A generic deviance detection principle for cortical on/off responses, omission response, and mismatch negativity

Neural responses to sudden changes can be observed in many parts of the sensory pathways at different organizational levels. For example, deviants that violate regularity at various levels of abstraction can be observed as simple On/Off responses of individual neurons or as cumulative responses of neural populations. The cortical deviance-related responses supporting different functionalities (e.g., gap detection, chunking, etc.) seem unlikely to arise from different function-specific neural circuits, given the relatively uniform and self-similar wiring patterns across cortical areas and spatial scales. Additionally, reciprocal wiring patterns (with heterogeneous combinations of excitatory and inhibitory connections) in the cortex naturally speak in favor of a generic deviance detection principle. Based on this concept, we propose a network model consisting of reciprocally coupled neural masses as a blueprint of a universal change detector. Simulation examples reproduce properties of cortical deviance-related responses including the On/Off responses, the omitted-stimulus response (OSR), and the mismatch negativity (MMN). We propose that the emergence of change detectors relies on the involvement of disinhibition. An analysis of network connection settings further suggests a supportive effect of synaptic adaptation and a destructive effect of N-methyl-D-aspartate receptor (NMDA-r) antagonists on change detection. We conclude that the nature of cortical reciprocal wiring gives rise to a whole range of local change detectors supporting the notion of a generic deviance detection principle. Several testable predictions are provided based on the network model. Notably, we predict that the NMDA-r antagonists would generally dampen the cortical Off response, the cortical OSR, and the MMN

Neural substrates and models of omission responses and predictive processes

Predictive coding theories argue that deviance detection phenomena, such as mismatch responses and omission responses, are generated by predictive processes with possibly overlapping neural substrates. Molecular imaging and electrophysiology studies of mismatch responses and corollary discharge in the rodent model allowed the development of mechanistic and computational models of these phenomena. These models enable translation between human and non-human animal research and help to uncover fundamental features of change-processing microcircuitry in the neocortex. This microcircuitry is characterized by stimulus-specific adaptation and feedforward inhibition of stimulus-selective populations of pyramidal neurons and interneurons, with specific contributions from different interneuron types. The overlap of the substrates of different types of responses to deviant stimuli remains to be understood. Omission responses, which are observed both in corollary discharge and mismatch response protocols in humans, are underutilized in animal research and may be pivotal in uncovering the substrates of predictive processes. Omission studies comprise a range of methods centered on the withholding of an expected stimulus. This review aims to provide an overview of omission protocols and showcase their potential to integrate and complement the different models and procedures employed to study prediction and deviance detection.This approach may reveal the biological foundations of core concepts of predictive coding, and allow an empirical test of the framework’s promise to unify theoretical models of attention and perception

