Cross-Modality Evidence for Reduced Choice History Biases in Psychosis-Prone Individuals.

OBJECTIVES: Predictive processing posits that perception emerges from inferential processes within a hierarchical cortical system. Alterations of these processes may result in psychotic experiences, such as hallucinations and delusions. Central to the predictive processing account of psychosis is the notion of aberrant weights attributed to prior information and sensory input. Based on the notion that previous perceptual choices represent a relevant source of prior information, we here asked whether the propensity towards psychotic experiences may be related to altered choice history biases in perceptual decision-making. METHODS: We investigated the relationship between choice history biases in perceptual decision-making and psychosis proneness in the general population. Choice history biases and their adaptation to experimentally induced changes in stimulus serial dependencies were investigated in decision-making tasks with auditory (experiment 1) and visual (experiment 2) stimuli. We further explored a potential compensatory mechanism for reduced choice history biases by reliance on predictive cross-modal cues. RESULTS: In line with our preregistered hypothesis, psychosis proneness was associated with decreased choice history biases in both experiments. This association is generalized across conditions with and without stimulus serial dependencies. We did not find consistent evidence for a compensatory reliance on cue information in psychosis-prone individuals across experiments. CONCLUSIONS: Our results show reduced choice history biases in psychosis proneness. A compensatory mechanism between implicit choice history effects and explicit cue information is not supported unequivocally by our data.

The value of confidence: Confidence prediction errors drive value-based learning in the absence of external feedback.

Reinforcement learning algorithms have a long-standing success story in explaining the dynamics of instrumental conditioning in humans and other species. While normative reinforcement learning models are critically dependent on external feedback, recent findings in the field of perceptual learning point to a crucial role of internally generated reinforcement signals based on subjective confidence, when external feedback is not available. Here, we investigated the existence of such confidence-based learning signals in a key domain of reinforcement-based learning: instrumental conditioning. We conducted a value-based decision making experiment which included phases with and without external feedback and in which participants reported their confidence in addition to choices. Behaviorally, we found signatures of self-reinforcement in phases without feedback, reflected in an increase of subjective confidence and choice consistency. To clarify the mechanistic role of confidence in value-based learning, we compared a family of confidence-based learning models with more standard models predicting either no change in value estimates or a devaluation over time when no external reward is provided. We found that confidence-based models indeed outperformed these reference models, whereby the learning signal of the winning model was based on the prediction error between current confidence and a stimulus-unspecific average of previous confidence levels. Interestingly, individuals with more volatile reward-based value updates in the presence of feedback also showed more volatile confidence-based value updates when feedback was not available. Together, our results provide evidence that confidence-based learning signals affect instrumentally learned subjective values in the absence of external feedback.

Reverse engineering of metacognition.

The human ability to introspect on thoughts, perceptions or actions - metacognitive ability - has become a focal topic of both cognitive basic and clinical research. At the same time it has become increasingly clear that currently available quantitative tools are limited in their ability to make unconfounded inferences about metacognition. As a step forward, the present work introduces a comprehensive modeling framework of metacognition that allows for inferences about metacognitive noise and metacognitive biases during the readout of decision values or at the confidence reporting stage. The model assumes that confidence results from a continuous but noisy and potentially biased transformation of decision values, described by a confidence link function. A canonical set of metacognitive noise distributions is introduced which differ, amongst others, in their predictions about metacognitive sign flips of decision values. Successful recovery of model parameters is demonstrated, and the model is validated on an empirical data set. In particular, it is shown that metacognitive noise and bias parameters correlate with conventional behavioral measures. Crucially, in contrast to these conventional measures, metacognitive noise parameters inferred from the model are shown to be independent of performance. This work is accompanied by a toolbox (ReMeta) that allows researchers to estimate key parameters of metacognition in confidence datasets.

