Search bioRxiv⌕ Search

Biology subjects

Kaiser, D.

Publications and source records attributed to Kaiser, D..

At least 37 records · Page 2Linked to original sources

Spatiotemporal representations of contextual associations for real-world objects

In the real world, objects always appear in context. Many objects are reliably associated with a certain scene context (e.g., pots appear in kitchens) and other objects that appear in the same context (e.g., pans appear together with pots). Previous neuroimaging work suggests that such contextual associations shape the neural representation of isolated objects even in the absence of the scene context. Yet, three key questions remain unanswered: (1) How do representations of contextual associations relate to perceptual and categorical representation in visual cortex, (2) how do they emerge across time, and (3) how are they mechanistically implemented? To answer these questions, we recorded fMRI and EEG while participants (human, both sexes) viewed isolated objects stemming from two scene contexts. Multivariate pattern analysis on the neural data revealed that objects from the same context were coded more similarly than objects from different contexts in object-selective LOC and scene-selective PPA, even when systematically controlling for perceptual and categorical similarities. Such contextual relation representations emerged relatively late during visual processing (i.e., after perceptual and categorical representations), specifically in the anterior PPA, and likely through a mixture of object-to-object and object-to-scene associations. Together, our results demonstrate that contextual relation representations emerged for isolated objects, and without a task that encourages their formation, suggesting that objects automatically activate context frames that support visual cognition in real-world environments.

neuroscience↗

How do visual and conceptual factors predict the composition of typical scene drawings?

Imagine you are asked to draw a typical bedroom, what would you put on paper? Your choice of objects is likely to depend on visual occurrence statistics (i.e., the objects present in previously encountered bedrooms) and semantic relations between objects and scenes (i.e., the semantic relationship between the bedroom and its constituent objects). To investigate how these two factors contribute to the composition of typical scene drawings, we analyzed 1,192 drawings of six indoor scene categories, obtained from 303 participants. For each object featured in the drawings, we estimated its visual occurrence frequency from the ADE20K dataset of annotated scene images, and its semantic relatedness to the scene concept from a word2vec language processing model. Across all scenes of a given category, generalized linear models revealed that visual and conceptual factors both predicted the likelihood of an object featuring in the scene drawings, with a combined model outperforming both single-factor models. We further computed the visual and semantic specificity of objects for a given scene, that is, how diagnostic an object is for the scene. Object specificity offered only weak predictive power when predicting the selection of objects, yet even infrequently drawn objects remained diagnostic of their scenes. Taken together, we show that visual and conceptual factors jointly shape the composition of typical scene drawings. By releasing a large dataset of typical scene drawings alongside this work, we further provide a starting point for future studies exploring other critical properties of human drawings.

neuroscience↗

Characterizing Surround Suppression with Dynamic Natural Scenes

Surround suppression refers to the reduction in perceptual sensitivity to a central stimulus caused by its surrounding context. Previous studies have examined this phenomenon mainly with simple stimuli varying only in low-level visual features, leaving it unclear whether the same principles apply to natural scenes. To address this, we investigated surround suppression in complex and dynamic scenes by systematically manipulating categorical similarity and motion congruence between the center and surrounding scenes. Categorical similarity was examined across four levels: identical exemplars, different exemplars of the same basic-level category, different basic-level categories, and different superordinate categories. Motion direction was manipulated by center and surround drifting either in the same or opposite directions. In two experiments, we measured contrast sensitivity for the center scene during a categorization task. We found that the suppression increased as the categorical similarity between the center and the surround decreased, with the strongest suppression observed for different superordinate categories. This contrasts with results for simple stimuli, where increasing center-surround similarity increases suppression. Yet, consistent with the findings from simple stimuli, suppression was stronger when the center and surround moved in the same direction. These findings show that contextual modulation in natural vision is governed not only by low-level feature similarity but also by high-level categorical structure. Context-dependent suppression may therefore help the visual system prioritize coherent, task-relevant information while filtering incongruent input.