Brain network dynamics in deviance response and auditory perception

Neuronale Reaktionen auf plötzliche Veränderungen des sensorischen Inputs können in vielen Teilen der sensorischen Bahnen auf verschiedenen Organisationsebenen beobachtet werden. So können beispielsweise Abweichungen, die die Regelmäßigkeit auf verschiedenen Abstraktionsebenen verletzen, als einfache Ein-/Aus-Reaktionen einzelner Neuronen oder als kumulative Reaktionen neuronaler Populationen beobachtet werden. Aufgrund des relativ einheitlichen und selbstähnlichen Verdrahtungsmuster im Kortex scheint es unwahrscheinlich, dass die verschiedenen kortikalen Reaktionen, die unterschiedliche Funktionalitäten unterstützen (z.B. Lückenerkennung, Chunking, etc.), jeweils auf spezialisierten kortikalen Verschaltungsmustern beruhen. Darüber hinaus sprechen reziproke Verdrahtungsmuster (mit heterogenen Kombinationen von exzitatorischen und inhibitorischen Verbindungen) im Kortex für ein generisches Prinzip zur Erkennung von Abweichungen. Das vorgeschlagene generische Prinzip der Abweichungserkennung unterteilt die Erzeugung der Abweichungsreaktion in zwei Funktionsschritte: Regularitätsbildung und Änderungserkennung. Das Prinzip legt nahe, dass die im Kortex beobachteten Reaktionen, wie die kortikalen Ein/Aus-Antworten, die kortikale Auslassungsreaktion (OSR) und die Mismatch-Negativität (MMN), als Änderungsreaktionen auf verschiedenen Abstraktionsebenen betrachtet werden können. Das Netzwerkmodell, das auf diesem Prinzip basiert, reproduziert mehrere experimentell beobachtete Befunde, zu denen die unterschiedlichen zeitlichen Profile der Ein/Aus-Antworten, die lineare Beziehung zwischen OSR-Latenz und Input Stimulus Onset Asynchrony (SOA) und die langsamen und schnellen Reaktionen im Sequenz-MMN-Paradigma gehören. In Bezug auf die Erkennung von Veränderungen deuten die Simulationsergebnisse darauf hin, dass für das Auftreten von Veränderungsdetektoren ein Disinhibitionsmechanismus erforderlich ist. Eine Analyse der Verbindungsstärken im Netzwerk deutet weiterhin auf einen unterstützenden Effekt der synaptischen Anpassung und einen destruktiven Effekt von N-Methyl-D-Aspartat-Rezeptor- (NMDA-r)-Antagonisten auf die Änderungserkennung hin. In Bezug auf die Regularitätsbildung deuten die Simulationsergebnisse auf den Notwendigkeit für ein raumcodierenden Schema, eine größere Zeitkonstante der hemmenden Population und kurzfristige Plastizität hin, um eine stetige neuronale Repräsentation der Regularität zu unterstützen. Für die experimentelle Validierung können wir mehrere Vorhersagen aus dem Modell ableiten. Erstens sollten die verschiedenen kortikalen Abweichungsreaktionen ähnliche laminare Profile aufweisen, insbesondere bzgl. der Aktivität der inhibitorischen Neuronen, in denen die Änderungserkennung stattfindet. Zweitens würden die NMDA-r-Antagonisten im Allgemeinen die kortikale Aus-Reaktion, die kortikale OSR und die MMN dämpfen. Drittens könnte es unterschiedliche räumliche Verteilungen der Änderungserkennung und Regularitätsbildung geben, da die beiden Funktionen aus unterschiedlichen Netzwerkeigenschaften wie Zeitkonstanten und Verbindungsmustern entstehen. Diese Arbeit bietet einen neuen Blickwinkel auf die neuronalen Mechanismen, die der Detektion von Abweichungen zugrunde liegen. Zukünftige Forschungsthemen, wie der Aufmerksamkeitsmechanismus in der Wahrnehmung, die funktionelle Rolle verschiedener Arten von hemmenden Neuronen sowie höhere kognitive Funktionen wie Spracherwerb und -verständnis, können auf der aktuellen Implementierung des Modells basieren.Neural responses to sudden changes can be observed in many parts of the sensory pathways at different organizational levels. For example, deviants that violate regularity at various levels of abstraction can be observed as simple On/Off responses of individual neurons or as cumulative responses of neural populations. The cortical deviance-related responses supporting different functionalities (e.g., gap detection, chunking, etc.) seem unlikely to arise from different function-specific neural circuits, given the relatively uniform and self-similar wiring patterns across cortical areas and spatial scales. Additionally, reciprocal wiring patterns (with heterogeneous combinations of excitatory and inhibitory connections) in the cortex naturally speak in favor of a generic deviance detection principle. The proposed generic deviance detection principle separates the generation of deviance response into two functional stages: regularity formation and change detection. The principle suggests that the deviance-related responses observed in the cortex, such as the cortical On/Off responses, the cortical omitted-stimulus response (OSR), and the mismatch negativity (MMN), can be regarded as change responses at different levels of abstraction. The network model based on the principle reproduce several experimentally observed properties of cortical deviance-related responses, which include the diverse temporal profiles of On/Off responses, the linear relationship between OSR latency and input stimulus onset asynchrony (SOA), and the slow and fast responses in the sequence MMN paradigm. Regarding change detection, the simulation results suggest that the emergence of change detectors relies on the involvement of disinhibition. An analysis of network connection settings further suggests a supportive effect of synaptic adaptation and a destructive effect of N-methyl-D-aspartate receptor (NMDA-r) antagonists on change detection. Regarding regularity formation, the simulation results suggest the need for a place coding scheme, a larger time constant of inhibitory population, and short-term plasticity to support a steady neural representation of regularity. Several model predictions are provided for experimental validation. First, the cortical deviance-related responses might show similar laminar profiles, especially the activity of the inhibitory neurons where the change detection takes place. Second, the NMDA-r antagonists would generally dampen the cortical Off response, the cortical OSR, and the MMN. Third, there might be distinct geometric distributions of change detection and regularity formation, since the two functions emerge from different network properties such as the time constants and connection patterns. This thesis provides a new viewpoint on the neural mechanisms underlying the generation of deviance response. Future research topics, such as the attention mechanism in perception, the functional roles of various types of inhibitory neurons, and the process of higher cognitive functions such as language acquisition and comprehension, can be based on the current implementation of simulations