Metacognition is a person's ability to think about their own thoughts. For example, imagine you are walking in a dark forest when you see an elongated object. You think it is a stick rather than a snake, but how sure are you? Reflecting on one's certainty about own thoughts or perceptions ­ confidence ­ is a prime example of metacognition. While our ability to think about our own thoughts in this way provides many, perhaps uniquely human, advantages, confidence judgements are prone to biases. Often, humans tend to be overconfident: we think we are right more often than we actually are. Internal noise of neural processes can also affect confidence. Understanding these imperfections in metacognition could shed light on how humans think, but studying this phenomenon is challenging. Current methods are lacking either mechanistic insight about the sources of metacognitive biases and noise or rely on unrealistic assumptions. A better model for how metacognition works could provide a clearer picture. Guggenmos developed a mathematical model and a computer toolbox to help researchers investigate how humans or animals estimate confidence in their own thoughts and resulting decisions . The model splits metacognition apart, allowing scientists to explore biases and sources of noise at different phases in the process. It takes two kinds of data: the decisions study participants make, and how sure they are about their decision being correct. It then recreates metacognition in three phases: the primary decision, the metacognitive readout of the evidence, and the confidence report. This allows investigators to see where and when noise and bias come into play. Guggenmos tested the model using independent data from a visual discrimination task and found that it was able to predict how confident participants reported to be in their decisions. Metacognitive ability can change in people with mental illness. People with schizophrenia have often been found to be overconfident in their decisions, while people with depression can be underconfident. Using this model to separate the various facets of metacognition could help to explain why. It could also shed light on human thinking in general.

Measuring metacognitive performance: type 1 performance dependence and test-retest reliability.

Research on metacognition-thinking about thinking-has grown rapidly and fostered our understanding of human cognition in healthy individuals and clinical populations. Of central importance is the concept of metacognitive performance, which characterizes the capacity of an individual to estimate and report the accuracy of primary (type 1) cognitive processes or actions ensuing from these processes. Arguably one of the biggest challenges for measures of metacognitive performance is their dependency on objective type 1 performance, although more recent methods aim to address this issue. The present work scrutinizes the most popular metacognitive performance measures in terms of two critical characteristics: independence of type 1 performance and test-retest reliability. Analyses of data from the Confidence Database (total N = 6912) indicate that no current metacognitive performance measure is independent of type 1 performance. The shape of this dependency is largely reproduced by extending current models of metacognition with a source of metacognitive noise. Moreover, the reliability of metacognitive performance measures is highly sensitive to the combination of type 1 performance and trial number. Importantly, trial numbers frequently employed in metacognition research are too low to achieve an acceptable level of test-retest reliability. Among common task characteristics, simultaneous choice and confidence reports most strongly improved reliability. Finally, general recommendations about design choices and analytical remedies for studies investigating metacognitive performance are provided.

No evidence for mnemonic modulation of interocularly suppressed visual input.

Visual working memory (VWM) allows for keeping visual information available for upcoming goal-directed behavior, while new visual input is processed concurrently. Interactions between the mnemonic and perceptual systems cause VWM to affect the processing of visual input in a content-specific manner: visual input that is initially suppressed from consciousness is detected faster when it matches rather than mismatches the content of VWM. It is currently under debate whether such mnemonic influences on perception occur prior to or after conscious access. To address this issue, we investigated whether VWM content modulates the neural response to visual input that remains suppressed from consciousness. We measured fMRI responses to interocularly suppressed stimuli in 20 human participants performing a delayed match-to-sample task: Participants were retro-cued to memorize one of two geometrical shapes for subsequent recognition. During retention, an interocularly suppressed peripheral stimulus (the probe) was briefly presented, which was either of the cued (memorized) or uncued (not memorized) shape category. We found no evidence that VWM content modulated the neural response to the probe. Substantial evidence for the absence of this modulation was found despite leveraging a highly liberal analysis approach: (1) selecting regions of interest that were particularly prone to detecting said modulation, and (2) using directional Bayesian tests favoring the presence of the hypothesized modulation. We did observe faster detection of memory-matching compared to memory-mismatching probes in a behavioral control experiment, thus validating the stimulus set. We conclude that VWM impacts the processing of visual input only once suppression is mostly alleviated.

Unreliable feedback deteriorates information processing in primary visual cortex.

It is well-established that increased sensory uncertainty impairs perceptual decision-making and leads to degraded neural stimulus representations. Recently, we also showed that providing unreliable feedback to choices leads to changes in perceptual decision-making similar to those of increased stimulus noise: A deterioration in objective task performance, a decrease in subjective confidence and a lower reliance on sensory information for perceptual inference. To investigate the neural basis of such feedback-based changes in perceptual decision-making, in the present study, two groups of healthy human participants (n = 15 each) performed a challenging visual orientation discrimination task while undergoing functional magnetic resonance imaging (fMRI). Critically, one group received reliable feedback regarding their task performance in an intervention phase, whereas the other group correspondingly received unreliable feedback - thereby keeping stimulus information constant. The effects of feedback reliability on performance and stimulus representation in the primary visual cortex (V1) were studied by comparing the pre- and post-intervention test phases between the groups. Compared to participants who received reliable feedback, those receiving unreliable feedback showed a decline in task performance that was paralleled by reduced distinctness of fMRI response patterns in V1. These results show that environmental uncertainty can affect perceptual inference at the earliest cortical processing stages.

