Forward models demonstrate that repetition suppression is best modelled by local neural scaling

The distinct development of stimulus and response serial dependence

Serial dependence (SD) is a phenomenon wherein current perceptions are biased by the previous stimulus and response. This helps to attenuate perceptual noise and variability in sensory input and facilitates stable ongoing perceptions of the environment. However, little is known about the developmental trajectory of SD. This study investigates how the stimulus and response biases of the SD effect develop across three age groups. Conventional analyses, in which previous stimulus and response biases were assessed separately, revealed significant changes in the biases over time. Previous stimulus bias shifted from repulsion to attraction, while previous response bias evolved from attraction to greater attraction. However, there was a strong correlation between stimulus and response orientations. Therefore, a generalized linear mixed-effects (GLME) analysis that simultaneously considered both previous stimulus and response, outperformed separate analyses. This revealed that previous stimulus and response resulted in two distinct biases with different developmental trajectories. The repulsion bias of previous stimulus remained relatively stable across all age groups, whereas the attraction bias of previous response was significantly stronger in adults than in children and adolescents. These findings demonstrate that the repulsion bias towards preceding stimuli is established early in the developing brain (at least by around 10 years old), while the attraction bias towards responses is not fully developed until adulthood. Our findings provide new insights into the development of the SD phenomenon and how humans integrate two opposing mechanisms into their perceptual responses to external input during development.

Single-neuron mechanisms of neural adaptation in the human temporal lobe

A central function of the human brain is to adapt to new situations based on past experience. Adaptation is reflected behaviorally by shorter reaction times to repeating or similar stimuli, and neurophysiologically by reduced neural activity in bulk-tissue measurements with fMRI or EEG. Several potential single-neuron mechanisms have been hypothesized to cause this reduction of activity at the macroscopic level. We here explore these mechanisms using an adaptation paradigm with visual stimuli bearing abstract semantic similarity. We recorded intracranial EEG (iEEG) simultaneously with spiking activity of single neurons in the medial temporal lobes of 25 neurosurgical patients. Recording from 4917 single neurons, we demonstrate that reduced event-related potentials in the macroscopic iEEG signal are associated with a sharpening of single-neuron tuning curves in the amygdala, but with an overall reduction of single-neuron activity in the hippocampus, entorhinal cortex, and parahippocampal cortex, consistent with fatiguing in these areas.

Tilt aftereffect spreads across the visual field

The tilt aftereffect (TAE) is observed when adaptation to a tilted contour alters the perceived tilt of a subsequently presented contour. Thus far, TAE has been treated as a local aftereffect observed only at the location of the adapter. Whether and how TAE spreads to other locations in the visual field has not been systematically studied. Here, we sought an answer to this question by measuring TAE magnitudes at locations including but not limited to the adapter location. The adapter was a tilted grating presented at the same peripheral location throughout an experimental session. In a single trial, participants indicated the perceived tilt of a test grating presented after the adapter at one of fifteen locations in the same visual hemifield as the adapter. We found non-zero TAE magnitudes in all locations tested, showing that the effect spreads across the tested visual hemifield. Next, to establish a link between neuronal activity and behavioral results and to predict the possible neuronal origins of the spread, we built a computational model based on known characteristics of the visual cortex. The simulation results showed that the model could successfully capture the pattern of the behavioral results. Furthermore, the pattern of the optimized receptive field sizes suggests that mid-level visual areas, such as V4, could be critically involved in TAE and its spread across the visual field.

Improving the validity of neuroimaging decoding tests of invariant and configural neural representation

Many research questions in sensory neuroscience involve determining whether the neural representation of a stimulus property is invariant or specific to a particular stimulus context (e.g., Is object representation invariant to translation? Is the representation of a face feature specific to the context of other face features?). Between these two extremes, representations may also be context-tolerant or context-sensitive. Most neuroimaging studies have used operational tests in which a target property is inferred from a significant test against the null hypothesis of the opposite property. For example, the popular cross-classification test concludes that representations are invariant or tolerant when the null hypothesis of specificity is rejected. A recently developed neurocomputational theory suggests two insights regarding such tests. First, tests against the null of context-specificity, and for the alternative of context-invariance, are prone to false positives due to the way in which the underlying neural representations are transformed into indirect measurements in neuroimaging studies. Second, jointly performing tests against the nulls of invariance and specificity allows one to reach more precise and valid conclusions about the underlying representations, particularly when the null of invariance is tested using the fine-grained information from classifier decision variables rather than only accuracies (i.e., using the decoding separability test). Here, we provide empirical and computational evidence supporting both of these theoretical insights. In our empirical study, we use encoding of orientation and spatial position in primary visual cortex as a case study, as previous research has established that these properties are encoded in a context-sensitive way. Using fMRI decoding, we show that the cross-classification test produces false-positive conclusions of invariance, but that more valid conclusions can be reached by jointly performing tests against the null of invariance. The results of two simulations further support both of these conclusions. We conclude that more valid inferences about invariance or specificity of neural representations can be reached by jointly testing against both hypotheses, and using neurocomputational theory to guide the interpretation of results.

