Attention modulates human visual responses to objects by tuning sharpening.

Visual stimuli compete with each other for cortical processing and attention biases this competition in favor of the attended stimulus. How does the relationship between the stimuli affect the strength of this attentional bias? Here, we used functional MRI to explore the effect of target-distractor similarity in neural representation on attentional modulation in the human visual cortex using univariate and multivariate pattern analyses. Using stimuli from four object categories (human bodies, cats, cars, and houses), we investigated attentional effects in the primary visual area V1, the object-selective regions LO and pFs, the body-selective region EBA, and the scene-selective region PPA.

The transformation of sensory to perceptual braille letter representations in the visually deprived brain.

Experience-based plasticity of the human cortex mediates the influence of individual experience on cognition and behavior. The complete loss of a sensory modality is among the most extreme such experiences. Investigating such a selective, yet extreme change in experience allows for the characterization of experience-based plasticity at its boundaries.

Touch to text: Spatiotemporal evolution of braille letter representations in blind readers.

Visual deprivation does not silence the visual cortex, which is responsive to auditory, tactile, and other nonvisual tasks in blind persons. However, the underlying functional dynamics of the neural networks mediating such crossmodal responses remain unclear. Here, using braille reading as a model framework to investigate these networks, we presented sighted (N=13) and blind (N=12) readers with individual visual print and tactile braille alphabetic letters, respectively, during MEG recording.

Modeling short visual events through the BOLD moments video fMRI dataset and metadata.

Studying the neural basis of human dynamic visual perception requires extensive experimental data to evaluate the large swathes of functionally diverse brain neural networks driven by perceiving visual events. Here, we introduce the BOLD Moments Dataset (BMD), a repository of whole-brain fMRI responses to over 1000 short (3 s) naturalistic video clips of visual events across ten human subjects. We use the videos' extensive metadata to show how the brain represents word- and sentence-level descriptions of visual events and identify correlates of video memorability scores extending into the parietal cortex.

Laminar dissociation of feedforward and feedback in high-level ventral visual cortex during imagery and perception.

Visual imagery and perception share neural machinery but rely on different information flow. While perception is driven by the integration of sensory feedforward and internally generated feedback information, imagery relies on feedback only. This suggests that although imagery and perception may activate overlapping brain regions, they do so in informationally distinctive ways.

Visual features are processed before navigational affordances in the human brain.

To navigate through their immediate environment humans process scene information rapidly. How does the cascade of neural processing elicited by scene viewing to facilitate navigational planning unfold over time? To investigate, we recorded human brain responses to visual scenes with electroencephalography and related those to computational models that operationalize three aspects of scene processing (2D, 3D, and semantic information), as well as to a behavioral model capturing navigational affordances. We found a temporal processing hierarchy: navigational affordance is processed later than the other scene features (2D, 3D, and semantic) investigated.

Coherent categorical information triggers integration-related alpha dynamics.

To create coherent visual experiences, the brain spatially integrates the complex and dynamic information it receives from the environment. We previously demonstrated that feedback-related alpha activity carries stimulus-specific information when two spatially and temporally coherent naturalistic inputs can be integrated into a unified percept. In this study, we sought to determine whether such integration-related alpha dynamics are triggered by categorical coherence in visual inputs.

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.

Communicative signals during joint attention promote neural processes of infants and caregivers.

Communicative signals such as eye contact increase infants' brain activation to visual stimuli and promote joint attention. Our study assessed whether communicative signals during joint attention enhance infant-caregiver dyads' neural responses to objects, and their neural synchrony. To track mutual attention processes, we applied rhythmic visual stimulation (RVS), presenting images of objects to 12-month-old infants and their mothers (n = 37 dyads), while we recorded dyads' brain activity (i.

Empirically Identifying and Computationally Modeling the Brain-Behavior Relationship for Human Scene Categorization.

Humans effortlessly make quick and accurate perceptual decisions about the nature of their immediate visual environment, such as the category of the scene they face. Previous research has revealed a rich set of cortical representations potentially underlying this feat. However, it remains unknown which of these representations are suitably formatted for decision-making.