A multimodal neuroimaging classifier for alcohol dependence.

With progress in magnetic resonance imaging technology and a broader dissemination of state-of-the-art imaging facilities, the acquisition of multiple neuroimaging modalities is becoming increasingly feasible. One particular hope associated with multimodal neuroimaging is the development of reliable data-driven diagnostic classifiers for psychiatric disorders, yet previous studies have often failed to find a benefit of combining multiple modalities. As a psychiatric disorder with established neurobiological effects at several levels of description, alcohol dependence is particularly well-suited for multimodal classification. To this aim, we developed a multimodal classification scheme and applied it to a rich neuroimaging battery (structural, functional task-based and functional resting-state data) collected in a matched sample of alcohol-dependent patients (N = 119) and controls (N = 97). We found that our classification scheme yielded 79.3% diagnostic accuracy, which outperformed the strongest individual modality - grey-matter density - by 2.7%. We found that this moderate benefit of multimodal classification depended on a number of critical design choices: a procedure to select optimal modality-specific classifiers, a fine-grained ensemble prediction based on cross-modal weight matrices and continuous classifier decision values. We conclude that the combination of multiple neuroimaging modalities is able to moderately improve the accuracy of machine-learning-based diagnostic classification in alcohol dependence.

Neural Response Patterns During Pavlovian-to-Instrumental Transfer Predict Alcohol Relapse and Young Adult Drinking.

BACKGROUND: Pavlovian-to-instrumental transfer (PIT) describes the influence of conditioned stimuli on instrumental behaviors and is discussed as a key process underlying substance abuse. Here, we tested whether neural responses during alcohol-related PIT predict future relapse in alcohol-dependent patients and future drinking behavior in adolescents. METHODS: Recently detoxified alcohol-dependent patients (n = 52) and young adults without dependence (n = 136) underwent functional magnetic resonance imaging during an alcohol-related PIT paradigm, and their drinking behavior was assessed in a 12-month follow-up. To predict future drinking behavior from PIT activation patterns, we used a multivoxel classification scheme based on linear support vector machines. RESULTS: When training and testing the classification scheme in patients, PIT activation patterns predicted future relapse with 71.2% accuracy. Feature selection revealed that classification was exclusively based on activation patterns in medial prefrontal cortex. To probe the generalizability of this functional magnetic resonance imaging-based prediction of future drinking behavior, we applied the support vector machine classifier that had been trained on patients to PIT functional magnetic resonance imaging data from adolescents. An analysis of cross-classification predictions revealed that those young social drinkers who were classified as abstainers showed a greater reduction in alcohol consumption at 12-month follow-up than those classified as relapsers (Δ = -24.4 ± 6.0 g vs. -5.7 ± 3.6 g; p = .019). CONCLUSIONS: These results suggest that neural responses during PIT could constitute a generalized prognostic marker for future drinking behavior in established alcohol use disorder and in at-risk states.

Multivariate pattern analysis for MEG: A comparison of dissimilarity measures.

Multivariate pattern analysis (MVPA) methods such as decoding and representational similarity analysis (RSA) are growing rapidly in popularity for the analysis of magnetoencephalography (MEG) data. However, little is known about the relative performance and characteristics of the specific dissimilarity measures used to describe differences between evoked activation patterns. Here we used a multisession MEG data set to qualitatively characterize a range of dissimilarity measures and to quantitatively compare them with respect to decoding accuracy (for decoding) and between-session reliability of representational dissimilarity matrices (for RSA). We tested dissimilarity measures from a range of classifiers (Linear Discriminant Analysis - LDA, Support Vector Machine - SVM, Weighted Robust Distance - WeiRD, Gaussian Naïve Bayes - GNB) and distances (Euclidean distance, Pearson correlation). In addition, we evaluated three key processing choices: 1) preprocessing (noise normalisation, removal of the pattern mean), 2) weighting decoding accuracies by decision values, and 3) computing distances in three different partitioning schemes (non-cross-validated, cross-validated, within-class-corrected). Four main conclusions emerged from our results. First, appropriate multivariate noise normalization substantially improved decoding accuracies and the reliability of dissimilarity measures. Second, LDA, SVM and WeiRD yielded high peak decoding accuracies and nearly identical time courses. Third, while using decoding accuracies for RSA was markedly less reliable than continuous distances, this disadvantage was ameliorated by decision-value-weighting of decoding accuracies. Fourth, the cross-validated Euclidean distance provided unbiased distance estimates and highly replicable representational dissimilarity matrices. Overall, we strongly advise the use of multivariate noise normalisation as a general preprocessing step, recommend LDA, SVM and WeiRD as classifiers for decoding and highlight the cross-validated Euclidean distance as a reliable and unbiased default choice for RSA.