neuroscience↗

Shaping a Collaborative, Sustainable, Accessible, and Reproducible Future for Computational Modeling

The o2S2PARC platform is an open-source, extensible, and scalable cloud-based platform developed in the context of the U.S. National Institutes of Health SPARC program to support collaborative, sustainable, FAIR (findable, accessible, interoperable, reusable) and reproducible computational modeling and analysis. This publication presents the main features of o2S2PARC, its underlying approaches and philosophy, innovative aspects of the developed technologies, while also drawing attention to its rapid adoption. The paper showcases a variety of applications and use cases enabled by the platform. These include hybrid electromagnetic-electrophysiology simulations of neural interfaces, personalized brain and spinal cord stimulation planning, in silico device safety assessments, the training and application of AI systems (e.g., for model-predictive control and medical image segmentation), hybridized surrogate modeling and multi-objective optimization in high-dimensional parameter spaces, sensitive and unbiased validation of measurement devices, and interactive data analysis as paper supplements.

neuroscience↗

Dynamic neural representations of scene beauty are relatively unaffected by stimulus timing and task

Understanding the neural correlates of aesthetic experiences in natural environments is a central question in neuroaesthetics. A previous EEG study (Kaiser, 2022) identified early and temporally sustained neural representations of visual scene beauty. These results were obtained with long presentation durations (1,450 ms) and with explicit beauty judgments, rendering it unclear how presentation time and task demands shape the neural correlates of scene beauty. In two EEG experiments, we replicated this study while varying presentation time and task. Experiment 1 tested whether reducing stimulus presentation time from 1,450 ms to 100 ms altered neural representations of beauty. Experiment 2 examined whether beauty-related representations prevailed when participants performed an orthogonal task instead of explicitly judging beauty. Representational similarity analysis revealed that beauty-related neural representations emerged early (within 150 - 200 ms post-stimulus) and were sustained over time, in line with previous findings. Critically, we found that neither reduced presentation time nor the absence of an explicit beauty judgment significantly altered beauty-related neural dynamics. These results suggest that the neural correlates of scene beauty are relatively robust to stimulus presentation and task regimes, providing a potential correlate of the spontaneous perception of beauty in natural environments.

neuroscience↗

Cortical alpha rhythms interpolate occluded motion from natural scene context

Tracking objects as they dynamically move in and out of sight is critical for parsing our ever-changing real-world surroundings. Here, we explored how the interpolation of occluded object motion in natural scenes is mediated by top-down information flows expressed in cortical alpha rhythms. We recorded EEG while participants viewed videos of a person walking across a scene. We then used multivariate decoding on alpha-band responses to decode the direction of movement across the scene. In trials where the person was temporarily occluded, alpha dynamics interpolated the persons predicted movement. Critically, they did so in a context-dependent manner: When the scene context required the person to stop in front of an obstacle, alpha dynamics tracked the termination of motion during occlusion. As these effects were obtained with an orthogonal task at fixation, we conclude that alpha rhythms automatically interpolate occluded motion in a context-dependent way.

neuroscience↗

The neural time course of size constancy in natural scenes

Accurate real-world size perception relies on size constancy, a mechanism that integrates an objects retinal size with distance information. The neural time course of extracting pictorial distance cues from scenes and integrating them with retinal size information - a process referred to as scene-based size constancy - remains unknown. In two experiments, participants viewed objects with either large or small retinal sizes, presented at near or far distances in outdoor scene photographs, while performing an unrelated one-back task. We applied multivariate pattern analysis (MVPA) to time-resolved EEG data to decode the retinal size of large versus small objects, depending on their distance (near versus far) in the scenes. The objects were either perceptually similar in size (large-near versus small-far) or perceptually dissimilar in size (large-far versus small-near), reflecting size constancy. We found that the retinal size of objects could be decoded from 80 ms after scene onset onwards. Distance information modulated size decoding at least 120 ms later: from 200 ms after scene onset when objects were fixated, and from 280 ms when objects were viewed in the periphery. These findings reveal the neural time course of size constancy based on pictorial distance cues in natural scenes.

