Preparatory attention incorporates contextual expectations

Visual periodicity reveals distinct attentional signatures for face and non-face categories

Observers can selectively deploy attention to regions of space, moments in time, specific visual features, individual objects, and even specific high-level categories-for example, when keeping an eye out for dogs while jogging. Here, we exploited visual periodicity to examine how category-based attention differentially modulates selective neural processing of face and non-face categories. We combined electroencephalography with a novel frequency-tagging paradigm capable of capturing selective neural responses for multiple visual categories contained within the same rapid image stream (faces/birds in Exp 1; houses/birds in Exp 2). We found that the pattern of attentional enhancement and suppression for face-selective processing is unique compared to other object categories: Where attending to non-face objects strongly enhances their selective neural signals during a later stage of processing (300-500 ms), attentional enhancement of face-selective processing is both earlier and comparatively more modest. Moreover, only the selective neural response for faces appears to be actively suppressed by attending towards an alternate visual category. These results underscore the special status that faces hold within the human visual system, and highlight the utility of visual periodicity as a powerful tool for indexing selective neural processing of multiple visual categories contained within the same image sequence.

Learned associations serve as target proxies during difficult but not easy visual search

The target template contains information in memory that is used to guide attention during visual search and is typically thought of as containing features of the actual target object. However, when targets are hard to find, it is advantageous to use other information in the visual environment that is predictive of the target's location to help guide attention. The purpose of these studies was to test if newly learned associations between face and scene category images lead observers to use scene information as a proxy for the face target. Our results showed that scene information was used as a proxy for the target to guide attention but only when the target face was difficult to discriminate from the distractor face; when the faces were easy to distinguish, attention was no longer guided by the scene unless the scene was presented earlier. The results suggest that attention is flexibly guided by both target features as well as features of objects that are predictive of the target location. The degree to which each contributes to guiding attention depends on the efficiency with which that information can be used to decode the location of the target in the current moment. The results contribute to the view that attentional guidance is highly flexible in its use of information to rapidly locate the target.

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.

Scene context automatically drives predictions of object transformations

As our viewpoint changes, the whole scene around us rotates coherently. This allows us to predict how one part of a scene (e.g., an object) will change by observing other parts (e.g., the scene background). While human object perception is known to be strongly context-dependent, previous research has largely focused on how scene context can disambiguate fixed object properties, such as identity (e.g., a car is easier to recognize on a road than on a beach). It remains an open question whether object representations are updated dynamically based on the surrounding scene context, for example across changes in viewpoint. Here, we tested whether human observers dynamically and automatically predict the appearance of objects based on the orientation of the background scene. In three behavioral experiments (N = 152), we temporarily occluded objects within scenes that rotated. Upon the objects' reappearance, participants had to perform a perceptual discrimination task, which did not require taking the scene rotation into account. Performance on this orthogonal task strongly depended on whether objects reappeared rotated coherently with the surrounding scene or not. This effect persisted even when a majority of trials violated this real-world contingency between scene and object, showcasing the automaticity of these scene-based predictions. These findings indicate that contextual information plays an important role in predicting object transformations in structured real-world environments.

Testing cognitive theories with multivariate pattern analysis of neuroimaging data

Multivariate pattern analysis (MVPA) has emerged as a powerful method for the analysis of functional magnetic resonance imaging, electroencephalography and magnetoencephalography data. The new approaches to experimental design and hypothesis testing afforded by MVPA have made it possible to address theories that describe cognition at the functional level. Here we review a selection of studies that have used MVPA to test cognitive theories from a range of domains, including perception, attention, memory, navigation, emotion, social cognition and motor control. This broad view reveals properties of MVPA that make it suitable for understanding the 'how' of human cognition, such as the ability to test predictions expressed at the item or event level. It also reveals limitations and points to future directions.

Predictive neural representations of naturalistic dynamic input

Adaptive behavior such as social interaction requires our brain to predict unfolding external dynamics. While theories assume such dynamic prediction, empirical evidence is limited to static snapshots and indirect consequences of predictions. We present a dynamic extension to representational similarity analysis that uses temporally variable models to capture neural representations of unfolding events. We applied this approach to source-reconstructed magnetoencephalography (MEG) data of healthy human subjects and demonstrate both lagged and predictive neural representations of observed actions. Predictive representations exhibit a hierarchical pattern, such that high-level abstract stimulus features are predicted earlier in time, while low-level visual features are predicted closer in time to the actual sensory input. By quantifying the temporal forecast window of the brain, this approach allows investigating predictive processing of our dynamic world. It can be applied to other naturalistic stimuli (e.g., film, soundscapes, music, motor planning/execution, social interaction) and any biosignal with high temporal resolution.

Preparatory attention to visual features primarily relies on non-sensory representation

Prior knowledge of behaviorally relevant information promotes preparatory attention before the appearance of stimuli. A key question is how our brain represents the attended information during preparation. A sensory template hypothesis assumes that preparatory signals evoke neural activity patterns that resembled the perception of the attended stimuli, whereas a non-sensory, abstract template hypothesis assumes that preparatory signals reflect the abstraction of attended stimuli. To test these hypotheses, we used fMRI and multivariate analysis to characterize neural activity patterns when human participants were prepared to attend a feature and then select it from a compound stimulus. In an fMRI experiment using basic visual feature (motion direction), we observed reliable decoding of the to-be-attended feature from the preparatory activity in both visual and frontoparietal areas. However, while the neural patterns constructed by a single feature from a baseline task generalized to the activity patterns during stimulus selection, they could not generalize to the activity patterns during preparation. Our findings thus suggest that neural signals during attentional preparation are predominantly non-sensory in nature that may reflect an abstraction of the attended feature. Such a representation could provide efficient and stable guidance of attention.

Visual cognition: A new perspective on mental rotation

Manipulating an object in one's mind has long been thought to mirror physically manipulating that object in allocentric three-dimensional space. A new study revises and clarifies this foundational assumption, identifying a previously unknown role for the observer's point-of-view.

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.