Omission responses in local field potentials in rat auditory cortex

Background Non-invasive recordings of gross neural activity in humans often show responses to omitted stimuli in steady trains of identical stimuli. This has been taken as evidence for the neural coding of prediction or prediction error. However, evidence for such omission responses from invasive recordings of cellular-scale responses in animal models is scarce. Here, we sought to characterise omission responses using extracellular recordings in the auditory cortex of anaesthetised rats. We profiled omission responses across local field potentials (LFP), analogue multiunit activity (AMUA), and single/multi-unit spiking activity, using stimuli that were fixed-rate trains of acoustic noise bursts where 5% of bursts were randomly omitted. Results Significant omission responses were observed in LFP and AMUA signals, but not in spiking activity. These omission responses had a lower amplitude and longer latency than burst-evoked sensory responses, and omission response amplitude increased as a function of the number of preceding bursts. Conclusions Together, our findings show that omission responses are most robustly observed in LFP and AMUA signals (relative to spiking activity). This has implications for models of cortical processing that require many neurons to encode prediction errors in their spike output

Mismatch responses: Probing probabilistic inference in the brain

Sensory signals are governed by statistical regularities and carry valuable information about the unfolding of environmental events. The brain is thought to capitalize on the probabilistic nature of sequential inputs to infer on the underlying (hidden) dynamics driving sensory stimulation. Mis-match responses (MMRs) such as the mismatch negativity (MMN) and the P3 constitute prominent neuronal signatures which are increasingly interpreted as reflecting a mismatch between the current sensory input and the brain’s generative model of incoming stimuli. As such, MMRs might be viewed as signatures of probabilistic inference in the brain and their response dynamics can provide insights into the underlying computational principles. However, given the dominance of the auditory modality in MMR research, the specifics of brain responses to probabilistic sequences across sensory modalities and especially in the somatosensory domain are not well characterized. The work presented here investigates MMRs across the auditory, visual and somatosensory modality by means of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). We designed probabilistic stimulus sequences to elicit and characterize MMRs and employed computational modeling of response dynamics to inspect different aspects of the brain’s generative model of the sensory environment. In the first study, we used a volatile roving stimulus paradigm to elicit somatosensory MMRs and performed single-trial modeling of EEG signals in sensor and source space. Model comparison suggested that responses reflect Bayesian inference based on the estimation of transition probability and limited information integration of the recent past in order to adapt to a changing environment. The results indicated that somatosensory MMRs reflect an initial mismatch between sensory input and model beliefs represented by confidence-corrected surprise (CS) followed by model adjustment dynamics represented by Bayesian surprise (BS). For the second and third study we designed a tri-modal roving stimulus paradigm to delineate modality specific and modality general features of mismatch processing. Computational modeling of EEG signals in study 2 suggested that single-trial dynamics reflect Bayesian inference based on estimation of uni-modal transition probabilities as well as cross-modal conditional dependencies. While early mismatch processing around the MMN tended to reflect CS, later MMRs around the P3 rather reflect BS, in correspondence to the somatosensory study. Finally, the fMRI results of study 3 showed that MMRs are generated by an interaction of modality specific