Combined fMRI- and eye movement-based decoding of bistable plaid motion perception.

The phenomenon of bistable perception, in which perception alternates spontaneously despite constant sensory stimulation, has been particularly useful in probing the neural bases of conscious perception. The study of such bistability requires access to the observer's perceptual dynamics, which is usually achieved via active report. This report, however, constitutes a confounding factor in the study of conscious perception and can also be biased in the context of certain experimental manipulations. One approach to circumvent these problems is to track perceptual alternations using signals from the eyes or the brain instead of observers' reports. Here we aimed to optimize such decoding of perceptual alternations by combining eye and brain signals. Eye-tracking and functional magnetic resonance imaging (fMRI) was performed in twenty participants while they viewed a bistable visual plaid motion stimulus and reported perceptual alternations. Multivoxel pattern analysis (MVPA) for fMRI was combined with eye-tracking in a Support vector machine to decode participants' perceptual time courses from fMRI and eye-movement signals. While both measures individually already yielded high decoding accuracies (on average 86% and 88% correct, respectively) classification based on the two measures together further improved the accuracy (91% correct). These findings show that leveraging on both fMRI and eye movement data may pave the way for optimized no-report paradigms through improved decodability of bistable motion perception and hence for a better understanding of the neural correlates of consciousness.

Quantitative neurobiological evidence for accelerated brain aging in alcohol dependence.

The premature aging hypothesis of alcohol dependence proposes that the neurobiological and behavioural deficits in individuals with alcohol dependence are analogous to those of chronological aging. However, to date no systematic neurobiological evidence for this hypothesis has been provided. To test the hypothesis, 119 alcohol-dependent subjects and 97 age- and gender-matched healthy control subjects underwent structural MRI. Whole-brain grey matter volume maps were computed from structural MRI scans using voxel-based morphometry and parcelled into a comprehensive set of anatomical brain regions. Regional grey matter volume averages served as the basis for cross-regional similarity analyses and a brain age model. We found a striking correspondence between regional patterns of alcohol- and age-related grey matter loss across 110 brain regions. The brain age model revealed that the brain age of age-matched AD subjects was increased by up to 11.7 years. Interestingly, while no brain aging was detected in the youngest AD subjects (20-30 years), we found that alcohol-related brain aging systematically increased in the following age decades controlling for lifetime alcohol consumption and general health status. Together, these results provide strong evidence for an accelerated aging model of AD and indicate an elevated risk of alcohol-related brain aging in elderly individuals.

When Habits Are Dangerous: Alcohol Expectancies and Habitual Decision Making Predict Relapse in Alcohol Dependence.

BACKGROUND: Addiction is supposedly characterized by a shift from goal-directed to habitual decision making, thus facilitating automatic drug intake. The two-step task allows distinguishing between these mechanisms by computationally modeling goal-directed and habitual behavior as model-based and model-free control. In addicted patients, decision making may also strongly depend upon drug-associated expectations. Therefore, we investigated model-based versus model-free decision making and its neural correlates as well as alcohol expectancies in alcohol-dependent patients and healthy controls and assessed treatment outcome in patients. METHODS: Ninety detoxified, medication-free, alcohol-dependent patients and 96 age- and gender-matched control subjects underwent functional magnetic resonance imaging during the two-step task. Alcohol expectancies were measured with the Alcohol Expectancy Questionnaire. Over a follow-up period of 48 weeks, 37 patients remained abstinent and 53 patients relapsed as indicated by the Alcohol Timeline Followback method. RESULTS: Patients who relapsed displayed reduced medial prefrontal cortex activation during model-based decision making. Furthermore, high alcohol expectancies were associated with low model-based control in relapsers, while the opposite was observed in abstainers and healthy control subjects. However, reduced model-based control per se was not associated with subsequent relapse. CONCLUSIONS: These findings suggest that poor treatment outcome in alcohol dependence does not simply result from a shift from model-based to model-free control but is instead dependent on the interaction between high drug expectancies and low model-based decision making. Reduced model-based medial prefrontal cortex signatures in those who relapse point to a neural correlate of relapse risk. These observations suggest that therapeutic interventions should target subjective alcohol expectancies.

