Object Vision in a Structured World

Decoding cognition in neurodevelopmental, psychiatric and neurological conditions with multivariate pattern analysis of EEG data

Multivariate pattern analysis (MVPA) of electroencephalographic (EEG) data represents a revolutionary approach to investigate how the brain encodes information. By considering complex interactions among spatio-temporal features at the individual level, MVPA overcomes the limitations of univariate techniques, which often fail to account for the significant inter- and intra-individual neural variability. This is particularly relevant when studying clinical populations, and therefore MVPA of EEG data has recently started to be employed as a tool to study cognition in brain disorders. Here, we review the insights offered by this methodology in the study of anomalous patterns of neural activity in conditions such as autism, ADHD, schizophrenia, dyslexia, neurological and neurodegenerative disorders, within different cognitive domains (perception, attention, memory, consciousness). Despite potential drawbacks that should be attentively addressed, these studies reveal a peculiar sensitivity of MVPA in unveiling dysfunctional and compensatory neurocognitive dynamics of information processing, which often remain blind to traditional univariate approaches. Such higher sensitivity in characterizing individual neurocognitive profiles can provide unique opportunities to optimise assessment and promote personalised interventions.

Adaptation in the visual system: Networked fatigue or suppressed prediction error signalling?

Our brains are constantly adapting to changes in our visual environments. Neural adaptation exerts a persistent influence on the activity of sensory neurons and our perceptual experience, however there is a lack of consensus regarding how adaptation is implemented in the visual system. One account describes fatigue-based mechanisms embedded within local networks of stimulus-selective neurons (networked fatigue models). Another depicts adaptation as a product of stimulus expectations (predictive coding models). In this review, I evaluate neuroimaging and psychophysical evidence that poses fundamental problems for predictive coding models of neural adaptation. Specifically, I discuss observations of distinct repetition and expectation effects, as well as incorrect predictions of repulsive adaptation aftereffects made by predictive coding accounts. Based on this evidence, I argue that networked fatigue models provide a more parsimonious account of adaptation effects in the visual system. Although stimulus expectations can be formed based on recent stimulation history, any consequences of these expectations are likely to co-occur (or interact) with effects of fatigue-based adaptation. I conclude by proposing novel, testable hypotheses relating to interactions between fatigue-based adaptation and other predictive processes, focusing on stimulus feature extrapolation phenomena.

Fine-grained knowledge about manipulable objects is well-predicted by contrastive language image pre-training

Object recognition is an important ability that relies on distinguishing between similar objects (e.g., deciding which utensil(s) to use at different stages of meal preparation). Recent work describes the fine-grained organization of knowledge about manipulable objects via the study of the constituent dimensions that are most relevant to human behavior, for example, vision, manipulation, and function-based properties. A logical extension of this work concerns whether or not these dimensions are uniquely human, or can be approximated by deep learning. Here, we show that behavioral dimensions are generally well-predicted by CLIP-ViT - a multimodal network trained on a large and diverse set of image-text pairs. Moreover, this model outperforms comparison networks pre-trained on smaller, image-only datasets. These results demonstrate the impressive capacity of CLIP-ViT to approximate fine-grained object knowledge. We discuss the possible sources of this benefit relative to other models (e.g., multimodal vs. image-only pre-training, dataset size, architecture).

Evidence for a role of synchrony but not common fate in the perception of biological group movements

Extensive research has shown that observers are able to efficiently extract summary information from groups of people. However, little is known about the cues that determine whether multiple people are represented as a social group or as independent individuals. Initial research on this topic has primarily focused on the role of static cues. Here, we instead investigate the role of dynamic cues. In two experiments with male and female human participants, we use EEG frequency tagging to investigate the influence of two fundamental Gestalt principles - synchrony and common fate - on the grouping of biological movements. In Experiment 1, we find that brain responses coupled to four point-light figures walking together are enhanced when they move in sync vs. out of sync, but only when they are presented upright. In contrast, we found no effect of movement direction (i.e., common fate). In Experiment 2, we rule out that synchrony takes precedence over common fate by replicating the null effect of movement direction while keeping synchrony constant. These results suggest that synchrony plays an important role in the processing of biological group movements. In contrast, the role of common fate is less clear and will require further research.

A Phone in a Basket Looks Like a Knife in a Cup: Role-Filler Independence in Visual Processing

When a piece of fruit is in a bowl, and the bowl is on a table, we appreciate not only the individual objects and their features, but also the relations containment and support, which abstract away from the particular objects involved. Independent representation of roles (e.g., containers vs. supporters) and "fillers" of those roles (e.g., bowls vs. cups, tables vs. chairs) is a core principle of language and higher-level reasoning. But does such role-filler independence also arise in automatic visual processing? Here, we show that it does, by exploring a surprising error that such independence can produce. In four experiments, participants saw a stream of images containing different objects arranged in force-dynamic relations-e.g., a phone contained in a basket, a marker resting on a garbage can, or a knife sitting in a cup. Participants had to respond to a single target image (e.g., a phone in a basket) within a stream of distractors presented under time constraints. Surprisingly, even though participants completed this task quickly and accurately, they false-alarmed more often to images matching the target's relational category than to those that did not-even when those images involved completely different objects. In other words, participants searching for a phone in a basket were more likely to mistakenly respond to a knife in a cup than to a marker on a garbage can. Follow-up experiments ruled out strategic responses and also controlled for various confounding image features. We suggest that visual processing represents relations abstractly, in ways that separate roles from fillers.

Face detection in contextual scenes

Object and scene perception are intertwined. When objects are expected to appear within a particular scene, they are detected and categorised with greater speed and accuracy. This study examined whether such context effects also moderate the perception of social objects such as faces. Female and male faces were embedded in scenes with a stereotypical female or male context. Semantic congruency of these scene contexts influenced the categorisation of faces (Experiment 1). These effects were bi-directional, such that face sex also affected scene categorisation (Experiment 2), suggesting concurrent automatic processing of both levels. In contrast, the more elementary task of face detection was not affected by semantic scene congruency (Experiment 3), even when scenes were previewed prior to face presentation (Experiment 4). This pattern of results indicates that semantic scene context can affect categorisation of faces. However, the earlier perceptual stage of detection appears to be encapsulated from the cognitive processes that give rise to this contextual interference.

Motor Strategies: The Role of Active Behavior in Spatial Hearing Research

When completing a task, the ability to implement behavioral strategies to solve it in an effective and cognitively less-demanding way is extremely adaptive for humans. This behavior makes it possible to accumulate evidence and test one's own predictions about the external world. In this work, starting from examples in the field of spatial hearing research, I analyze the importance of considering motor strategies in perceptual tasks, and I stress the urgent need to create ecological experimental settings, which are essential in allowing the implementation of such behaviors and in measuring them. In particular, I will consider head movements as an example of strategic behavior implemented to solve acoustic space-perception tasks.

