level. However, MMN was confined to global deviants and was not sensitive to conditional probability. This suggests that higher-level processing concerned with stimulus selection and/or evaluation (reflected in P3a) but not lower-level sensory processing (reflected in MMN) considers rarely encountered rules.

Development of Biomarkers Potentially Sensitive to Early Psychosis Using Mismatch Negativity (MMN) to Complex Pattern Deviations

Infrequent stimulus deviations from repetitive sequences elicit mismatch negativity (MMN) even passively, making MMN practical for clinical applications. Auditory MMN is typically elicited by a change in one (or more) physical stimulus parameters (eg, pitch, duration). This lower-order simple MMN (sMMN) is impaired in long-term schizophrenia. However, sMMN contains activity from release from stimulus adaptation, clouding its face validity as purely deviance-related. More importantly, it is unreliably reduced in samples of first-episode psychosis, limiting its utility as a biomarker. Complex pattern-deviant MMN (cMMN) tasks, which elicit early and late responses, are based on higher-order abstractions and better isolate deviance detection. Their abstract nature may increase the sensitivity to processing deficits in early psychosis. However, both the early and late cMMNs are small, limiting separation between healthy and psychotic samples. In 29 healthy individuals, we tested a new dual-rule cMMN paradigm to assess additivity of deviance. Sounds alternated lateralization between left and right, and low and high pitches, creating a left-low, right-high alternating pattern. Deviants were a repeated left-low, violating lateralization and pitch patterns. Early and late cMMNs on the dual-rule task were significantly larger than those on the one-rule extra tone cMMN task (P < .05). Further, the dual-rule early cMMN was not significantly smaller than pitch or duration sMMNs (P > .48, .28, respectively). These results demonstrate additivity for cMMN pattern-violating rules. This increase in cMMN amplitude should increase group difference effect size, making it a prime candidate for a biomarker of disease presence at first psychotic episode, and perhaps even prior to the emergence of psychosis.

Time-resolved dynamic computational modeling of human EEG recordings reveals gradients of generative mechanisms for the MMN response

Despite attempts to unify the different theoretical accounts of the mismatch negativity (MMN), there is still an ongoing debate on the neurophysiological mechanisms underlying this complex brain response. On one hand, neuronal adaptation to recurrent stimuli is able to explain many of the observed properties of the MMN, such as its sensitivity to controlled experimental parameters. On the other hand, several modeling studies reported evidence in favor of Bayesian learning models for explaining the trial-to-trial dynamics of the human MMN. However, direct comparisons of these two main hypotheses are scarce, and previous modeling studies suffered from methodological limitations. Based on reports indicating spatial and temporal dissociation of physiological mechanisms within the timecourse of mismatch responses in animals, we hypothesized that different computational models would best fit different temporal phases of the human MMN. Using electroencephalographic data from two independent studies of a simple auditory oddball task (n = 82), we compared adaptation and Bayesian learning models' ability to explain the sequential dynamics of auditory deviance detection in a time-resolved fashion. We first ran simulations to evaluate the capacity of our design to dissociate the tested models and found that they were sufficiently distinguishable above a certain level of signal-to-noise ratio (SNR). In subjects with a sufficient SNR, our time-resolved approach revealed a temporal dissociation between the two model families, with high evidence for adaptation during the early MMN window (from 90 to 150-190 ms post-stimulus depending on the dataset) and for Bayesian learning later in time (170-180 ms or 200-220ms). In addition, Bayesian model averaging of fixed-parameter models within the adaptation family revealed a gradient of adaptation rates, resembling the anatomical gradient in the auditory cortical hierarchy reported in animal studies.

Computational Modeling of Oddball Sequence Processing Exposes Common and Differential Auditory Network Changes in First-Episode Schizophrenia-Spectrum Disorders and Schizophrenia

BACKGROUND AND HYPOTHESIS

Differences in sound relevance filtering in schizophrenia are proposed to represent a key index of biological changes in brain function in the illness. This study featured a computational modeling approach to test the hypothesis that processing differences might already be evident in first-episode, becoming more pronounced in the established illness.