Attention Modulates Human Visual Responses to Objects by Tuning Sharpening.

Visual stimuli compete with each other for cortical processing and attention biases this competition in favor of the attended stimulus. How does the relationship between the stimuli affect the strength of this attentional bias? Here, we used functional MRI to explore the effect of target-distractor similarity in neural representation on attentional modulation in the human visual cortex using univariate and multivariate pattern analyses. Using stimuli from four object categories (human bodies, cats, cars and houses), we investigated attentional effects in the primary visual area V1, the object-selective regions LO and pFs, the body-selective region EBA, and the scene-selective region PPA.

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.

Deep Neural Networks and Visuo-Semantic Models Explain Complementary Components of Human Ventral-Stream Representational Dynamics.

Deep neural networks (DNNs) are promising models of the cortical computations supporting human object recognition. However, despite their ability to explain a significant portion of variance in neural data, the agreement between models and brain representational dynamics is far from perfect. We address this issue by asking which representational features are currently unaccounted for in neural time series data, estimated for multiple areas of the ventral stream via source-reconstructed magnetoencephalography data acquired in human participants (nine females, six males) during object viewing.

The Spatiotemporal Neural Dynamics of Object Recognition for Natural Images and Line Drawings.

Drawings offer a simple and efficient way to communicate meaning. While line drawings capture only coarsely how objects look in reality, we still perceive them as resembling real-world objects. Previous work has shown that this perceived similarity is mirrored by shared neural representations for drawings and natural images, which suggests that similar mechanisms underlie the recognition of both.

Visual category representations in the infant brain.

Visual categorization is a human core cognitive capacity that depends on the development of visual category representations in the infant brain. However, the exact nature of infant visual category representations and their relationship to the corresponding adult form remains unknown. Our results clarify the nature of visual category representations from electroencephalography (EEG) data in 6- to 8-month-old infants and their developmental trajectory toward adult maturity in the key characteristics of temporal dynamics, representational format, and spectral properties.

A large and rich EEG dataset for modeling human visual object recognition.

The human brain achieves visual object recognition through multiple stages of linear and nonlinear transformations operating at a millisecond scale. To predict and explain these rapid transformations, computational neuroscientists employ machine learning modeling techniques. However, state-of-the-art models require massive amounts of data to properly train, and to the present day there is a lack of vast brain datasets which extensively sample the temporal dynamics of visual object recognition.

A multivariate comparison of electroencephalogram and functional magnetic resonance imaging to electrocorticogram using visual object representations in humans.

Today, most neurocognitive studies in humans employ the non-invasive neuroimaging techniques functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG). However, how the data provided by fMRI and EEG relate exactly to the underlying neural activity remains incompletely understood. Here, we aimed to understand the relation between EEG and fMRI data at the level of neural population codes using multivariate pattern analysis.

Resolving the time course of visual and auditory object categorization.

Humans can effortlessly categorize objects, both when they are conveyed through visual images and spoken words. To resolve the neural correlates of object categorization, studies have so far primarily focused on the visual modality. It is therefore still unclear how the brain extracts categorical information from auditory signals.

Time-resolved multivariate pattern analysis of infant EEG data: A practical tutorial.

Time-resolved multivariate pattern analysis (MVPA), a popular technique for analyzing magneto- and electro-encephalography (M/EEG) neuroimaging data, quantifies the extent and time-course by which neural representations support the discrimination of relevant stimuli dimensions. As EEG is widely used for infant neuroimaging, time-resolved MVPA of infant EEG data is a particularly promising tool for infant cognitive neuroscience. MVPA has recently been applied to common infant imaging methods such as EEG and fNIRS.

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.

Modelling brain representations of abstract concepts.

conceptual representations are critical for human cognition. Despite their importance, key properties of these representations remain poorly understood. Here, we used computational models of distributional semantics to predict multivariate fMRI activity patterns during the activation and contextualization of abstract concepts.

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.