Representations of imaginary scenes and their properties in cortical alpha activity

Imagining natural scenes enables us to engage with a myriad of simulated environments. How do our brains generate such complex mental images? Recent research suggests that cortical alpha activity carries information about individual objects during visual imagery. However, it remains unclear if more complex imagined contents such as natural scenes are similarly represented in alpha activity. Here, we answer this question by decoding the contents of imagined scenes from rhythmic cortical activity patterns. In an EEG experiment, participants imagined natural scenes based on detailed written descriptions, which conveyed four complementary scene properties: openness, naturalness, clutter level and brightness. By conducting classification analyses on EEG power patterns across neural frequencies, we were able to decode both individual imagined scenes as well as their properties from the alpha band, showing that also the contents of complex visual images are represented in alpha rhythms. A cross-classification analysis between alpha power patterns during the imagery task and during a perception task, in which participants were presented images of the described scenes, showed that scene representations in the alpha band are partly shared between imagery and late stages of perception. This suggests that alpha activity mediates the top-down re-activation of scene-related visual contents during imagery.

Predictions and errors are distinctly represented across V1 layers

Popular accounts of mind and brain propose that the brain continuously forms predictions about future sensory inputs and combines predictions with inputs to determine what we perceive.1,2,3,4,5,6 Under "predictive processing" schemes, such integration is supported by the hierarchical organization of the cortex, whereby feedback connections communicate predictions from higher-level deep layers to agranular (superficial and deep) lower-level layers.7,8,9,10 Predictions are compared with input to compute the "prediction error," which is transmitted up the hierarchy from superficial layers of lower cortical regions to the middle layers of higher areas, to update higher-level predictions until errors are reconciled.11,12,13,14,15 In the primary visual cortex (V1), predictions have thereby been proposed to influence representations in deep layers while error signals may be computed in superficial layers. Despite the framework's popularity, there is little evidence for these functional distinctions because, to our knowledge, unexpected sensory events have not previously been presented in human laminar paradigms to contrast against expected events. To this end, this 7T fMRI study contrasted V1 responses to expected (75% likely) and unexpected (25%) Gabor orientations. Multivariate decoding analyses revealed an interaction between expectation and layer, such that expected events could be decoded with comparable accuracy across layers, while unexpected events could only be decoded in superficial laminae. Although these results are in line with these accounts that have been popular for decades, such distinctions have not previously been demonstrated in humans. We discuss how both prediction and error processes may operate together to shape our unitary perceptual experiences.

Neural Mechanisms Determining the Duration of Task-free, Self-paced Visual Perception

Humans spend hours each day spontaneously engaging with visual content, free from specific tasks and at their own pace. Currently, the brain mechanisms determining the duration of self-paced perceptual behavior remain largely unknown. Here, participants viewed naturalistic images under task-free settings and self-paced each image's viewing duration while undergoing EEG and pupillometry recordings. Across two independent data sets, we observed large inter- and intra-individual variability in viewing duration. However, beyond an image's presentation order and category, specific image content had no consistent effects on spontaneous viewing duration across participants. Overall, longer viewing durations were associated with sustained enhanced posterior positivity and anterior negativity in the ERPs. Individual-specific variations in the spontaneous viewing duration were consistently correlated with evoked EEG activity amplitudes and pupil size changes. By contrast, presentation order was selectively correlated with baseline alpha power and baseline pupil size. Critically, spontaneous viewing duration was strongly predicted by the temporal stability in neural activity patterns starting as early as 350 msec after image onset, suggesting that early neural stability is a key predictor for sustained perceptual engagement. Interestingly, neither bottom-up nor top-down predictions about image category influenced spontaneous viewing duration. Overall, these results suggest that individual-specific factors can influence perceptual processing at a surprisingly early time point and influence the multifaceted ebb and flow of spontaneous human perceptual behavior in naturalistic settings.

Concurrent Encoding of Sequence Predictability and Event-Evoked Prediction Error in Unfolding Auditory Patterns

Human listeners possess an innate capacity to discern patterns within rapidly unfolding sensory input. Core questions, guiding ongoing research, focus on the mechanisms through which these representations are acquired and whether the brain prioritizes or suppresses predictable sensory signals. Previous work, using fast auditory sequences (tone-pips presented at a rate of 20 Hz), revealed sustained response effects that appear to track the dynamic predictability of the sequence. Here, we extend the investigation to slower sequences (4 Hz), permitting the isolation of responses to individual tones. Stimuli were 50 ms tone-pips, ordered into random (RND) and regular (REG; a repeating pattern of 10 frequencies) sequences; Two timing profiles were created: in "fast" sequences, tone-pips were presented in direct succession (20 Hz); in "slow" sequences, tone-pips were separated by a 200 ms silent gap (4 Hz). Naive participants (N = 22; both sexes) passively listened to these sequences, while brain responses were recorded using magnetoencephalography (MEG). Results unveiled a heightened magnitude of sustained brain responses in REG when compared to RND patterns. This manifested from three tones after the onset of the pattern repetition, even in the context of slower sequences characterized by extended pattern durations (2,500 ms). This observation underscores the remarkable implicit sensitivity of the auditory brain to acoustic regularities. Importantly, brain responses evoked by single tones exhibited the opposite pattern-stronger responses to tones in RND than REG sequences. The demonstration of simultaneous but opposing sustained and evoked response effects reveals concurrent processes that shape the representation of unfolding auditory patterns.

Individual differences in internal models explain idiosyncrasies in scene perception

According to predictive processing theories, vision is facilitated by predictions derived from our internal models of what the world should look like. However, the contents of these models and how they vary across people remains unclear. Here, we use drawing as a behavioral readout of the contents of the internal models in individual participants. Participants were first asked to draw typical versions of scene categories, as descriptors of their internal models. These drawings were converted into standardized 3d renders, which we used as stimuli in subsequent scene categorization experiments. Across two experiments, participants' scene categorization was more accurate for renders tailored to their own drawings compared to renders based on others' drawings or copies of scene photographs, suggesting that scene perception is determined by a match with idiosyncratic internal models. Using a deep neural network to computationally evaluate similarities between scene renders, we further demonstrate that graded similarity to the render based on participants' own typical drawings (and thus to their internal model) predicts categorization performance across a range of candidate scenes. Together, our results showcase the potential of a new method for understanding individual differences - starting from participants' personal expectations about the structure of real-world scenes.