A modeling framework for determining modulation of neural-level tuning from non-invasive human fMRI data

Many neuroscience theories assume that tuning modulation of individual neurons underlies changes in human cognition. However, non-invasive fMRI lacks sufficient resolution to visualize this modulation. To address this limitation, we developed an analysis framework called Inferring Neural Tuning Modulation (INTM) for "peering inside" voxels. Precise specification of neural tuning from the BOLD signal is not possible. Instead, INTM compares theoretical alternatives for the form of neural tuning modulation that might underlie changes in BOLD across experimental conditions. The most likely form is identified via formal model comparison, with assumed parametric Normal tuning functions, followed by a non-parametric check of conclusions. We validated the framework by successfully identifying a well-established form of modulation: visual contrast-induced multiplicative gain for orientation tuned neurons. INTM can be applied to any experimental paradigm testing several points along a continuous feature dimension (e.g., direction of motion, isoluminant hue) across two conditions (e.g., with/without attention, before/after learning).

Dampened sensory representations for expected input across the ventral visual stream

Expectations, derived from previous experience, can help in making perception faster, more reliable and informative. A key neural signature of perceptual expectations is expectation suppression, an attenuated neural response to expected compared with unexpected stimuli. While expectation suppression has been reported using a variety of paradigms and recording methods, it remains unclear what neural modulation underlies this response attenuation. Sharpening models propose that neural populations tuned away from an expected stimulus are particularly suppressed by expectations, thereby resulting in an attenuated, but sharper population response. In contrast, dampening models suggest that neural populations tuned toward the expected stimulus are most suppressed, thus resulting in a dampened, less redundant population response. Empirical support is divided, with some studies favoring sharpening, while others support dampening. A key limitation of previous neuroimaging studies is the ability to draw inferences about neural-level modulations based on population (e.g. voxel) level signals. Indeed, recent simulations of repetition suppression showed that opposite neural modulations can lead to comparable population-level modulations. Forward models provide one solution to this inference limitation. Here, we used forward models to implement sharpening and dampening models, mapping neural modulations to voxel-level data. We show that a feature-specific gain modulation, suppressing neurons tuned toward the expected stimulus, best explains the empirical fMRI data. Thus, our results support the dampening account of expectation suppression, suggesting that expectations reduce redundancy in sensory cortex, and thereby promote updating of internal models on the basis of surprising information.

Neural tuning and representational geometry

A central goal of neuroscience is to understand the representations formed by brain activity patterns and their connection to behaviour. The classic approach is to investigate how individual neurons encode stimuli and how their tuning determines the fidelity of the neural representation. Tuning analyses often use the Fisher information to characterize the sensitivity of neural responses to small changes of the stimulus. In recent decades, measurements of large populations of neurons have motivated a complementary approach, which focuses on the information available to linear decoders. The decodable information is captured by the geometry of the representational patterns in the multivariate response space. Here we review neural tuning and representational geometry with the goal of clarifying the relationship between them. The tuning induces the geometry, but different sets of tuned neurons can induce the same geometry. The geometry determines the Fisher information, the mutual information and the behavioural performance of an ideal observer in a range of psychophysical tasks. We argue that future studies can benefit from considering both tuning and geometry to understand neural codes and reveal the connections between stimuli, brain activity and behaviour.