neuroscience↗

Representational shifts from feedforward to feedback rhythms index phenomenological integration in natural vision

How does the brain integrate complex and dynamic visual inputs into phenomenologically seamless percepts? Previous results demonstrate that when visual inputs are organized coherently across space and time, they are more strongly encoded in feedback-related alpha rhythms, and less strongly in feedforward-related gamma rhythms. Here, we tested whether this representational shift from feedforward to feedback rhythms is linked to the phenomenological experience of coherence. In an EEG study, we manipulated the degree of spatiotemporal coherence by presenting two segments from the same video across visual hemifields, either synchronously or asynchronously (with a delay between segments). We asked participants whether they perceived the stimulus as coherent or incoherent. When stimuli were presented at the perceptual threshold (i.e., when the same stimulus was judged as coherent 50% of times), perception co-varied with stimulus coding across alpha and gamma rhythms: When stimuli were perceived as coherent, they were represented in alpha activity; when stimuli were perceived as incoherent, they were represented in gamma activity. Whether the same visual input is perceived as coherent or incoherent thus depends on representational shifts between feedback-related alpha and feedforward-related gamma rhythms.

neuroscience↗

Enhanced and idiosyncratic neural representations of personally typical scenes

Previous research shows that the typicality of visual scenes (i.e., if they are good examples of a category) determines how easily they can be perceived and represented in the brain. However, the unique visual diets individuals are exposed to across their lifetimes should sculpt very personal notions of typicality. Here, we thus investigated whether scenes that are more typical to individual observers are more accurately perceived and represented in the brain. We used drawings to enable participants to describe typical scenes (e.g., a kitchen) and converted these drawings into 3D renders. These renders were used as stimuli in a scene categorization task, during which we recorded EEG. In line with previous findings, categorization was most accurate for renders resembling the typical scene drawings of individual participants. Our EEG analyses reveal two critical insights on how these individual differences emerge on the neural level: First, personally typical scenes yielded enhanced neural representations from around 200 ms after onset. Second, personally typical scenes were represented in idiosyncratic ways, with reduced dependence on high-level visual features. We interpret these findings in a predictive processing framework, where individual differences in internal models of scene categories formed through experience shape visual analysis in idiosyncratic ways.

neuroscience↗

The representational nature of spatio-temporal recurrent processing in visual object recognition

The human brain orchestrates object vision through an interplay of feedforward processing in concert with recurrent processing. However, where, when and how recurrent processing contributes to visual processing is incompletely understood due to the difficulties in teasing apart feedforward and recurrent processing. We combined a backward masking paradigm with multivariate analysis on EEG and fMRI data to isolate and characterize the nature of recurrent processing. We find that recurrent processing substantially shapes visual representations across the ventral visual stream, starting early on at around 100ms in early visual cortex (EVC) and in two later phases of around 175 and 300ms in lateral occipital cortex (LOC), adding persistent rather than transient neural dynamics to visual processing. Using deep neural network models for comparison with the brain, we show that recurrence changes the feature format in LOC from predominantly mid-level to more high-level features. Finally, we show that recurrence is mediated by four distinct spectro-temporal neural components in EVC and LOC, which span the theta to beta frequency range. Together, our results reveal the nature and mechanisms of the effects of recurrent processing on the visual representations in the human brain.

neuroscience↗

How do we imagine a speech? A triple network model for situationally simulated inner speech