Integrative processing in artificial and biological vision predicts the perceived beauty of natural images

Previous research shows that the beauty of natural images is already determined during perceptual analysis. However, it is unclear which perceptual computations give rise to the perception of beauty. Here, we tested whether perceived beauty is predicted by spatial integration across an image, a perceptual computation that reduces processing demands by aggregating image parts into more efficient representations of the whole. We quantified integrative processing in an artificial deep neural network model, where the degree of integration was determined by the amount of deviation between activations for the whole image and its constituent parts. This quantification of integration predicted beauty ratings for natural images across four studies with different stimuli and designs. In a complementary functional magnetic resonance imaging study, we show that integrative processing in human visual cortex similarly predicts perceived beauty. Together, our results establish integration as a computational principle that facilitates perceptual analysis and thereby mediates the perception of beauty.

The Accuracy and Precision of Memory for Natural Scenes: A Walk in the Park

It is challenging to quantify the accuracy and precision of scene memory because it is unclear what 'space' scenes occupy (how can we quantify error when misremembering a natural scene?). To address this, we exploited the ecologically valid, metric space in which scenes occur and are represented: routes. In a delayed estimation task, participants briefly saw a target scene drawn from a video of an outdoor 'route loop', then used a continuous report wheel of the route to pinpoint the scene. Accuracy was high and unbiased, indicating there was no net boundary extension/contraction. Interestingly, precision was higher for routes that were more self-similar (as characterized by the half-life, in meters, of a route's Multiscale Structural Similarity index), consistent with previous work finding a 'similarity advantage' where memory precision is regulated according to task demands. Overall, scenes were remembered to within a few meters of their actual location.

Vision-driven metasurfaces for perception enhancement

Metasurfaces have exhibited unprecedented degree of freedom in manipulating electromagnetic (EM) waves and thus provide fantastic front-end interfaces for smart systems. Here we show a framework for perception enhancement based on vision-driven metasurface. Human's eye movements are matched with microwave radiations to extend the humans' perception spectrum. By this means, our eyes can "sense" visual information and invisible microwave information. Several experimental demonstrations are given for specific implementations, including a physiological-signal-monitoring system, an "X-ray-glasses" system, a "glimpse-and-forget" tracking system and a speech reception system for deaf people. Both the simulation and experiment results verify evident advantages in perception enhancement effects and improving information acquisition efficiency. This framework can be readily integrated into healthcare systems to monitor physiological signals and to offer assistance for people with disabilities. This work provides an alternative framework for perception enhancement and may find wide applications in healthcare, wearable devices, search-and-rescue and others.

Category-Selective Representation of Relationships in the Visual Cortex

Understanding social interaction requires processing social agents and their relationships. The latest results show that much of this process is visually solved: visual areas can represent multiple people encoding emergent information about their interaction that is not explained by the response to the individuals alone. A neural signature of this process is an increased response in visual areas, to face-to-face (seemingly interacting) people, relative to people presented as unrelated (back-to-back). This effect highlighted a network of visual areas for representing relational information. How is this network organized? Using functional MRI, we measured the brain activity of healthy female and male humans (N = 42), in response to images of two faces or two (head-blurred) bodies, facing toward or away from each other. Taking the facing > non-facing effect as a signature of relation perception, we found that relations between faces and between bodies were coded in distinct areas, mirroring the categorical representation of faces and bodies in the visual cortex. Additional analyses suggest the existence of a third network encoding relations between (nonsocial) objects. Finally, a separate occipitotemporal network showed the generalization of relational information across body, face, and nonsocial object dyads (multivariate pattern classification analysis), revealing shared properties of relations across categories. In sum, beyond single entities, the visual cortex encodes the relations that bind multiple entities into relationships; it does so in a category-selective fashion, thus respecting a general organizing principle of representation in high-level vision. Visual areas encoding visual relational information can reveal the processing of emergent properties of social (and nonsocial) interaction, which trigger inferential processes.

Converging evidence that left extrastriate body area supports visual sensitivity to social interactions

Navigating our complex social world requires processing the interactions we observe. Recent psychophysical and neuroimaging studies provide parallel evidence that the human visual system may be attuned to efficiently perceive dyadic interactions. This work implies, but has not yet demonstrated, that activity in body-selective cortical regions causally supports efficient visual perception of interactions. We adopt a multi-method approach to close this important gap. First, using a large fMRI dataset (n = 92), we found that the left hemisphere extrastriate body area (EBA) responds more to face-to-face than non-facing dyads. Second, we replicated a behavioral marker of visual sensitivity to interactions: categorization of facing dyads is more impaired by inversion than non-facing dyads. Third, in a pre-registered experiment, we used fMRI-guided transcranial magnetic stimulation to show that online stimulation of the left EBA, but not a nearby control region, abolishes this selective inversion effect. Activity in left EBA, thus, causally supports the efficient perception of social interactions.

Computational components of visual predictive coding circuitry

If a full visual percept can be said to be a 'hypothesis', so too can a neural 'prediction' - although the latter addresses one particular component of image content (such as 3-dimensional organisation, the interplay between lighting and surface colour, the future trajectory of moving objects, and so on). And, because processing is hierarchical, predictions generated at one level are conveyed in a backward direction to a lower level, seeking to predict, in fact, the neural activity at that prior stage of processing, and learning from errors signalled in the opposite direction. This is the essence of 'predictive coding', at once an algorithm for information processing and a theoretical basis for the nature of operations performed by the cerebral cortex. Neural models for the implementation of predictive coding invoke specific functional classes of neuron for generating, transmitting and receiving predictions, and for producing reciprocal error signals. Also a third general class, 'precision' neurons, tasked with regulating the magnitude of error signals contingent upon the confidence placed upon the prediction, i.e., the reliability and behavioural utility of the sensory data that it predicts. So, what is the ultimate source of a 'prediction'? The answer is multifactorial: knowledge of the current environmental context and the immediate past, allied to memory and lifetime experience of the way of the world, doubtless fine-tuned by evolutionary history too. There are, in consequence, numerous potential avenues for experimenters seeking to manipulate subjects' expectation, and examine the neural signals elicited by surprising, and less surprising visual stimuli. This review focuses upon the predictive physiology of mouse and monkey visual cortex, summarising and commenting on evidence to date, and placing it in the context of the broader field. It is concluded that predictive coding has a firm grounding in basic neuroscience and that, unsurprisingly, there remains much to learn.

Does Active Inference Provide a Comprehensive Theory of Placebo Analgesia?