STUDY DESIGN

Auditory event-related potentials to a typical oddball sequence (rare pitch deviations amongst regular sounds) were recorded from 90 persons with schizophrenia-spectrum disorders (40 first-episode schizophrenia-spectrum, 50 established illness) and age-matched healthy controls. The data were analyzed using dynamic causal modeling to identify the changes in effective connectivity that best explained group differences.

STUDY RESULTS

Group differences were linked to intrinsic (within brain region) connectivity changes. In activity-dependent measures these were restricted to the left auditory cortex in first-episode schizophrenia-spectrum but were more widespread in the established illness. Modeling suggested that both established illness and first-episode schizophrenia-spectrum groups expressed significantly lower inhibition of inhibitory interneuron activity and altered gain on superficial pyramidal cells with the data indicative of differences in both putative N-methyl-d-aspartate glutamate receptor activity-dependent plasticity and classic neuromodulation.

CONCLUSIONS

The study provides further support for the notion that examining the ability to alter responsiveness to structured sound sequences in schizophrenia and first-episode schizophrenia-spectrum could be informative to uncovering the nature and progression of changes in brain function during the illness. Furthermore, modeling suggested that limited differences present at first-episode schizophrenia-spectrum may become more expansive with illness progression.

Linking the neural basis of distributional statistical learning with transitional statistical learning: The paradox of attention

Statistical learning, the process of tracking distributional information and discovering embedded patterns, is traditionally regarded as a form of implicit learning. However, recent studies proposed that both implicit (attention-independent) and explicit (attention-dependent) learning systems are involved in statistical learning. To understand the role of attention in statistical learning, the current study investigates the cortical processing of distributional patterns in speech across local and global contexts. We then ask how these cortical responses relate to statistical learning behavior in a word segmentation task. We found Event-Related Potential (ERP) evidence of pre-attentive processing of both the local (mismatching negativity) and global distributional information (late discriminative negativity). However, as speech elements became less frequent and more surprising, some participants showed an involuntary attentional shift, reflected in a P3a response. Individuals who displayed attentive neural tracking of distributional information showed faster learning in a speech statistical learning task. These results suggest that an involuntary attentional shift might play a facilitatory, but not essential, role in statistical learning.

Dynamics of Oddball Sound Processing: Trial-by-Trial Modeling of ECoG Signals

Recent computational models of perception conceptualize auditory oddball responses as signatures of a (Bayesian) learning process, in line with the influential view of the mismatch negativity (MMN) as a prediction error signal. Novel MMN experimental paradigms have put an emphasis on neurophysiological effects of manipulating regularity and predictability in sound sequences. This raises the question of the contextual adaptation of the learning process itself, which on the computational side speaks to the mechanisms of gain-modulated (or precision-weighted) prediction error. In this study using electrocorticographic (ECoG) signals, we manipulated the predictability of oddball sound sequences with two objectives: (i) Uncovering the computational process underlying trial-by-trial variations of the cortical responses. The fluctuations between trials, generally ignored by approaches based on averaged evoked responses, should reflect the learning involved. We used a general linear model (GLM) and Bayesian Model Reduction (BMR) to assess the respective contributions of experimental manipulations and learning mechanisms under probabilistic assumptions. (ii) To validate and expand on previous findings regarding the effect of changes in predictability using simultaneous EEG-MEG recordings. Our trial-by-trial analysis revealed only a few stimulus-responsive sensors but the measured effects appear to be consistent over subjects in both time and space. In time, they occur at the typical latency of the MMN (between 100 and 250 ms post-stimulus). In space, we found a dissociation between time-independent effects in more anterior temporal locations and time-dependent (learning) effects in more posterior locations. However, we could not observe any clear and reliable effect of our manipulation of predictability modulation onto the above learning process. Overall, these findings clearly demonstrate the potential of trial-to-trial modeling to unravel perceptual learning processes and their neurophysiological counterparts.