Visual Working Memory Enhances the Neural Response to Matching Visual Input.

Visual working memory (VWM) is used to maintain visual information available for subsequent goal-directed behavior. The content of VWM has been shown to affect the behavioral response to concurrent visual input, suggesting that visual representations originating from VWM and from sensory input draw upon a shared neural substrate (i.e., a sensory recruitment stance on VWM storage). Here, we hypothesized that visual information maintained in VWM would enhance the neural response to concurrent visual input that matches the content of VWM. To test this hypothesis, we measured fMRI BOLD responses to task-irrelevant stimuli acquired from 15 human participants (three males) performing a concurrent delayed match-to-sample task. In this task, observers were sequentially presented with two shape stimuli and a retro-cue indicating which of the two shapes should be memorized for subsequent recognition. During the retention interval, a task-irrelevant shape (the probe) was briefly presented in the peripheral visual field, which could either match or mismatch the shape category of the memorized stimulus. We show that this probe stimulus elicited a stronger BOLD response, and allowed for increased shape-classification performance, when it matched rather than mismatched the concurrently memorized content, despite identical visual stimulation. Our results demonstrate that VWM enhances the neural response to concurrent visual input in a content-specific way. This finding is consistent with the view that neural populations involved in sensory processing are recruited for VWM storage, and it provides a common explanation for a plethora of behavioral studies in which VWM-matching visual input elicits a stronger behavioral and perceptual response.SIGNIFICANCE STATEMENT Humans heavily rely on visual information to interact with their environment and frequently must memorize such information for later use. Visual working memory allows for maintaining such visual information in the mind's eye after termination of its retinal input. It is hypothesized that information maintained in visual working memory relies on the same neural populations that process visual input. Accordingly, the content of visual working memory is known to affect our conscious perception of concurrent visual input. Here, we demonstrate for the first time that visual input elicits an enhanced neural response when it matches the content of visual working memory, both in terms of signal strength and information content.

Mesolimbic confidence signals guide perceptual learning in the absence of external feedback.

It is well established that learning can occur without external feedback, yet normative reinforcement learning theories have difficulties explaining such instances of learning. Here, we propose that human observers are capable of generating their own feedback signals by monitoring internal decision variables. We investigated this hypothesis in a visual perceptual learning task using fMRI and confidence reports as a measure for this monitoring process. Employing a novel computational model in which learning is guided by confidence-based reinforcement signals, we found that mesolimbic brain areas encoded both anticipation and prediction error of confidence-in remarkable similarity to previous findings for external reward-based feedback. We demonstrate that the model accounts for choice and confidence reports and show that the mesolimbic confidence prediction error modulation derived through the model predicts individual learning success. These results provide a mechanistic neurobiological explanation for learning without external feedback by augmenting reinforcement models with confidence-based feedback.

Gaze Direction Modulates the Relation between Neural Responses to Faces and Visual Awareness.

Gaze direction and especially direct gaze is a powerful nonverbal cue that plays an important role in social interactions. Here we studied the neural mechanisms underlying the privileged access of direct gaze to visual awareness. We performed functional magnetic resonance imaging in healthy human volunteers who were exposed to faces with direct or averted gaze under continuous flash suppression, thereby manipulating their awareness of the faces. A gaze processing network comprising fusiform face area (FFA), superior temporal sulcus, amygdala, and intraparietal sulcus showed overall reduced neural responses when participants reported to be unaware of the faces. Interestingly, direct gaze elicited greater responses than averted gaze when participants were aware of the faces, but smaller responses when they were unaware. Additional between-subject correlation and single-trial analyses indicated that this pattern of results was due to a modulation of the relationship between neural responses and awareness by gaze direction: with increasing neural activation in the FFA, direct-gaze faces entered awareness more readily than averted-gaze faces. These findings suggest that for direct gaze, lower levels of neural activity are sufficient to give rise to awareness than for averted gaze, thus providing a neural basis for privileged access of direct gaze to awareness. Significance statement: Another person's eye gaze directed at oneself is a powerful social signal acting as a catalyst for further communication. Here, we studied the neural mechanisms underlying the prioritized access of direct gaze to visual awareness in healthy human volunteers and show that with increasing neural activation, direct-gaze faces enter awareness more readily than averted-gaze faces. This suggests that for a socially highly relevant cue like direct gaze, lower levels of neural activity are sufficient to give rise to awareness compared with averted gaze, possibly because the human brain is attuned to the efficient neural processing of direct gaze due to the biological importance of eye contact for social interactions.