Visual short-term memory load modulates repetition related fMRI signal adaptation

While several computational models have suggested how predictive coding could be implemented on an algorithmic level, reference to cognitive processes remains rather sparse. A crucial process might be elevating relevant prior information from long-term memory to render it highly accessible for subsequent comparison with sensory input. In many models, visual short-term memory (VSTM) is considered as information from long-term memory in a state of elevated activity. We measured the BOLD signal in face-specific cortical areas using repetition suppression (RS) paradigm. RS has been associated with predictive processing in previous studies. We show that RS within the fusiform face area is significantly attenuated when VSTM is loaded with other, non-facial visual information. Although an unequivocal inference is not possible, the data indicate a role of VSTM for predictive processes as indexed by expectation-related RS.

Application of Empirical Mode Decomposition for Decoding Perception of Faces Using Magnetoencephalography

Neural decoding is useful to explore the timing and source location in which the brain encodes information. Higher classification accuracy means that an analysis is more likely to succeed in extracting useful information from noises. In this paper, we present the application of a nonlinear, nonstationary signal decomposition technique—the empirical mode decomposition (EMD), on MEG data. We discuss the fundamental concepts and importance of nonlinear methods when it comes to analyzing brainwave signals and demonstrate the procedure on a set of open-source MEG facial recognition task dataset. The improved clarity of data allowed further decoding analysis to capture distinguishing features between conditions that were formerly over-looked in the existing literature, while raising interesting questions concerning hemispheric dominance to the encoding process of facial and identity information.

Combining Repetition Suppression and Pattern Analysis Provides New Insights into the Role of M1 and Parietal Areas in Skilled Sequential Actions

How does the brain change during learning? In functional magnetic resonance imaging (fMRI) studies, both multivariate pattern analysis (MVPA) and repetition suppression (RS) have been used to detect changes in neuronal representations. In the context of motor sequence learning, the two techniques have provided discrepant findings: pattern analysis showed that only premotor and parietal regions, but not primary motor cortex (M1), develop a representation of trained sequences. In contrast, RS suggested trained sequence representations in all these regions. Here, we applied both analysis techniques to a five-week finger sequence training study, in which participants executed each sequence twice before switching to a different sequence. Both RS and pattern analysis indicated learning-related changes for parietal areas, but only RS showed a difference between trained and untrained sequences in M1. A more fine-grained analysis, however, revealed that the RS effect in M1 reflects a fundamentally different process than in parietal areas. On the first execution, M1 represents especially the first finger of each sequence, likely reflecting preparatory processes. This effect dramatically reduces during the second execution. In contrast, parietal areas represent the identity of a sequence, and this representation stays relatively stable on the second execution. These results suggest that the RS effect does not reflect a trained sequence representation in M1, but rather a preparatory signal for movement initiation. More generally, our study demonstrates that across regions RS can reflect different representational changes in the neuronal population code, emphasizing the importance of combining pattern analysis and RS techniques.SIGNIFICANCE STATEMENT Previous studies using pattern analysis have suggested that primary motor cortex (M1) does not represent learnt sequential actions. However, a study using repetition suppression (RS) has reported M1 changes during motor sequence learning. Combining both techniques, we first replicate the discrepancy between them, with learning-related changes in M1 in RS, but not pattern dissimilarities. We further analyzed the representational changes with repetition, and found that the RS effects differ across regions. M1's activity represents the starting finger of the sequence, an effect that vanishes with repetition. In contrast, activity patterns in parietal areas exhibit sequence dependency, which persists with repetition. These results demonstrate the importance of combining RS and pattern analysis to understand the function of brain regions.

Population Models, Not Analyses, of Human Neuroscience Measurements

Selectivity for many basic properties of visual stimuli, such as orientation, is thought to be organized at the scale of cortical columns, making it difficult or impossible to measure directly with noninvasive human neuroscience measurement. However, computational analyses of neuroimaging data have shown that selectivity for orientation can be recovered by considering the pattern of response across a region of cortex. This suggests that computational analyses can reveal representation encoded at a finer spatial scale than is implied by the spatial resolution limits of measurement techniques. This potentially opens up the possibility to study a much wider range of neural phenomena that are otherwise inaccessible through noninvasive measurement. However, as we review in this article, a large body of evidence suggests an alternative hypothesis to this superresolution account: that orientation information is available at the spatial scale of cortical maps and thus easily measurable at the spatial resolution of standard techniques. In fact, a population model shows that this orientation information need not even come from single-unit selectivity for orientation tuning, but instead can result from population selectivity for spatial frequency. Thus, a categorical error of interpretation can result whereby orientation selectivity can be confused with spatial frequency selectivity. This is similarly problematic for the interpretation of results from numerous studies of more complex representations and cognitive functions that have built upon the computational techniques used to reveal stimulus orientation. We suggest in this review that these interpretational ambiguities can be avoided by treating computational analyses as models of the neural processes that give rise to measurement. Building upon the modeling tradition in vision science using considerations of whether population models meet a set of core criteria is important for creating the foundation for a cumulative and replicable approach to making valid inferences from human neuroscience measurements. Expected final online publication date for the Annual Review of Vision Science, Volume 7 is September 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Enhanced inter-regional coupling of neural responses and repetition suppression provide separate contributions to long-term behavioral priming