regions in higher order sensory cortices and a modality general fronto-parietal network. Inferior parietal regions in particular were sensitive to expectation violations with respect to the cross-modal contingencies in the stimulus sequences. Overall, our results indicate that MMRs across the senses reflect processes of probabilistic inference in a complex and inherently multi-modal environment.Sensorische Signale sind durch statistische Regularitäten bestimmt und beinhalten wertvolle Informationen über die Entwicklung von Umweltereignissen. Es wird angenommen, dass das Gehirn die Wahrscheinlichkeitseigenschaften sequenzieller Reize nutzt um auf die zugrundeliegenden (verborgenen) Dynamiken zu schließen, welche sensorische Stimulation verursachen. Diskrepanz-Reaktionen ("Mismatch responses"; MMRs) wie die "mismatch negativity" (MMN) und die P3 sind bekannte neuronale Signaturen die vermehrt als Signale einer Diskrepanz zwischen der momentanen sensorischen Einspeisung und dem generativen Modell, welches das Gehirn von den eingehenden Reizen erstellt angesehen werden. Als solche können MMRs als Signaturen von wahrscheinlichkeitsbasierter Inferenz im Gehirn betrachtet werden und ihre Reaktionsdynamiken können Einblicke in die zugrundeliegenden komputationalen Prinzipien geben. Angesichts der Dominanz der auditorischen Modalität in der MMR-Forschung, sind allerdings die spezifischen Eigenschaften von Hirn-Reaktionen auf Wahrscheinlichkeitssequenzen über sensorische Modalitäten hinweg und vor allem in der somatosensorischen Modalität nicht gut charakterisiert. Die hier vorgestellte Arbeit untersucht MMRs über die auditorische, visuelle und somatosensorische Modalität hinweg anhand von Elektroenzephalographie (EEG) und funktioneller Magnetresonanztomographie (fMRT). Wir gestalteten wahrscheinlichkeitsbasierte Reizsequenzen, um MMRs auszulösen und zu charakterisieren und verwendeten komputationale Modellierung der Reaktionsdynamiken, um verschiedene Aspekte des generativen Modells des Gehirns von der sensorischen Umwelt zu untersuchen. In der ersten Studie verwendeten wir ein volatiles "Roving-Stimulus"-Paradigma, um somatosensorische MMRs auszulösen und modellierten die Einzel-Proben der EEG-Signale im sensorischen und Quell-Raum. Modellvergleiche legten nahe, dass die Reaktionen Bayes’sche Inferenz abbilden, basierend auf der Schätzung von Transitionswahrscheinlichkeiten und limitierter Integration von Information der jüngsten Vergangenheit, welche eine Anpassung an Umweltänderungen ermöglicht. Die Ergebnisse legen nahe, dass somatosen-sorische MMRs eine initiale Diskrepanz zwischen sensorischer Einspeisung und Modellüberzeugung reflektieren welche durch "confidence-corrected surprise" (CS) repräsentiert ist, gefolgt von Modelanpassungsdynamiken repräsentiert von "Bayesian surprise" (BS). Für die zweite und dritte Studie haben wir ein Tri-Modales "Roving-Stimulus"-Paradigma gestaltet, um modalitätsspezifische und modalitätsübergreifende Eigenschaften von Diskrepanzprozessierung zu umreißen. Komputationale Modellierung von EEG-Signalen in Studie 2 legte nahe, dass Einzel-Proben Dynamiken Bayes’sche Inferenz abbilden, basierend auf der Schätzung von unimodalen Transitionswahrscheinlichkeiten sowie modalitätsübergreifenden bedingten Abhängigkeiten. Während frühe Diskrepanzprozessierung um die MMN dazu tendierten CS zu reflektieren, so reflektierten spätere MMRs um die P3 eher BS, in Übereinstimmung mit der somatosensorischen Studie. Abschließend zeigten die fMRT-Ergebnisse der Studie 3 dass MMRs durch eine Interaktion von modalitätsspezifischen Regionen in sensorischen Kortizes höherer Ordnung mit einem modalitätsübergreifenden fronto-parietalen Netzwerk generiert werden. Inferior parietale Regionen im Speziellen waren sensitiv gegenüber Erwartungsverstoß in Bezug auf die modalitätsübergreifenden Wahrscheinlichkeiten in den Reizsequenzen. Insgesamt weisen unsere Ergebnisse darauf hin, dass MMRs über die Sinne hinweg Prozesse von wahrscheinlichkeitsbasierter Inferenz in einer komplexen und inhärent multi-modalen Umwelt darstellen