Spatial attention enhances object coding in local and distributed representations of the lateral occipital complex.

The modulation of neural activity in visual cortex is thought to be a key mechanism of visual attention. The investigation of attentional modulation in high-level visual areas, however, is hampered by the lack of clear tuning or contrast response functions. In the present functional magnetic resonance imaging study we therefore systematically assessed how small voxel-wise biases in object preference across hundreds of voxels in the lateral occipital complex were affected when attention was directed to objects. We found that the strength of attentional modulation depended on a voxel's object preference in the absence of attention, a pattern indicative of an amplificatory mechanism. Our results show that such attentional modulation effectively increased the mutual information between voxel responses and object identity. Further, these local modulatory effects led to improved information-based object readout at the level of multi-voxel activation patterns and to an increased reproducibility of these patterns across repeated presentations. We conclude that attentional modulation enhances object coding in local and distributed object representations of the lateral occipital complex.

A hippocampal signature of perceptual learning in object recognition.

Perceptual learning is the improvement in perceptual performance through training or exposure. Here, we used fMRI before and after extensive behavioral training to investigate the effects of perceptual learning on the recognition of objects under challenging viewing conditions. Objects belonged either to trained or untrained categories. Trained categories were further subdivided into trained and untrained exemplars and were coupled with high or low monetary rewards during training. After a 3-day training, object recognition was markedly improved. Although there was a considerable transfer of learning to untrained exemplars within categories, an enhancing effect of reward reinforcement was specific to trained exemplars. fMRI showed that hippocampus responses to both trained and untrained exemplars of trained categories were enhanced by perceptual learning and correlated with the effect of reward reinforcement. Our results suggest a key role of hippocampus in object recognition after perceptual learning.

Non-holistic coding of objects in lateral occipital complex with and without attention.

A fundamental issue in visual cognition is whether high-level visual areas code objects in a part-based or a view-based (holistic) format. Previous behavioral and neuroimaging studies that examined the viewpoint invariance of object recognition have yielded ambiguous results, providing evidence for either type of representational format. A critical factor distinguishing the two formats could be the availability of attentional resources, as a number of priming studies have found greater viewpoint invariance for attended compared to unattended objects. It has therefore been suggested that the activation of part-based representations requires attention, whereas the activation of holistic representations occurs automatically irrespective of attention. Using functional magnetic resonance imaging in combination with a novel multivariate pattern analysis approach, the present study probed the format of object representations in human lateral occipital complex and its dependence on attention. We presented human participants with intact and half-split versions of objects that were either attended or unattended. Cross-classifying between intact and split objects, we found that the object-related information coded in activation patterns of intact objects is fully preserved in the patterns of split objects and vice versa. Importantly, the generalization between intact and split objects did not depend on attention. We conclude that lateral occipital complex codes objects in a non-holistic format, both in the presence and absence of attention.

Altered contextual modulation of primary visual cortex responses in schizophrenia.

Schizophrenia is typically associated with higher-level cognitive symptoms, such as disorganized thoughts, delusions, and hallucinations. However, deficits in visual processing have been consistently reported with the illness. Here, we provide strong neurophysiological evidence for a marked perturbation at the earliest level of cortical visual processing in patients with paranoid schizophrenia. Using functional magnetic resonance imaging (fMRI) and adapting a well-established approach from electrophysiology, we found that orientation-specific contextual modulation of cortical responses in human primary visual cortex (V1)--a hallmark of early neural encoding of visual stimuli--is dramatically reduced in patients with schizophrenia. This indicates that contextual processing in schizophrenia is altered at the earliest stages of visual cortical processing and supports current theories that emphasize the role of abnormalities in perceptual synthesis (eg, false inference) in schizophrenia.