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Many of the objects we encounter in our everyday environments would be hard to recognize without any expectations about these objects. For example, a distant silhouette may be perceived as a car because we expect objects of that size, positioned on a road, to be cars. Reflecting the influence of such expectations on visual processing, neuroimaging studies have shown that when objects are poorly visible, expectations derived from scene context facilitate the representations of these objects in visual cortex from around 300 ms after scene onset. The current magnetoencephalography (MEG) study tested whether this facilitation occurs independently of attention and task relevance. Participants viewed degraded objects alone or within scene context while they either attended the scenes (attended condition) or the fixation cross (unattended condition), also temporally directing attention away from the scenes. Results showed that at 300 ms after stimulus onset, multivariate classifiers trained to distinguish clearly visible animate vs inanimate objects generalized to distinguish degraded objects in scenes better than degraded objects alone, despite the added clutter of the scene background. Attention also modulated object representations at this latency, with better category decoding in the attended than the unattended condition. The modulatory effects of context and attention were independent of each other. Finally, data from the current study and a previous study were combined (N = 51) to provide a more detailed temporal characterization of contextual facilitation. These results extend previous work by showing that facilitatory scene-object interactions are independent of the specific task performed on the visual input., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Real-world scenes consist of objects, defined by local information, and scene background, defined by global information. Although objects and scenes are processed in separate pathways in visual cortex, their processing interacts. Specifically, previous studies have shown that scene context makes blurry objects look sharper, an effect that can be observed as a sharpening of object representations in visual cortex from around 300 ms after stimulus onset. Here, we use MEG to show that objects can also sharpen scene representations, with the same temporal profile. Photographs of indoor (closed) and outdoor (open) scenes were blurred such that they were difficult to categorize on their own but easily disambiguated by the inclusion of an object. Classifiers were trained to distinguish MEG response patterns to intact indoor and outdoor scenes, presented in an independent run, and tested on degraded scenes in the main experiment. Results revealed better decoding of scenes with objects than scenes alone and objects alone from 300 ms after stimulus onset. This effect was strongest over left posterior sensors. These findings show that the influence of objects on scene representations occurs at similar latencies as the influence of scenes on object representations, in line with a common predictive processing mechanism., (© The Author(s) 2023. Published by Oxford University Press.)

Everyday experiences are dynamic, driving fluctuations across simultaneous cognitive processes. A key challenge in the study of naturalistic cognition is to disentangle the complexity of these dynamic processes, without altering the natural experience itself. Retrospective behavioral sampling (RBS) is a novel approach to model the cognitive fluctuations corresponding to the time-course of naturalistic stimulation, across a variety of cognitive dimensions. We tested the effectiveness and reliability of RBS in a web-based experiment, in which 53 participants viewed short movies and listened to a story, followed by retrospective reporting. Participants recalled their experience of 55 discrete events from the stimuli, rating their quality of memory, magnitude of surprise, intensity of negative and positive emotions, perceived importance, reflectivity state, and mental time travel. In addition, a subset of the original cohort re-rated their memory of events in a follow-up questionnaire. Results show highly replicable fluctuation patterns across distinct cognitive dimensions, thereby revealing a stimulus-driven experience that is substantially shared among individuals. Remarkably, memory ratings more than a week after stimulation resulted in an almost identical time-course of memorability as measured immediately following stimulation. In addition, idiosyncratic response patterns were preserved across different stimuli, indicating that RBS characterizes individual differences that are stimulus invariant. The current findings highlight the potential of RBS as a powerful tool for measuring dynamic processes of naturalistic cognition. We discuss the promising approach of matching RBS fluctuations with dynamic processes measured via other testing modalities, such as neuroimaging, to study the neural manifestations of naturalistic cognitive processing., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Brandman, Malach and Simony.)

The default mode network (DMN) is a group of high-order brain regions recently implicated in processing external naturalistic events, yet it remains unclear what cognitive function it serves. Here we identified the cognitive states predictive of DMN fMRI coactivation. Particularly, we developed a state-fluctuation pattern analysis, matching network coactivations across a short movie with retrospective behavioral sampling of movie events. Network coactivation was selectively correlated with the state of surprise across movie events, compared to all other cognitive states (e.g. emotion, vividness). The effect was exhibited in the DMN, but not dorsal attention or visual networks. Furthermore, surprise was found to mediate DMN coactivations with hippocampus and nucleus accumbens. These unexpected findings point to the DMN as a major hub in high-level prediction-error representations.