Prediction related phenomena of visual perception

Perception is grounded in our ability to optimize predictions about upcoming events. Such predictions depend on both the incoming sensory input and on our previously acquired conceptual knowledge. Correctly predicted or expected sensory stimuli induce reduced responses when compared to incorrectly predicted, surprising inputs. Predictions enable an efficient neuronal encoding so that less energy is invested to interpret redundant sensory stimuli. Several different neuronal phenomena are the consequences of predictions, such as repetition suppression (RS) and mismatch negativity (MMN). RS represents the reduced neuronal response to a stimulus upon its repeated presentation. MMN is the electrophysiological response difference between rare and frequent stimuli in an oddball sequence. While both are currently studied extensively, the underlying mechanisms of RS and MMN as well as their relation to predictions remains poorly understood. In the current thesis, four experiments were devised to investigate prediction related phenomena dependent on the repetition probability of stimuli. Two studies deal with the RS phenomenon, while the other two investigate the MMN response. In Experiment 1 the temporal dynamics underlying prediction and RS effects were tested. Participants were presented with expected and surprising stimulus pairs with two different inter-stimulus intervals (0.5s for Immediate and 1.75 or 3.75s for Delayed target presentation). These pairs could either repeat or alternate. Expectations were contingent on face gender and were manipulated with the repetition probability. We found that the prediction effects do not depend on the length of the ISI period, suggesting that Immediate and Delayed cue-target stimulus arrangements create similar expectation effects. In order to elucidate the neuronal mechanisms underlying these prediction effects (i.e. surprise enhancement or expectation suppression), in our second study, we employed the experimental design of the first experiment with the addition of random events as a control. We found that surprising events elicit stronger Blood Oxygen Level Dependent (BOLD) responses than random events, implying that predictions influence the neuronal responses via surprise enhancement. Similarly, the third experiment was employed to disentangle which neural mechanism underlies the visual MMN (vMMN). We compared the responses to the stimuli (chairs, faces, real and false characters) presented in conventional oddball sequences to the same stimuli in control sequences (Kaliukhovich and Vogels, 2014). We found that the neural mechanisms underlying vMMN are category dependent: the vMMN of faces and chairs was due to RS, while the vMMN response of real and false characters was mainly driven by surprise-related changes. So far, no study used category-specific regions of interest (ROIs) to examine the neuroimaging correlates of the vMMN. Therefore, for the fourth experiment, we recorded electrophysiological and neuroimaging data from the same participants with an oddball paradigm for real and false characters. We found a significant correlation between vMMN (CP1 cluster at 400 ms) and functional magnetic resonance imaging adaptation (in the letter form area for real characters), suggesting their strong relationship. Taking the four studies into consideration, it is clear that surprise has an important role in prediction related phenomena. The role of surprise is discussed in the light of these results and other recent developments reported in the literature. Overall, this thesis suggests the unification of RS and MMN within the framework of predictive coding

Modeling biophysical and neural circuit bases for core cognitive abilities evident in neuroimaging patterns: hippocampal mismatch, mismatch negativity, repetition positivity, and alpha suppression of distractors

This dissertation develops computational models to address outstanding problems in the domain of expectation-related cognitive processes and their neuroimaging markers in functional MRI or EEG. The new models reveal a way to unite diverse phenomena within a common framework focused on dynamic neural encoding shifts, which can arise from robust interactive effects of M-currents and chloride currents in pyramidal neurons. By specifying efficient, biologically realistic circuits that achieve predictive coding (e.g., Friston, 2005), these models bridge among neuronal biophysics, systems neuroscience, and theories of cognition. Chapter one surveys data types and neural processes to be examined, and outlines the Dynamically Labeled Predictive Coding (DLPC) framework developed during the research. Chapter two models hippocampal prediction and mismatch, using the DLPC framework. Chapter three presents extensions to the model that allow its application for modeling neocortical EEG genesis. Simulations of this extended model illustrate how dynamic encoding shifts can produce Mismatch Negativity (MMN) phenomena, including pharmacological effects on MMN reported for humans or animals. Chapters four and five describe new modeling studies of possible neural bases for alpha-induced information suppression, a phenomenon associated with active ignoring of stimuli. Two models explore the hypothesis that in simple rate-based circuits, information suppression might be a robust effect of neural saturation states arising near peaks of resonant alpha oscillations. A new proposal is also introduced for how the basal ganglia may control onset and offset of alpha-induced information suppression. Although these rate models could reproduce many experimental findings, they fell short of reproducing a key electrophysiological finding: phase-dependent reduction in spiking activity correlated with power in the alpha frequency band. Therefore, chapter five also specifies how a DLPC model, adapted from the neocortical model developed in chapter three, can provide an expectation-based model of alpha-induced information suppression that exhibits phase-dependent spike reduction during alpha-band oscillations. The model thus can explain experimental findings that were not reproduced by the rate models. The final chapter summarizes main theses, results, and basic research implications, then suggests future directions, including expanded models of neocortical mismatch, applications to artificial neural networks, and the introduction of reward circuitry

An exploration of pre-attentive visual discrimination using event-related potentials