Predictability-Based Source Segregation and Sensory Deviance Detection in Auditory Aging

When multiple sound sources are present at the same time, auditory perception is often challenged with disentangling the resulting mixture and focusing attention on the target source. It has been repeatedly demonstrated that background (distractor) sound sources are easier to ignore when their spectrotemporal signature is predictable. Prior evidence suggests that this ability to exploit predictability for foreground-background segregation degrades with age. On a theoretical level, this has been related with an impairment in elderly adults’ capabilities to detect certain types of sensory deviance in unattended sound sequences. Yet the link between those two capacities, deviance detection and predictability-based sound source segregation, has not been empirically demonstrated. Here we report on a combined behavioral-EEG study investigating the ability of elderly listeners (60–75 years of age) to use predictability as a cue for sound source segregation, as well as their sensory deviance detection capacities. Listeners performed a detection task on a target stream that can only be solved when a concurrent distractor stream is successfully ignored. We contrast two conditions whose distractor streams differ in their predictability. The ability to benefit from predictability was operationalized as performance difference between the two conditions. Results show that elderly listeners can use predictability for sound source segregation at group level, yet with a high degree of inter-individual variation in this ability. In a further, passive-listening control condition, we measured correlates of deviance detection in the event-related brain potential (ERP) elicited by occasional deviations from the same spectrotemporal pattern as used for the predictable distractor sequence during the behavioral task. ERP results confirmed neural signatures of deviance detection in terms of mismatch negativity (MMN) at group level. Correlation analyses at single-subject level provide no evidence for the hypothesis that deviance detection ability (measured by MMN amplitude) is related to the ability to benefit from predictability for sound source segregation. These results are discussed in the frameworks of sensory deviance detection and predictive coding.

Shorter Contextual Timescale Rather Than Memory Deficit in Aging

Many aspects of cognitive ability and brain function that change as we age look like deficits on account of measurable differences in comparison to younger adult groups. One such difference occurs in auditory sensory responses that index perceptual learning. Meta-analytic findings show reliable age-related differences in auditory responses to repetitive patterns of sound and to rare violations of those patterns, variously attributed to deficits in auditory sensory memory and inhibition. Here, we determine whether proposed deficits would render older adults less prone to primacy effects, robustly observed in young adults, which present as a tendency for first learning to have a disproportionate influence over later perceptual inference. The results confirm this reduced sensitivity to primacy effects but do not support impairment in auditory sensory memory as the origin of this difference. Instead, the aging brain produces data consistent with shorter timescales of contextual reference. In conclusion, age-related differences observed previously for perceptual inference appear highly context-specific necessitating reconsideration of whether and to what function the notion of deficit should be attributed, and even whether the notion of deficit is appropriate at all.

The influence of variability on mismatch negativity amplitude

Mismatch Negativity (MMN) to pattern deviations reveals exquisite pattern detection ability in the brain. MMN amplitude is proposed to be precision-weighted, being inversely proportional to variability within a patterned sound sequence. Two experiments were conducted to determine whether pattern variability, shown to influence MMN to simple pattern deviance, also extends to MMN elicited to abstract pattern deviants. Participants were presented with 3-tone triplet sequences that were defined by regular frequency ascendance with adjacent (A<B<C) or non-adjacent (A<C) dependency. The triplets were defined by an abstract pattern in that the starting frequency of A roamed randomly between 500-3700 Hz. Using variants of these sequences over two studies the results show that MMN was elicited to rare A > C deviants for adjacent and non-adjacent dependencies, was smaller for the latter, was impervious to variance in tone loudness, but showed prolonged sensitivity to the level of variability at sequence onset.

Change detection of auditory tonal patterns defined by absolute versus relative pitch information. A combined behavioural and EEG study