Placebo interventions generate mismatches between expected pain and sensory signals from which pain states are inferred. Because we lack direct access to bodily states, we can only infer whether nociceptive activity indicates tissue damage or results from noise in sensory channels. Predictive processing models propose to make optimal inferences using prior knowledge given noisy sensory data. However, these models do not provide a satisfactory explanation of how pain relief expectations are translated into physiological manifestations of placebo responses. Furthermore, they do not account for individual differences in the ability to endogenously regulate nociceptive activity in predicting placebo analgesia. The brain not only passively integrates prior pain expectations with nociceptive activity to infer pain states (perceptual inference) but also initiates various types of actions to ensure that sensory data are consistent with prior pain expectations (active inference). We argue that depending on whether the brain interprets conflicting sensory data (prediction errors) as a signal to learn from or noise to be attenuated, the brain initiates opposing types of action to facilitate learning from sensory data or, conversely, to enhance the biasing influence of prior pain expectations on pain perception. Furthermore, we discuss the role of stress, anxiety, and unpredictability of pain in influencing the weighting of prior pain expectations and sensory data and how they relate to the individual ability to regulate nociceptive activity (endogenous pain modulation). Finally, we provide suggestions for future studies to test the implications of the active inference model of placebo analgesia.

Predictive processing of scenes and objects

Real-world visual input consists of rich scenes that are meaningfully composed of multiple objects which interact in complex, but predictable, ways. Despite this complexity, we recognize scenes, and objects within these scenes, from a brief glance at an image. In this review, we synthesize recent behavioral and neural findings that elucidate the mechanisms underlying this impressive ability. First, we review evidence that visual object and scene processing is partly implemented in parallel, allowing for a rapid initial gist of both objects and scenes concurrently. Next, we discuss recent evidence for bidirectional interactions between object and scene processing, with scene information modulating the visual processing of objects, and object information modulating the visual processing of scenes. Finally, we review evidence that objects also combine with each other to form object constellations, modulating the processing of individual objects within the object pathway. Altogether, these findings can be understood by conceptualizing object and scene perception as the outcome of a joint probabilistic inference, in which "best guesses" about objects act as priors for scene perception and vice versa, in order to concurrently optimize visual inference of objects and scenes.

Visual Representations: Insights from Neural Decoding

Patterns of brain activity contain meaningful information about the perceived world. Recent decades have welcomed a new era in neural analyses, with computational techniques from machine learning applied to neural data to decode information represented in the brain. In this article, we review how decoding approaches have advanced our understanding of visual representations and discuss efforts to characterize both the complexity and the behavioral relevance of these representations. We outline the current consensus regarding the spatiotemporal structure of visual representations and review recent findings that suggest that visual representations are at once robust to perturbations, yet sensitive to different mental states. Beyond representations of the physical world, recent decoding work has shone a light on how the brain instantiates internally generated states, for example, during imagery and prediction. Going forward, decoding has remarkable potential to assess the functional relevance of visual representations for human behavior, reveal how representations change across development and during aging, and uncover their presentation in various mental disorders.

Suppressive Control of Incentive Salience in Real-World Human Vision

Reward-related activity in the dopaminergic midbrain is thought to guide animal behavior, in part by boosting the perceptual and attentional processing of reward-predictive environmental stimuli. In line with this incentive salience hypothesis, studies of human visual search have shown that simple synthetic stimuli, such as lines, shapes, or Gabor patches, capture attention to their location when they are characterized by reward-associated visual features, such as color. In the real world, however, we commonly search for members of a category of visually heterogeneous objects, such as people, cars, or trees, where category examples do not share low-level features. Is attention captured to examples of a reward-associated real-world object category? Here, we have human participants search for targets in photographs of city and landscapes that contain task-irrelevant examples of a reward-associated category. We use the temporal precision of EEG machine learning and ERPs to show that these distractors acquire incentive salience and draw attention, but do not capture it. Instead, we find evidence of rapid, stimulus-triggered attentional suppression, such that the neural encoding of these objects is degraded relative to neutral objects. Humans appear able to suppress the incentive salience of reward-associated objects when they know these objects will be irrelevant, supporting the rapid deployment of attention to other objects that might be more useful. Incentive salience is thought to underlie key behaviors in eating disorders and addiction, among other conditions, and the kind of suppression identified here likely plays a role in mediating the attentional biases that emerge in these circumstances.Significance Statement Like other animals, humans are prone to notice and interact with environmental objects that have proven rewarding in earlier experience. However, it is common that such objects have no immediate strategic use and are therefore distracting. Do these reward-associated real-world objects capture our attention, despite our strategic efforts otherwise? Or are we able to strategically control the impulse to notice them? Here we use machine learning classification of human electrical brain activity to show that we can establish strategic control over the salience of naturalistic reward-associated objects. These objects draw our attention, but do not necessarily capture it, and this kind of control may play an important role in mediating conditions like eating disorder and addiction.

The effect of context congruency on fMRI repetition suppression for objects

The recognition of objects is strongly facilitated when they are presented in the context of other objects (Biederman, 1972). Such contexts facilitate perception and induce expectations of context-congruent objects (Trapp and Bar, 2015). The neural mechanisms underlying these facilitatory effects of context on object processing, however, are not yet fully understood. In the present study, we investigate how context-induced expectations affect subsequent object processing. We used functional magnetic resonance imaging and measured repetition suppression as a proxy for prediction error processing. Participants viewed pairs of alternating or repeated object images which were preceded by context-congruent, context-incongruent or neutral cues. We found a stronger repetition suppression in congruent as compared to incongruent or neutral cues in the object sensitive lateral occipital cortex. Interestingly, this stronger effect was driven by enhanced responses to alternating stimulus pairs in the congruent contexts, rather than by suppressed responses to repeated stimulus pairs, which emphasizes the contribution of surprise-related response enhancement for the context modulation on RS when expectations are violated. In addition, in the congruent condition, we discovered significant functional connectivity between object-responsive and frontal cortical regions, as well as between object-responsive regions and the fusiform gyrus. Our findings indicate that prediction errors, reflected in enhanced brain responses to violated contextual expectations, underlie the facilitating effect of context during object perception.