Sounds (e.g., barking) help us to visually identify objects (e.g., a dog) that are distant or ambiguous. While neuroimaging studies have revealed neuroanatomical sites of audiovisual interactions, little is known about the time course by which sounds facilitate visual object processing. Here we used magnetoencephalography to reveal the time course of the facilitatory influence of natural sounds (e.g., barking) on visual object processing and compared this to the facilitatory influence of spoken words (e.g., "dog"). Participants viewed images of blurred objects preceded by a task-irrelevant natural sound, a spoken word, or uninformative noise. A classifier was trained to discriminate multivariate sensor patterns evoked by animate and inanimate intact objects with no sounds, presented in a separate experiment, and tested on sensor patterns evoked by the blurred objects in the 3 auditory conditions. Results revealed that both sounds and words, relative to uninformative noise, significantly facilitated visual object category decoding between 300-500 ms after visual onset. We found no evidence for earlier facilitation by sounds than by words. These findings provide evidence for a semantic route of facilitation by both natural sounds and spoken words, whereby the auditory input first activates semantic object representations, which then modulate the visual processing of objects., (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

We internally represent the structure of our surroundings even when there is little layout information available in the visual image, such as when walking through fog or darkness. One way in which we disambiguate such scenes is through object cues; for example, seeing a boat supports the inference that the foggy scene is a lake. Recent studies have investigated the neural mechanisms by which object and scene processing interact to support object perception. The current study examines the reverse interaction by which objects facilitate the neural representation of scene layout. Photographs of indoor (closed) and outdoor (open) real-world scenes were blurred such that they were difficult to categorize on their own but easily disambiguated by the inclusion of an object. fMRI decoding was used to measure scene representations in scene-selective parahippocampal place area (PPA) and occipital place area (OPA). Classifiers were trained to distinguish response patterns to fully visible indoor and outdoor scenes, presented in an independent experiment. Testing these classifiers on blurred scenes revealed a strong improvement in classification in left PPA and OPA when objects were present, despite the reduced low-level visual feature overlap with the training set in this condition. These findings were specific to left PPA/OPA, with no evidence for object-driven facilitation in right PPA/OPA, object-selective areas, and early visual cortex. These findings demonstrate separate roles for left and right scene-selective cortex in scene representation, whereby left PPA/OPA represents inferred scene layout, influenced by contextual object cues, and right PPA/OPA represents a scene's visual features.

Scenes strongly facilitate object recognition, such as when we make out the shape of a distant boat on the water. Yet, although known to interact in perception, neuroimaging research has primarily provided evidence for separate scene- and object-selective cortical pathways. This raises the question of how these pathways interact to support context-based perception. Here we used a novel approach in human fMRI and MEG studies to reveal supra-additive scene-object interactions. Participants (men and women) viewed degraded objects that were hard to recognize when presented in isolation but easy to recognize within their original scene context, in which no other associated objects were present. fMRI decoding showed that the multivariate representation of the objects' category (animate/inanimate) in object-selective cortex was strongly enhanced by the presence of scene context, even though the scenes alone did not evoke category-selective response patterns. This effect in object-selective cortex was correlated with concurrent activity in scene-selective regions. MEG decoding results revealed that scene-based facilitation of object processing peaked at 320 ms after stimulus onset, 100 ms later than peak decoding of intact objects. Together, results suggest that expectations derived from scene information, processed in scene-selective cortex, feed back to shape object representations in visual cortex. These findings characterize, in space and time, functional interactions between scene- and object-processing pathways. SIGNIFICANCE STATEMENT Although scenes and objects are known to contextually interact in visual perception, the study of high-level vision has mostly focused on the dissociation between their selective neural pathways. The current findings are the first to reveal direct facilitation of object recognition and neural representation by scene background, even in the absence of contextually associated objects. Using a multivariate approach to both fMRI and MEG, we characterize the functional neuroanatomy and neural dynamics of such scene-based object facilitation. Finally, the correlation of this effect with scene-selective activity suggests that, although functionally distinct, scene and object processing pathways do interact at a perceptual level to fill in for insufficient visual detail., (Copyright © 2017 the authors 0270-6474/17/377700-11$15.00/0.)