The human auditory system often relies on relative pitch information to extract and identify auditory objects; such as when the same melody is played in different keys. The current study investigated the mental chronometry underlying the active discrimination of unfamiliar melodic six-tone patterns by measuring behavioural performance and event-related potentials (ERPs). In a roving standard paradigm, such patterns were either repeated identically within a stimulus train, carrying absolute frequency information about the pattern, or shifted in pitch (transposed) between repetitions, so only relative pitch information was available to extract the pattern identity. Results showed that participants were able to use relative pitch to detect when a new melodic pattern occurred. Though in the absence of absolute pitch sensitivity significantly decreased and behavioural reaction time to pattern changes increased. Mismatch-Negativity (MMN), an ERP indicator of auditory deviance detection, was elicited at approximately 206 ms after stimulus onset at frontocentral electrodes, even when only relative pitch was available to inform pattern discrimination. A P3a was elicited in both conditions, comparable in amplitude and latency. Increased latencies but no differences in amplitudes of N2b, and P3b suggest that processing at higher levels is affected when, in the absence of absolute pitch cues, relative pitch has to be extracted to inform pattern discrimination. Interestingly, the response delay of approximately 70 ms on the behavioural level, already fully manifests at the level of N2b. This is in accordance with recent findings on implicit auditory learning processes and suggests that in the absence of absolute pitch cues a slowing of target selection rather than a slowing of the auditory pattern change detection process causes the deterioration in behavioural performance.

Do rat auditory event related potentials exhibit human mismatch negativity attributes related to predictive coding?

Rodent models play a significant role in understanding disease mechanisms and the screening of new treatments. With regard to psychiatric disorders such as schizophrenia, however, it is difficult to replicate the human symptoms in rodents because these symptoms are often either 'uniquely human' or are only conveyed via self-report. There is a growing interest in rodent mismatch responses (MMRs) as a translatable 'biomarker' for disorders such as schizophrenia. In this review, we will summarize the attributes of human MMN, and discuss the scope of exploring the attributes of human MMN in rodents. Here, we examine how reliably MMRs that are measured in rats mimic human attributes, and present original data examining whether manipulations of stimulus conditions known to modulate human MMN, do the same for rat MMRs. Using surgically-implanted epidural electroencephalographic electrodes and wireless telemetry in freely-moving rats, we observed human-like modulations of MMRs, namely that larger MMRs were elicited to unexpected (deviant) stimuli that a) had a larger change in pitch compared to the expected (standard) stimulus, b) were less frequently presented (lower probability), and c) had no jitter (stable stimulus onset asynchrony) compared to high jitter. Overall, these findings contribute to the mounting evidence for rat MMRs as a good analogue of human MMN, bolstering the development of a novel approach in future to validate the preclinical models based on a translatable biomarker, MMN.

Making Sense of Mismatch Negativity

Evoked potentials provide valuable insight into brain processes that are integral to our ability to interact effectively and efficiently in the world. The mismatch negativity (MMN) component of the evoked potential has proven highly informative on the ways in which sensitivity to regularity contributes to perception and cognition. This review offers a compendium of research on MMN with a view to scaffolding an appreciation for its use as a tool to explore the way regularities contribute to predictions about the sensory environment over many timescales. In compiling this work, interest in MMN as an index of sensory encoding and memory are addressed, as well as attention. Perspectives on the possible underlying computational processes are reviewed as well as recent observations that invite consideration of how MMN relates to how we learn, what we learn, and why.

Hierarchical modulation of auditory prediction error signaling is independent of attention

The auditory system is tuned to detect rhythmic regularities in the environment which can occur on different timescales. Event-related potentials such as mismatch negativity (MMN) and P3b are thought to index local and global deviance, respectively. However, it is not clear how these hierarchical levels interact and to what extent attention modulates this interaction. In this EEG study with 17 healthy young adults, we used a hierarchical oddball paradigm with local (sequence-level) and global (block-level) violations in attended and unattended conditions. Amplitude of N2 and P3b were analyzed in a 2*2*2 factorial model (local status, global status, attention condition). We found a significant interaction between the local and global status on the N2 amplitude, while there was no significant three-way interaction with attention, together demonstrating that lower-level prediction error is modulated by detection of higher-order regularity but expressed independently of attention. By contrast, higher-level prediction error, indexed by P3b, was sensitive to global regularity violations if the auditory stream was attended. The results demonstrate the capacity of our auditory perception to preattentively resolve conflicts between different levels of predictive hierarchy even across longer time intervals as indexed by MMN modulation, while P3b represents a different, attention-dependent system.