Intermodulation responses show integration of interacting bodies in a new whole

People are often seen among other people, relating to and interacting with one another. Recent studies suggest that socially relevant spatial relations between bodies, such as the face-to-face positioning, or facingness, change the visual representation of those bodies, relative to when the same items appear unrelated (e.g., back-to-back) or in isolation. The current study addresses the hypothesis that face-to-face bodies give rise to a new whole, an integrated representation of individual bodies in a new perceptual unit. Using frequency-tagging EEG, we targeted, as a measure of integration, an EEG correlate of the non-linear combination of the neural responses to each of two individual bodies presented either face-to-face as if interacting, or back-to-back. During EEG recording, participants (N = 32) viewed two bodies, either face-to-face or back-to-back, flickering at two different frequencies (F1 and F2), yielding two distinctive responses in the EEG signal. Spectral analysis examined the responses at the intermodulation frequencies (nF1±mF2), signaling integration of individual responses. An anterior intermodulation response was observed for face-to-face bodies, but not for back-to-back bodies, nor for face-to-face chairs and machines. These results show that interacting bodies are integrated into a representation that is more than the sum of its parts. This effect, specific to body dyads, may mark an early step in the transformation towards an integrated representation of a social event, from the visual representation of individual participants in that event.

Humans predict the forest, not the trees: statistical learning of spatiotemporal structure in visual scenes

The human brain is capable of using statistical regularities to predict future inputs. In the real world, such inputs typically comprise a collection of objects (e.g. a forest constitutes numerous trees). The present study aimed to investigate whether perceptual anticipation relies on lower-level or higher-level information. Specifically, we examined whether the human brain anticipates each object in a scene individually or anticipates the scene as a whole. To explore this issue, we first trained participants to associate co-occurring objects within fixed spatial arrangements. Meanwhile, participants implicitly learned temporal regularities between these displays. We then tested how spatial and temporal violations of the structure modulated behavior and neural activity in the visual system using fMRI. We found that participants only showed a behavioral advantage of temporal regularities when the displays conformed to their previously learned spatial structure, demonstrating that humans form configuration-specific temporal expectations instead of predicting individual objects. Similarly, we found suppression of neural responses for temporally expected compared with temporally unexpected objects in lateral occipital cortex only when the objects were embedded within expected configurations. Overall, our findings indicate that humans form expectations about object configurations, demonstrating the prioritization of higher-level over lower-level information in temporal expectation.

Independent spatiotemporal effects of spatial attention and background clutter on human object location representations

Spatial attention helps us to efficiently localize objects in cluttered environments. However, the processing stage at which spatial attention modulates object location representations remains unclear. Here we investigated this question identifying processing stages in time and space in an EEG and fMRI experiment respectively. As both object location representations and attentional effects have been shown to depend on the background on which objects appear, we included object background as an experimental factor. During the experiments, human participants viewed images of objects appearing in different locations on blank or cluttered backgrounds while either performing a task on fixation or on the periphery to direct their covert spatial attention away or towards the objects. We used multivariate classification to assess object location information. Consistent across the EEG and fMRI experiment, we show that spatial attention modulated location representations during late processing stages (>150 ms, in middle and high ventral visual stream areas) independent of background condition. Our results clarify the processing stage at which attention modulates object location representations in the ventral visual stream and show that attentional modulation is a cognitive process separate from recurrent processes related to the processing of objects on cluttered backgrounds.

The representational hierarchy in human and artificial visual systems in the presence of object-scene regularities

Human vision is still largely unexplained. Computer vision made impressive progress on this front, but it is still unclear to which extent artificial neural networks approximate human object vision at the behavioral and neural levels. Here, we investigated whether machine object vision mimics the representational hierarchy of human object vision with an experimental design that allows testing within-domain representations for animals and scenes, as well as across-domain representations reflecting their real-world contextual regularities such as animal-scene pairs that often co-occur in the visual environment. We found that DCNNs trained in object recognition acquire representations, in their late processing stage, that closely capture human conceptual judgements about the co-occurrence of animals and their typical scenes. Likewise, the DCNNs representational hierarchy shows surprising similarities with the representational transformations emerging in domain-specific ventrotemporal areas up to domain-general frontoparietal areas. Despite these remarkable similarities, the underlying information processing differs. The ability of neural networks to learn a human-like high-level conceptual representation of object-scene co-occurrence depends upon the amount of object-scene co-occurrence present in the image set thus highlighting the fundamental role of training history. Further, although mid/high-level DCNN layers represent the category division for animals and scenes as observed in VTC, its information content shows reduced domain-specific representational richness. To conclude, by testing within- and between-domain selectivity while manipulating contextual regularities we reveal unknown similarities and differences in the information processing strategies employed by human and artificial visual systems.

Subjective perception of objects depends on the interaction between the validity of context-based expectations and signal reliability

Predictive coding theories of visual perception assume that expectations based on prior knowledge modulate the processing of information. However, the underlying mechanisms remain debated. Some accounts propose that expectations enhance the perception of expected relative to unexpected stimuli while others assume the opposite. Recently, the opposing process theory suggested that enhanced perception of expected vs. unexpected stimuli may occur alternatively depending upon the reliability of the visual signal. When the signal is noisy, perception would be biassed toward what is expected since anything else may be too noisy to be resolved. When the signal is unambiguous, perception would be biassed toward what diverges from expectations and is more informative. Our study tested this hypothesis, using a perceptual matching task to investigate the influence of expectations on the perceived sharpness of objects in context. Participants saw two blurred images depicting the same object and had to adjust the blur level of one object to match the blur level of the other one. We manipulated the validity of expectations about objects by varying their scene context (congruent or incongruent context leading to valid or invalid expectations about the object). We also manipulated the reliability of the visual signal by varying the initial blur level of object pairs. Results showed that expectations validity differentially affected the perception of objects depending on signal reliability. Perception of validly expected objects was enhanced (sharpened) relative to unexpected objects when visual inputs were unreliable while this effect reversed to the benefit of unexpected objects when the signal was more reliable.

Understanding Human Object Vision: A Picture Is Worth a Thousand Representations

Objects are the core meaningful elements in our visual environment. Classic theories of object vision focus upon object recognition and are elegant and simple. Some of their proposals still stand, yet the simplicity is gone. Recent evolutions in behavioral paradigms, neuroscientific methods, and computational modeling have allowed vision scientists to uncover the complexity of the multidimensional representational space that underlies object vision. We review these findings and propose that the key to understanding this complexity is to relate object vision to the full repertoire of behavioral goals that underlie human behavior, running far beyond object recognition. There might be no such thing as core object recognition, and if it exists, then its importance is more limited than traditionally thought.