Behavioral studies suggested that bodies are represented as wholes rather than in a part-based manner. However, neural selectivity for body stimuli is found for both whole bodies and body parts. It is therefore undetermined whether the neural representation of bodies is configural or part-based. We used functional MRI to test the role of first-order configuration on body representation in the human occipital-temporal cortex by comparing the response to a whole body versus the sum of its parts. Results show that body-selective areas, whether defined by selectivity to headless bodies or body parts, preferred whole bodies over their sum of parts and successfully decoded body configuration. This configural representation was specific to body stimuli and not found for faces. In contrast, general object areas showed no preference for wholes over parts and decoded the configuration of both bodies and faces. Finally, whereas effects of inversion on configural face representation were specific to face-selective mechanisms, effects of body inversion were not unique to body-selective mechanisms. We conclude that the neural representation of body parts is strengthened by their arrangement into an intact body, thereby demonstrating a central role of first-order configuration in the neural representation of bodies in their category-selective areas., (© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Numerous studies have attributed the face inversion effect (FIE) to configural processing of internal facial features in upright but not inverted faces. Recent findings suggest that face mechanisms can be activated by faceless stimuli presented in the context of a body. Here we asked whether faceless stimuli with or without body context may induce an inversion effect as large as the FIE. In Study 1 participants performed a sequential matching task for upright and inverted faces, faceless heads with full, minimal or no body context, headless bodies and bodies viewed from the back. Results show inversion effects as large as the FIE for faceless heads with full or minimal body context, but not for faceless heads without body context, headless bodies or bodies viewed from the back. These findings remarkably show that in contrast to the well-established configural explanation for the FIE, the FIE does not necessarily depend on the processing of internal facial features, but can be also triggered for faceless stimuli presented in body context. In Study 2 participants rated the extent to which they detected a face in stimuli presented with or without faces briefly followed by a mask. We found that faceless stimuli that generated a large inversion effect were rated higher for the existence of a face than faceless stimuli that generated small or no inversion effects. These findings further suggest that the FIE can be generated by a contextually induced face percept at the face detection stage rather than the face identification stage., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Neural selectivity to specific object categories has been demonstrated in extrastriate cortex with both functional MRI [1-3] and event-related potential (ERP) [4, 5]. Here we tested for a causal relationship between the activation of category-selective areas and ERP to their preferred categories. Electroencephalogram (EEG) was recorded while participants observed faces and headless bodies. Concurrently with EEG recording, we delivered two pulses of transcranial magnetic stimulation (TMS) over the right occipital face area (OFA) or extrastriate body area (EBA) at 60 and 100 ms after stimulus onset. Results showed a clear dissociation between the stimulated site and the stimulus category on ERP modulation: stimulation of the OFA significantly increased the N1 amplitude to faces but not to bodies, whereas stimulation of the EBA significantly increased the N1 amplitude to bodies but not to faces. These findings provide the first evidence for a specific and causal link between activity in category-selective networks and scalp-recorded ERP to their preferred categories. This result also demonstrates that the face and body N1 reflects several nonoverlapping neural sources, rather than changes in face-selective mechanisms alone. Lastly, because early stimulation (60-100 ms) affected selectivity of a later ERP component (150-200 ms), the results could imply a feed-forward connection between occipital and temporal category-selective areas., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Recognition of faces is better when faces are presented in the left than right-visual-field. Furthermore, this perceptual asymmetry is a stable individual characteristic. Although it has been commonly assumed that the right hemispheric dominance for face processing underlies this left-visual-field superiority in face recognition, this neural-behavioral association has never been directly demonstrated. Here we applied functional MRI (fMRI) to measure the magnitude of the asymmetric response to faces for each subject. To determine whether the asymmetric neural response to faces is stable across sessions, subjects returned for a second fMRI session. In addition, subjects performed a behavioral experiment outside the scanner where they had to recognize centrally presented chimeric faces, which presented different identities in the right- and left-visual-field. This task yielded a measure of the magnitude of the left-visual-field bias for each subject. Our findings show that the magnitude of the asymmetry of the face-selective area in the fusiform gyrus (FFA) is highly consistent for each individual across scans. We then show that the behavioral left-visual-field asymmetry, measured outside the scanner, was strongly and specifically correlated with the asymmetry of the FFA across subjects, but not with other face-specific or nearby object-general regions. Our findings provide the first empirical evidence for the prevalent idea that perceptual asymmetries in face recognition are associated with the well-known hemispheric asymmetry for faces. We conclude that the FFA asymmetry is a highly stable individual characteristic that underlies the well-established left-visual-field superiority for face recognition.