Stimulus identification commonly improves with repetition over long delays ("repetition priming"), whereas neural activity commonly decreases ("repetition suppression"). Multiple models have been proposed to explain this brain-behavior relationship, predicting alterations in functional and/or effective connectivity (Synchrony and Predictive Coding models), in the latency of neural responses (Facilitation model), and in the relative similarity of neural representations (Sharpening model). Here, we test these predictions with fMRI during overt and covert naming of repeated and novel objects. While we find partial support for predictions of the Facilitation and Sharpening models in the left fusiform gyrus and left frontal cortex, the data were most consistent with the Synchrony model, with increased coupling between right temporoparietal and anterior cingulate cortex for repeated objects that correlated with priming magnitude across participants. Increased coupling and repetition suppression varied independently, each explaining unique variance in priming and requiring modifications of all current models.

Limited Evidence for Sensory Prediction Error Responses in Visual Cortex of Macaques and Humans

A recent formulation of predictive coding theory proposes that a subset of neurons in each cortical area encodes sensory prediction errors, the difference between predictions relayed from higher cortex and the sensory input. Here, we test for evidence of prediction error responses in spiking responses and local field potentials (LFP) recorded in primary visual cortex and area V4 of macaque monkeys, and in complementary electroencephalographic (EEG) scalp recordings in human participants. We presented a fixed sequence of visual stimuli on most trials, and violated the expected ordering on a small subset of trials. Under predictive coding theory, pattern-violating stimuli should trigger robust prediction errors, but we found that spiking, LFP and EEG responses to expected and pattern-violating stimuli were nearly identical. Our results challenge the assertion that a fundamental computational motif in sensory cortex is to signal prediction errors, at least those based on predictions derived from temporal patterns of visual stimulation.

Can expectation suppression be explained by reduced attention to predictable stimuli?

The expectation-suppression effect - reduced stimulus-evoked responses to expected stimuli - is widely considered to be an empirical hallmark of reduced prediction errors in the framework of predictive coding. Here we challenge this notion by proposing that that expectation suppression could be explained by a reduced attention effect. Specifically, we argue that reduced responses to predictable stimuli can also be explained by a reduced saliency-driven allocation of attention. We base our discussion mainly on findings in the visual cortex and propose that resolving this controversy requires the assessment of qualitative differences between the ways in which attention and surprise enhance brain responses.

Patterns and networks of language control in bilingual language production

Many studies have examined the cognitive and neural mechanisms of bilingual language control, but few of them have captured the pattern information of brain activation. However, language control is a functional combination of both cognitive control and language production which demonstrates distinct patterns of neural representations under different language contexts. The first aim of the present study was to explore the brain activation patterns of language control using multivoxel pattern analysis (MVPA). During the experiment, Chinese-English bilinguals were instructed to name pictures in either Chinese or English according to a visually presented cue while being scanned with functional magnetic resonance imaging (fMRI). We found that patterns of neural activity in frontal brain regions including the left dorsolateral prefrontal cortex, left inferior frontal gyrus, left supplementary motor area, anterior cingulate cortex, bilateral precentral gyri, and the left cerebellum reliably discriminated between switch and non-switch conditions. We then modeled causal interactions between these regions by applying effective connectivity analyses based on an extended unified structure equation model (euSEM). The results showed that frontal and fronto-cerebellar connectivity were key components of the language control network. These findings further reveal the engagement of the cognitive control network in bilingual language production.