The best game in town: The reemergence of the language-of-thought hypothesis across the cognitive sciences

Mental representations remain the central posits of psychology after many decades of scrutiny. However, there is no consensus about the representational format(s) of biological cognition. This paper provides a survey of evidence from computational cognitive psychology, perceptual psychology, developmental psychology, comparative psychology, and social psychology, and concludes that one type of format that routinely crops up is the language-of-thought (LoT). We outline six core properties of LoTs: (i) discrete constituents; (ii) role-filler independence; (iii) predicate-argument structure; (iv) logical operators; (v) inferential promiscuity; and (vi) abstract content. These properties cluster together throughout cognitive science. Bayesian computational modeling, compositional features of object perception, complex infant and animal reasoning, and automatic, intuitive cognition in adults all implicate LoT-like structures. Instead of regarding LoT as a relic of the previous century, researchers in cognitive science and philosophy-of-mind must take seriously the explanatory breadth of LoT-based architectures. We grant that the mind may harbor many formats and architectures, including iconic and associative structures as well as deep-neural-network-like architectures. However, as computational/representational approaches to the mind continue to advance, classical compositional symbolic structures - that is, LoTs - only prove more flexible and well-supported over time.

Visual motion perception as online hierarchical inference

Identifying the structure of motion relations in the environment is critical for navigation, tracking, prediction, and pursuit. Yet, little is known about the mental and neural computations that allow the visual system to infer this structure online from a volatile stream of visual information. We propose online hierarchical Bayesian inference as a principled solution for how the brain might solve this complex perceptual task. We derive an online Expectation-Maximization algorithm that explains human percepts qualitatively and quantitatively for a diverse set of stimuli, covering classical psychophysics experiments, ambiguous motion scenes, and illusory motion displays. We thereby identify normative explanations for the origin of human motion structure perception and make testable predictions for future psychophysics experiments. The proposed online hierarchical inference model furthermore affords a neural network implementation which shares properties with motion-sensitive cortical areas and motivates targeted experiments to reveal the neural representations of latent structure.

Statistical learning of distractor co-occurrences facilitates visual search

Visual search is facilitated by knowledge of the relationship between the target and the distractors, including both where the target is likely to be among the distractors and how it differs from the distractors. Whether the statistical structure among distractors themselves, unrelated to target properties, facilitates search is less well understood. Here, we assessed the benefit of distractor structure using novel shapes whose relationship to each other was learned implicitly during visual search. Participants searched for target items in arrays of shapes that comprised either four pairs of co-occurring distractor shapes (structured scenes) or eight distractor shapes randomly partitioned into four pairs on each trial (unstructured scenes). Across five online experiments (N = 1,140), we found that after a period of search training, participants were more efficient when searching for targets in structured than unstructured scenes. This structure benefit emerged independently of whether the position of the shapes within each pair was fixed or variable and despite participants having no explicit knowledge of the structured pairs they had seen. These results show that implicitly learned co-occurrence statistics between distractor shapes increases search efficiency. Increased efficiency in the rejection of regularly co-occurring distractors may contribute to the efficiency of visual search in natural scenes, where such regularities are abundant.

Following Other People's Footsteps: A Contextual-Attraction Effect Induced by Biological Motion

Our visual system is bombarded with numerous social interactions that form intangible social bonds among people, as exemplified by synchronized walking in crowds. Here, we investigated whether these perceived social bonds implicitly intrude on visual perception and induce a contextual effect. Using multiple point-light walkers and a classical contextual paradigm, we tested 72 college-age adults across six experiments and found that the perceived direction of the central walker was attracted toward the direction of the surrounding walkers. The observed contextual-attraction effect occurred even when the surrounding walkers differed from the central walker in gender and walking speed but disappeared when they were asynchronously presented or replaced by inanimate motion. Strikingly, this contextual-attraction effect partially persisted in the context of local motion rather than static figures. These findings, in contrast to the typical contextual-repulsion effect, lend support for the distinctiveness of perceived social bonds on contextual modulation and suggest a specialized contextual mechanism tuned to social factors.

Semantic Scene-Object Consistency Modulates N300/400 EEG Components, but Does Not Automatically Facilitate Object Representations

During natural vision, objects rarely appear in isolation, but often within a semantically related scene context. Previous studies reported that semantic consistency between objects and scenes facilitates object perception and that scene-object consistency is reflected in changes in the N300 and N400 components in EEG recordings. Here, we investigate whether these N300/400 differences are indicative of changes in the cortical representation of objects. In two experiments, we recorded EEG signals, while participants viewed semantically consistent or inconsistent objects within a scene; in Experiment 1, these objects were task-irrelevant, while in Experiment 2, they were directly relevant for behavior. In both experiments, we found reliable and comparable N300/400 differences between consistent and inconsistent scene-object combinations. To probe the quality of object representations, we performed multivariate classification analyses, in which we decoded the category of the objects contained in the scene. In Experiment 1, in which the objects were not task-relevant, object category could be decoded from ~100 ms after the object presentation, but no difference in decoding performance was found between consistent and inconsistent objects. In contrast, when the objects were task-relevant in Experiment 2, we found enhanced decoding of semantically consistent, compared with semantically inconsistent, objects. These results show that differences in N300/400 components related to scene-object consistency do not index changes in cortical object representations but rather reflect a generic marker of semantic violations. Furthermore, our findings suggest that facilitatory effects between objects and scenes are task-dependent rather than automatic.

Encoding specificity instead of online integration of real-world spatial regularities for objects in working memory

Most objects show high degrees of spatial regularity (e.g. beach umbrellas appear above, not under, beach chairs). The spatial regularities of real-world objects benefit visual working memory (VWM), but the mechanisms behind this spatial regularity effect remain unclear. The "encoding specificity" hypothesis suggests that spatial regularity will enhance the visual encoding process but will not facilitate the integration of information online during VWM maintenance. The "perception-alike" hypothesis suggests that spatial regularity will function in both visual encoding and online integration during VWM maintenance. We investigated whether VWM integrates sequentially presented real-world objects by focusing on the existence of the spatial regularity effect. Throughout five experiments, we manipulated the presentation (simultaneous vs. sequential) and regularity (with vs. without regularity) of memory arrays among pairs of real-world objects. The spatial regularity of memory objects presented simultaneously, but not sequentially, improved VWM performance. We also examined whether memory load, verbal suppression and masking, and memory array duration hindered the spatial regularity effect in sequential presentation. We found a stable absence of the spatial regularity effect, suggesting that the participants were unable to integrate real-world objects based on spatial regularities online. Our results support the encoding specificity hypothesis, wherein the spatial regularity of real-world objects can enhance the efficiency of VWM encoding, but VWM cannot exploit spatial regularity to help organize sampled sequential information into meaningful integrations.