Inner speech is a silent verbal experience and plays central roles in human consciousness and cognition. Despite impressive studies over the past decades, the neural mechanisms of inner speech remain largely unknown. In this study, we adopted an ecological paradigm called situationally simulated inner speech. Unlike mere imaging speech of words, situationally simulated inner speech involves the dynamic integration of contextual background, episodic and semantic memories, and external events into a coherent structure. We conducted dynamic activation and network analyses on fMRI data, where participants were instructed to engage in inner speech prompted by cue words across 10 different contextual backgrounds. Our seed-based co-activation pattern analyses revealed dynamic involvement of the language network, sensorimotor network, and default mode network in situationally simulated inner speech. Additionally, frame-wise dynamic conditional correlation analysis uncovered four temporal-reoccurring states with distinct functional connectivity patterns among these networks. We proposed a triple network model for deliberate inner speech, including language network for a truncated form of overt speech, sensorimotor network for perceptual simulation and monitoring, and default model network for integration and sense-making processing. HighlightsO_LIIn ten contextual backgrounds, subjects were instructed to perform situationally simulated inner speech based on cue words. C_LIO_LIThe ventral parts of the bilateral somatosensory areas and middle superior temporal gyrus were as centers for seed-based co-activation pattern analyses. C_LIO_LIA triple network model of language network, sensorimotor network, and default mode network was proposed for deliberate inner speech. C_LI

neuroscience↗

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. In an EEG experiment, we manipulated the degree of coherence by presenting pairs of videos from the same or different categories through two apertures in the left and right visual hemifields. Critically, video pairs could be video-level coherent (i.e., stem from the same video), coherent in their basic-level category, coherent in their superordinate category, or incoherent (i.e., stem from videos from two entirely different categories). We conducted multivariate classification analyses on rhythmic EEG responses to decode between the video stimuli in each condition. As the key result, we significantly decoded the video-level coherent and basic-level coherent stimuli, but not the superordinate coherent and incoherent stimuli, from cortical alpha rhythms. This suggests that alpha dynamics play a critical role in integrating information across space, and that cortical integration processes are flexible enough to accommodate information from different exemplars of the same basic-level category.

neuroscience↗

Imaginary scenes are represented 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. An additional 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 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.

neuroscience↗

Your place or mine? The neural dynamics of personally familiar scene recognition suggests category independent familiarity encoding

Recognizing a stimulus as familiar is an important capacity in our everyday life. Recent investigation of visual processes has led to important insights into the nature of the neural representations of familiarity for human faces. Still, little is known about how familiarity affects the neural dynamics of non-face stimulus processing. Here we report the results of an EEG study, examining the representational dynamics of personally familiar scenes. Participants viewed highly variable images of their own apartments and unfamiliar ones, as well as personally familiar and unfamiliar faces. Multivariate pattern analyses were used to examine the time course of differential processing of familiar and unfamiliar stimuli. Time-resolved classification revealed that familiarity is decodable from the EEG data similarly for scenes and faces. The temporal dynamics showed delayed onsets and peaks for scenes as compared to faces. Familiarity information, starting at 200 ms, generalized across stimulus categories and led to a robust familiarity effect. In addition, familiarity enhanced category representations in early (250 - 300 ms) and later (>400 ms) processing stages. Our results extend previous face familiarity results to another stimulus category and suggest that familiarity as a construct can be understood as a general, stimulus-independent processing step during recognition. HighlightsO_LIWhether a face or scene is familiar can be decoded from the EEG signal with very similar temporal dynamics, starting at 200 ms and peaking around 400 ms after stimulus onset. C_LIO_LIThe neural dynamics of this familiarity information generalizes across stimulus categories. C_LIO_LIFamiliarity modulates stimulus category representations from 200 ms after stimulus onset, indicating deeper processing of familiar as compared to unfamiliar stimuli already during early processing stages. C_LI