A Gradient of Sharpening Effects by Perceptual Prior across the Human Cortical Hierarchy

Prior knowledge profoundly influences perceptual processing. Previous studies have revealed consistent suppression of predicted stimulus information in sensory areas, but how prior knowledge modulates processing higher up in the cortical hierarchy remains poorly understood. In addition, the mechanism leading to suppression of predicted sensory information remains unclear, and studies thus far have revealed a mixed pattern of results in support of either the "sharpening" or "dampening" model. Here, using 7T fMRI in humans (both sexes), we observed that prior knowledge acquired from fast, one-shot perceptual learning sharpens neural representation throughout the ventral visual stream, generating suppressed sensory responses. In contrast, the frontoparietal and default mode networks exhibit similar sharpening of content-specific neural representation, but in the context of unchanged and enhanced activity magnitudes, respectively: a pattern we refer to as "selective enhancement." Together, these results reveal a heretofore unknown macroscopic gradient of prior knowledge's sharpening effect on neural representations across the cortical hierarchy.SIGNIFICANCE STATEMENT A fundamental question in neuroscience is how prior knowledge shapes perceptual processing. Perception is constantly informed by internal priors in the brain acquired from past experiences, but the neural mechanisms underlying this process are poorly understood. To date, research on this question has focused on early visual regions, reporting a consistent downregulation when predicted stimuli are encountered. Here, using a dramatic one-shot perceptual learning paradigm, we observed that prior knowledge results in sharper neural representations across the cortical hierarchy of the human brain through a gradient of mechanisms. In visual regions, neural responses tuned away from internal predictions are suppressed. In frontoparietal regions, neural activity consistent with priors is selectively enhanced. These results deepen our understanding of how prior knowledge informs perception.

Adaptation aftereffects reveal how categorization training changes the encoding of face identity

Previous research suggests that learning to categorize faces along a novel dimension changes the perceptual representation of such dimension, increasing its discriminability, its invariance, and the information used to identify faces varying along the dimension. A common interpretation of these results is that categorization training promotes the creation of novel dimensions, rather than simply the enhancement of already existing representations. Here, we trained a group of participants to categorize faces that varied along two morphing dimensions, one of them relevant to the categorization task and the other irrelevant to the task. An untrained group did not receive such categorization training. In three experiments, we used face adaptation aftereffects to explore how categorization training changes the encoding of face identities at the extremes of the category-relevant dimension and whether such training produces encoding of the category-relevant dimension as a preferred direction in face space. The pattern of results suggests that categorization training enhances the already existing norm-based coding of face identity, rather than creating novel category-relevant representations. We formalized this conclusion in a model that explains the most important results in our experiments and serves as a working hypothesis for future work in this area.

Duration Selectivity in Right Parietal Cortex Reflects the Subjective Experience of Time

The perception of duration in the subsecond range has been hypothesized to be mediated by the population response of duration-sensitive units, each tuned to a preferred duration. One line of support for this hypothesis comes from neuroimaging studies showing that cortical regions, such as in parietal cortex exhibit duration tuning. It remains unclear whether this representation is based on the physical duration of the sensory input or the subjective duration, a question that is important given that our perception of the passage of time is often not veridical, but rather, biased by various contextual factors. Here we used fMRI to examine the neural correlates of subjective time perception in human participants. To manipulate perceived duration while holding physical duration constant, we used an adaptation method, in which, before judging the duration of a test stimulus, the participants were exposed to a train of adapting stimuli of a fixed duration. Behaviorally, this procedure produced a pronounced negative aftereffect: A short adaptor biased participants to judge stimuli as longer and a long adaptor-biased participants to judge stimuli as shorter. Duration tuning modulation, manifest as an attenuated BOLD response to stimuli similar in duration to the adaptor, was only observed in the right supramarginal gyrus (SMG) of the parietal lobe and middle occipital gyrus, bilaterally. Across individuals, the magnitude of the behavioral aftereffect was positively correlated with the magnitude of duration tuning modulation in SMG. These results indicate that duration-tuned neural populations in right SMG reflect the subjective experience of time.SIGNIFICANCE STATEMENT The subjective sense of time is a fundamental dimension of sensory experience. To investigate the neural basis of subjective time, we conducted an fMRI study, using an adaptation procedure that allowed us to manipulate perceived duration while holding physical duration constant. Regions within the occipital cortex and right parietal lobe showed duration tuning that was modulated when the test stimuli were similar in duration to the adaptor. Moreover, the magnitude of the distortion in perceived duration was correlated with the degree of duration tuning modulation in the parietal region. These results provide strong physiological evidence that the population coding of time in the right parietal cortex reflects our subjective experience of time.