Behavioral and neural markers of visual configural processing in social scene perception

Research on face perception has revealed highly specialized visual mechanisms such as configural processing, and provided markers of interindividual differences -including disease risks and alterations- in visuo-perceptual abilities that traffic in social cognition. Is face perception unique in degree or kind of mechanisms, and in its relevance for social cognition? Combining functional MRI and behavioral methods, we address the processing of an uncharted class of socially relevant stimuli: minimal social scenes involving configurations of two bodies spatially close and face-to-face as if interacting (hereafter, facing dyads). We report category-specific activity for facing (vs. non-facing) dyads in visual cortex. That activity shows face-like signatures of configural processing -i.e., stronger response to facing (vs. non-facing) dyads, and greater susceptibility to stimulus inversion for facing (vs. non-facing) dyads-, and is predicted by performance-based measures of configural processing in visual perception of body dyads. Moreover, we observe that the individual performance in body-dyad perception is reliable, stable-over-time and correlated with the individual social sensitivity, coarsely captured by the Autism-Spectrum Quotient. Further analyses clarify the relationship between single-body and body-dyad perception. We propose that facing dyads are processed through highly specialized mechanisms -and brain areas-, analogously to other biologically and socially relevant stimuli such as faces. Like face perception, facing-dyad perception can reveal basic (visual) processes that lay the foundations for understanding others, their relationships and interactions.

Selective neural coding of object, feature, and geometry spatial cues in humans

Orienting in space requires the processing of visual spatial cues. The dominant hypothesis about the brain structures mediating the coding of spatial cues stipulates the existence of a hippocampal-dependent system for the representation of geometry and a striatal-dependent system for the representation of landmarks. However, this dual-system hypothesis is based on paradigms that presented spatial cues conveying either conflicting or ambiguous spatial information and that used the term landmark to refer to both discrete three-dimensional objects and wall features. Here, we test the hypothesis of complex activation patterns in the hippocampus and the striatum during visual coding. We also postulate that object-based and feature-based navigation are not equivalent instances of landmark-based navigation. We examined how the neural networks associated with geometry-, object-, and feature-based spatial navigation compared with a control condition in a two-choice behavioral paradigm using fMRI. We showed that the hippocampus was involved in all three types of cue-based navigation, whereas the striatum was more strongly recruited in the presence of geometric cues than object or feature cues. We also found that unique, specific neural signatures were associated with each spatial cue. Object-based navigation elicited a widespread pattern of activity in temporal and occipital regions relative to feature-based navigation. These findings extend the current view of a dual, juxtaposed hippocampal-striatal system for visual spatial coding in humans. They also provide novel insights into the neural networks mediating object versus feature spatial coding, suggesting a need to distinguish these two types of landmarks in the context of human navigation.

Perception of semantic relations in scenes: A registered report study of attention hold

To what extent the semantic relations present in scenes guide spatial attention automatically remains a matter of debate. Considering that spatial attention can be understood as a sequence of shifts, engagements, and disengagements, semantic relations might affect each stage of this process differently. Therefore, we investigated whether objects that violate semantic rules engage attention for longer than objects that are expected in a given context. The experiment involved a central presentation of a distractor scene that contained a semantically congruent or incongruent object, and a peripheral presentation of a small target letter. We found that incongruent scenes did not delay responses to the peripheral target, which indicates that they did not hold attention for longer than congruent scenes. Therefore, by showing that violations of semantic relations do not engage attention automatically, our study contributes to a better understanding of how attention operates in naturalistic settings.

Synchronised neural signature of creative mental imagery in reality and augmented reality

Creativity, transforming imaginative thinking into reality, is a mental imagery simulation in essence. It can be incorporeal, concerns sophisticated and/or substantial thinking, and involves objects. In the present study, a mental imagery task consisting of creating a scene using familiar (FA) or abstract (AB) physical or virtual objects in real (RMI) and augmented reality (VMI) environments, and an execution task involving effectively creating a scene in augmented reality (VE), were utilised. The beta and gamma neural oscillations of healthy participants were recorded via a 32 channel wireless 10/20 international EGG system. In real and augmented environments and for both the mental imagery and execution tasks, the participants displayed a similar cortico-cortical neural signature essentially based on synchronous vs asynchronous beta and gamma oscillatory activities between anterior (i.e. frontal) and posterior (i.e. parietal, occipito-parietal and occipito-temporal) areas bilaterally. The findings revealed a transient synchronised neural architecture that appears to be consistent with the hypothesis according to which, creativity, because of its inherent complexity, cannot be confined to a single brain area but engages various interconnected networks.

The spatiotemporal neural dynamics of object location representations in the human brain

To interact with objects in complex environments, we must know what they are and where they are in spite of challenging viewing conditions. Here, we investigated where, how and when representations of object location and category emerge in the human brain when objects appear on cluttered natural scene images using a combination of functional magnetic resonance imaging, electroencephalography and computational models. We found location representations to emerge along the ventral visual stream towards lateral occipital complex, mirrored by gradual emergence in deep neural networks. Time-resolved analysis suggested that computing object location representations involves recurrent processing in high-level visual cortex. Object category representations also emerged gradually along the ventral visual stream, with evidence for recurrent computations. These results resolve the spatiotemporal dynamics of the ventral visual stream that give rise to representations of where and what objects are present in a scene under challenging viewing conditions.

The importance of an exaggerated attention bottleneck for understanding psychopathy

The psychopath has long captured the imagination. A name such as Ted Bundy evokes a morbid curiosity. The crimes committed by Bundy are so cruel that it is hard to imagine how someone could do such things. In this review we discuss evidence that exaggeration in an attention bottleneck is one mechanism that makes it possible for psychopathic individuals to be adept at focusing on a single stimulus feature or goal but struggle to process multiple streams of information simultaneously. This exaggeration may partly explain the behavioral, affective, and social deficits that are apparent among psychopathic individuals. Further research on this attentional mechanism may promote a science that adequately captures the complexity of psychopathic behavior and offers new avenues for intervention.

Semantic object-scene inconsistencies affect eye movements, but not in the way predicted by contextualized meaning maps

Semantic information is important in eye movement control. An important semantic influence on gaze guidance relates to object-scene relationships: objects that are semantically inconsistent with the scene attract more fixations than consistent objects. One interpretation of this effect is that fixations are driven toward inconsistent objects because they are semantically more informative. We tested this explanation using contextualized meaning maps, a method that is based on crowd-sourced ratings to quantify the spatial distribution of context-sensitive “meaning” in images. In Experiment 1, we compared gaze data and contextualized meaning maps for images, in which objects-scene consistency was manipulated. Observers fixated more on inconsistent versus consistent objects. However, contextualized meaning maps did not assign higher meaning to image regions that contained semantic inconsistencies. In Experiment 2, a large number of raters evaluated image-regions, which were deliberately selected for their content and expected meaningfulness. The results suggest that the same scene locations were experienced as slightly less meaningful when they contained inconsistent compared to consistent objects. In summary, we demonstrated that — in the context of our rating task — semantically inconsistent objects are experienced as less meaningful than their consistent counterparts and that contextualized meaning maps do not capture prototypical influences of image meaning on gaze guidance.