The Mismatch Negativity (MMN) has been characterised as a ‘pre-attentive’ component of an Event-Related Potential (ERP) that is related to discriminatory processes. Although well established in the auditory domain, characteristics of the MMN are less well characterised in the visual domain. The five main studies presented in this thesis examine visual cortical processing using event-related potentials. Novel methodologies have been used to elicit visual detection and discrimination components in the absence of a behavioural task. Developing paradigms in which a behavioural task is not required may have important clinical applications for populations, such as young children, who cannot comply with the demands of an active task. The ‘pre-attentive’ nature of visual MMN has been investigated by modulating attention. Generators and hemispheric lateralisation of visual MMN have been investigated by using pertinent clinical groups. A three stimulus visual oddball paradigm was used to explore the elicitation of visual discrimination components to a change in the orientation of stimuli in the absence of a behavioural task. Monochrome stimuli based on pacman figures were employed that differed from each other only in terms of the orientation of their elements. One such stimulus formed an illusory figure in order to capture the participant’s attention, either in place of, or alongside, a behavioural task. The elicitation of a P3a to the illusory figure but not to the standard or deviant stimuli provided evidence that the illusory figure captured attention. A visual MMN response was recorded in a paradigm with no task demands. When a behavioural task was incorporated into the paradigm, a P3b component was elicited consistent with the allocation of attentional resources to the task. However, visual discrimination components were attenuated revealing that the illusory figure was unable to command all attentional resources from the standard deviant transition. The results are the first to suggest that the visual MMN is modulated by attention. Using the same three stimulus oddball paradigm, generators of visual MMN were investigated by recording potentials directly from the cortex of an adolescent undergoing pre-surgical evaluation for resection of a right anterior parietal lesion. To date no other study has explicitly recorded activity related to the visual MMN intracranially using an oddball paradigm in the absence of a behavioural task. Results indicated that visual N1 and visual MMN could be temporally and spatially separated, with visual MMN being recorded more anteriorly than N1. The characteristic abnormality in retinal projections in albinism afforded the opportunity to investigate each hemisphere in relative isolation and was used, for the first time, as a model to investigate lateralisation of visual MMN and illusory contour processing. Using the three stimulus oddball paradigm, no visual MMN was elicited in this group, and so no conclusions regarding the lateralisation of visual MMN could be made. Results suggested that both hemispheres were equally capable of processing an illusory figure. As a method of presenting visual test stimuli without conscious perception, a continuous visual stream paradigm was developed that used a briefly presented checkerboard stimulus combined with masking for exploring stimulus detection below and above subjective levels of perception. A correlate of very early cortical processing at a latency of 60-80 ms (CI) was elicited whether stimuli were reported as seen or unseen. Differences in visual processing were only evident at a latency of 90 ms (CII) implying that this component may represent a correlate of visual consciousness/awareness. Finally, an oddball sequence was introduced into the visual stream masking paradigm to investigate whether visual MMN responses could be recorded without conscious perception. The stimuli comprised of black and white checkerboard elements differing only in terms of their orientation to form an x or a +. Visual MMN was not recorded when participants were unable to report seeing the stimulus. Results therefore suggest that behavioural identification of the stimuli was required for the elicitation of visual MMN and that visual MMN may require some attentional resources. On the basis of these studies it is concluded that visual MMN is not entirely independent of attention. Further, the combination of clinical and non-clinical investigations provides a unique opportunity to study the characterisation and localisation of putative mechanisms related to conscious and non-conscious visual processing

A dual role for prediction error in associative learning

Confronted with a rich sensory environment, the brain must learn statistical regularities across sensory domains to construct causal models of the world. Here, we used functional magnetic resonance imaging and dynamic causal modeling (DCM) to furnish neurophysiological evidence that statistical associations are learnt, even when task-irrelevant. Subjects performed an audio-visual target-detection task while being exposed to distractor stimuli. Unknown to them, auditory distractors predicted the presence or absence of subsequent visual distractors. We modeled incidental learning of these associations using a Rescorla--Wagner (RW) model. Activity in primary visual cortex and putamen reflected learning-dependent surprise: these areas responded progressively more to unpredicted, and progressively less to predicted visual stimuli. Critically, this prediction-error response was observed even when the absence of a visual stimulus was surprising. We investigated the underlying mechanism by embedding the RW model into a DCM to show that auditory to visual connectivity changed significantly over time as a function of prediction error. Thus, consistent with predictive coding models of perception, associative learning is mediated by prediction-error dependent changes in connectivity. These results posit a dual role for prediction-error in encoding surprise and driving associative plasticity