Diverse Temporal Dynamics of Repetition Suppression Revealed by Intracranial Recordings in the Human Ventral Temporal Cortex

Repeated stimulus presentations commonly produce decreased neural responses-a phenomenon known as repetition suppression (RS) or adaptation-in ventral temporal cortex (VTC) of humans and nonhuman primates. However, the temporal features of RS in human VTC are not well understood. To fill this gap in knowledge, we utilized the precise spatial localization and high temporal resolution of electrocorticography (ECoG) from nine human subjects implanted with intracranial electrodes in the VTC. The subjects viewed nonrepeated and repeated images of faces with long-lagged intervals and many intervening stimuli between repeats. We report three main findings: 1) robust RS occurs in VTC for activity in high-frequency broadband (HFB), but not lower-frequency bands; 2) RS of the HFB signal is associated with lower peak magnitude (PM), lower total responses, and earlier peak responses; and 3) RS effects occur early within initial stages of stimulus processing and persist for the entire stimulus duration. We discuss these findings in the context of early and late components of visual perception, as well as theoretical models of repetition suppression.

Temporal tuning of repetition suppression across the visual cortex

The visual system adapts to its recent history. A phenomenon related to this is repetition suppression (RS), a reduction in neural responses to repeated compared with nonrepeated visual input. An intriguing hypothesis is that the timescale over which RS occurs across the visual hierarchy is tuned to the temporal statistics of visual input features, which change rapidly in low-level areas but are more stable in higher level areas. Here, we tested this hypothesis by studying the influence of the temporal lag between successive visual stimuli on RS throughout the visual system using functional (f)MRI. Twelve human volunteers engaged in four fMRI sessions in which we characterized the blood oxygen level-dependent response to pairs of repeated and nonrepeated natural images with interstimulus intervals (ISI) ranging from 50 to 1,000 ms to quantify the temporal tuning of RS along the posterior-anterior axis of the visual system. As expected, RS was maximal for short ISIs and decayed with increasing ISI. Crucially, however, and against our hypothesis, RS decayed at a similar rate in early and late visual areas. This finding challenges the prevailing view that the timescale of RS increases along the posterior-anterior axis of the visual system and suggests that RS is not tuned to temporal input regularities.NEW & NOTEWORTHY Visual areas show reduced neural responses to repeated compared with nonrepeated visual input, a phenomenon termed repetition suppression (RS). Here we show that RS decays at a similar rate in low- and high-level visual areas, suggesting that the short-term decay of RS across the visual hierarchy is not tuned to temporal input regularities. This may limit the specificity with which the mechanisms underlying RS could optimize the processing of input features across the visual hierarchy.

Suppressed Sensory Response to Predictable Object Stimuli throughout the Ventral Visual Stream

Prediction plays a crucial role in perception, as prominently suggested by predictive coding theories. However, the exact form and mechanism of predictive modulations of sensory processing remain unclear, with some studies reporting a downregulation of the sensory response for predictable input whereas others observed an enhanced response. In a similar vein, downregulation of the sensory response for predictable input has been linked to either sharpening or dampening of the sensory representation, which are opposite in nature. In the present study, we set out to investigate the neural consequences of perceptual expectation of object stimuli throughout the visual hierarchy, using fMRI in human volunteers. Participants of both sexes were exposed to pairs of sequentially presented object images in a statistical learning paradigm, in which the first object predicted the identity of the second object. Image transitions were not task relevant; thus, all learning of statistical regularities was incidental. We found strong suppression of neural responses to expected compared with unexpected stimuli throughout the ventral visual stream, including primary visual cortex, lateral occipital complex, and anterior ventral visual areas. Expectation suppression in lateral occipital complex scaled positively with image preference and voxel selectivity, lending support to the dampening account of expectation suppression in object perception.SIGNIFICANCE STATEMENT It has been suggested that the brain fundamentally relies on predictions and constructs models of the world to make sense of sensory information. Previous research on the neural basis of prediction has documented suppressed neural responses to expected compared with unexpected stimuli. In the present study, we demonstrate robust expectation suppression throughout the entire ventral visual stream, and underlying this suppression a dampening of the sensory representation in object-selective visual cortex, but not in primary visual cortex. Together, our results provide novel evidence in support of theories conceptualizing perception as an active inference process, which selectively dampens cortical representations of predictable objects. This dampening may support our ability to automatically filter out irrelevant, predictable objects.