Scene Context Impairs Perception of Semantically Congruent Objects

Visual scene context is well-known to facilitate the recognition of scene-congruent objects. Interestingly, however, according to predictive-processing accounts of brain function, scene congruency may lead to reduced (rather than enhanced) processing of congruent objects, compared with incongruent ones, because congruent objects elicit reduced prediction-error responses. We tested this counterintuitive hypothesis in two online behavioral experiments with human participants (N = 300). We found clear evidence for impaired perception of congruent objects, both in a change-detection task measuring response times and in a bias-free object-discrimination task measuring accuracy. Congruency costs were related to independent subjective congruency ratings. Finally, we show that the reported effects cannot be explained by low-level stimulus confounds, response biases, or top-down strategy. These results provide convincing evidence for perceptual congruency costs during scene viewing, in line with predictive-processing theory.

Spatial and Temporal Context Jointly Modulate the Sensory Response within the Ventral Visual Stream

Both spatial and temporal context play an important role in visual perception and behavior. Humans can extract statistical regularities from both forms of context to help process the present and to construct expectations about the future. Numerous studies have found reduced neural responses to expected stimuli compared with unexpected stimuli, for both spatial and temporal regularities. However, it is largely unclear whether and how these forms of context interact. In the current fMRI study, 33 human volunteers were exposed to pairs of object stimuli that could be expected or surprising in terms of their spatial and temporal context. We found reliable independent contributions of both spatial and temporal context in modulating the neural response. Specifically, neural responses to stimuli in expected compared with unexpected contexts were suppressed throughout the ventral visual stream. These results suggest that both spatial and temporal context may aid sensory processing in a similar fashion, providing evidence on how different types of context jointly modulate perceptual processing.

Dissociated face- and word-selective intracerebral responses in the human ventral occipito-temporal cortex

The extent to which faces and written words share neural circuitry in the human brain is actively debated. Here, we compare face-selective and word-selective responses in a large group of patients (N = 37) implanted with intracerebral electrodes in the ventral occipito-temporal cortex (VOTC). Both face-selective (i.e., significantly different responses to faces vs. non-face visual objects) and word-selective (i.e., significantly different responses to words vs. pseudofonts) neural activity is isolated with frequency-tagging. Critically, this sensitive approach allows to objectively quantify category-selective neural responses and disentangle them from general visual responses. About 70% of significant electrode contacts show either face-selectivity or word-selectivity only, with the expected right and left hemispheric dominance, respectively. Spatial dissociations are also found within core regions of face and word processing, with a medio-lateral dissociation in the fusiform gyrus (FG) and surrounding sulci, respectively. In the 30% of overlapping face- and word-selective contacts across the VOTC or in the FG and surrounding sulci, between-category-selective amplitudes (faces vs. words) show no-to-weak correlations, despite strong correlations in both the within-category-selective amplitudes (face-face, word-word) and the general visual responses to words and faces. Overall, these observations support the view that category-selective circuitry for faces and written words is largely dissociated in the human adult VOTC.

Visuospatial coding as ubiquitous scaffolding for human cognition

For more than 100 years we have known that the visual field is mapped onto the surface of visual cortex, imposing an inherently spatial reference frame on visual information processing. Recent studies highlight visuospatial coding not only throughout visual cortex, but also brain areas not typically considered visual. Such widespread access to visuospatial coding raises important questions about its role in wider cognitive functioning. Here, we synthesise these recent developments and propose that visuospatial coding scaffolds human cognition by providing a reference frame through which neural computations interface with environmental statistics and task demands via perception-action loops.

Reward Rapidly Enhances Visual Perception

Rewards exert a deep influence on our cognition and behavior. Here, we used a paradigm in which reward information was provided at either encoding or retrieval of a brief, masked stimulus to show that reward can also rapidly modulate perceptual encoding of visual information. Experiment 1 (n = 30 adults) showed that participants' response accuracy was enhanced when a to-be-encoded grating signaled high reward relative to low reward, but only when the grating was presented very briefly and participants reported that they were not consciously aware of it. Experiment 2 (n = 29 adults) showed that there was no difference in participants' response accuracy when reward information was instead provided at the stage of retrieval, ruling out an explanation of the reward-modulation effect in terms of differences in motivated retrieval. Taken together, our findings provide behavioral evidence consistent with a rapid reward modulation of visual perception, which may not require consciousness.

Parts and Wholes in Scene Processing

During natural vision, our brains are constantly exposed to complex, but regularly structured environments. Real-world scenes are defined by typical part-whole relationships, where the meaning of the whole scene emerges from configurations of localized information present in individual parts of the scene. Such typical part-whole relationships suggest that information from individual scene parts is not processed independently, but that there are mutual influences between the parts and the whole during scene analysis. Here, we review recent research that used a straightforward, but effective approach to study such mutual influences: By dissecting scenes into multiple arbitrary pieces, these studies provide new insights into how the processing of whole scenes is shaped by their constituent parts and, conversely, how the processing of individual parts is determined by their role within the whole scene. We highlight three facets of this research: First, we discuss studies demonstrating that the spatial configuration of multiple scene parts has a profound impact on the neural processing of the whole scene. Second, we review work showing that cortical responses to individual scene parts are shaped by the context in which these parts typically appear within the environment. Third, we discuss studies demonstrating that missing scene parts are interpolated from the surrounding scene context. Bridging these findings, we argue that efficient scene processing relies on an active use of the scene's part-whole structure, where the visual brain matches scene inputs with internal models of what the world should look like.

The secret life of predictive brains: what's spontaneous activity for?

Brains at rest generate dynamical activity that is highly structured in space and time. We suggest that spontaneous activity, as in rest or dreaming, underlies top-down dynamics of generative models. During active tasks, generative models provide top-down predictive signals for perception, cognition, and action. When the brain is at rest and stimuli are weak or absent, top-down dynamics optimize the generative models for future interactions by maximizing the entropy of explanations and minimizing model complexity. Spontaneous fluctuations of correlated activity within and across brain regions may reflect transitions between 'generic priors' of the generative model: low dimensional latent variables and connectivity patterns of the most common perceptual, motor, cognitive, and interoceptive states. Even at rest, brains are proactive and predictive.