neuroscience↗

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

Previous research indicates that the beauty of natural images is already determined during perceptual analysis. However, it is still largely unclear which perceptual computations give rise to the perception of beauty. Theories of processing fluency suggest that the ease of processing for an image determines its perceived beauty. Here, we tested whether perceived beauty is related to the amount of spatial integration across an image, a perceptual computation that reduces processing demands by aggregating image elements into more efficient representations of the whole. We hypothesized that higher degrees of integration reduce processing demands in the visual system and thereby predispose the perception of beauty. We quantified integrative processing in an artificial deep neural network model of vision: We compared activations between parts of the image and the whole image, 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 the beauty ratings for natural images across four studies, which featured different stimuli and task demands. In a complementary fMRI study, we show that integrative processing in human visual cortex predicts perceived beauty in a similar way as in artificial neural networks. Together, our results establish integration as a computational principle that facilitates perceptual analysis and thereby mediates the perception of beauty.

neuroscience↗

TMS disruption of the lateral prefrontal cortex increases neural activity in the default mode network when naming facial expressions

Recognizing facial expressions is dependent on multiple brain networks specialized for different cognitive functions. In the current study participants (N=20) were scanned using functional magnetic resonance imaging (fMRI) while they performed a covert facial expression naming task. Immediately prior to scanning thetaburst transcranial magnetic stimulation (TMS) was delivered over the right lateral prefrontal cortex (PFC), or the vertex control site. A group whole-brain analysis revealed that TMS induced opposite effects in the neural responses across different brain networks. Stimulation of the right PFC (compared to stimulation of the vertex) decreased neural activity in the left lateral PFC but increased neural activity in three nodes of the default mode network (DMN): the right superior frontal gyrus (SFG), right angular gyrus and the bilateral middle cingulate gyrus. A region of interest (ROI) analysis showed that TMS delivered over the right PFC reduced neural activity across all functionally localised face areas (including in the PFC) compared to TMS delivered over the vertex. These results causally demonstrate that visually recognizing facial expressions is dependent on the dynamic interaction of the face processing network and the DMN. Our study also demonstrates the utility of combined TMS / fMRI studies for revealing the dynamic interactions between different functional brain networks.

neuroscience↗

EEG decoding reveals neural predictions for naturalistic material behaviors

Material properties like softness or stickiness determine how an object can be used. Based on our real-life experience, we form strong expectations about how objects should behave under force, given their typical material properties. Such expectations have been shown to modulate perceptual processes, but we currently do not know how expectation influences the temporal dynamics of the cortical visual analysis for objects and their materials. Here, we tracked the neural representations of expected and unexpected material behaviors using time-resolved EEG decoding in a violation-of-expectation paradigm, where objects fell to the ground and deformed in expected or unexpected ways. Participants were 25 men and women. Our study yielded three key results: First, both objects and materials were represented rapidly and in a temporally sustained fashion. Second, objects exhibiting unexpected material behaviors were more successfully decoded than objects exhibiting expected behaviors within 190ms after the impact, which might indicate additional processing demands when expectations are unmet. Third, general signals of expectation fulfillment that generalize across specific objects and materials were found within the first 150ms after the impact. Together, our results provide new insights into the temporal neural processing cascade that underlies the analysis of real-world material behaviors. They reveal a sequence of predictions, with cortical signals progressing from a general signature of expectation fulfillment towards increased processing of unexpected material behaviors. Significance StatementIn the real world, we can make accurate predictions about how an objects material shapes its behavior: For instance, we know that cups are typically made of porcelain and shatter when we accidentally drop them. Here, we use EEG to experimentally test how expectations about material behaviors impact neural processing. We showed our participants videos of objects that exhibited expected material behaviors (such as a glass shattering when falling to the ground) or unexpected material behaviors (such as a glass melting upon impact). Our results reveal a hierarchy of predictions in cortex: The visual system rapidly generates signals that index whether expectations about material behaviors are met. These signals are followed by increased processing of objects displaying unexpected material behaviors.

neuroscience↗

Alpha-frequency feedback to early visual cortex orchestrates coherent natural vision

During natural vision, the brain generates coherent percepts by integrating sensory inputs scattered across the visual field. Here, we asked whether this integration process is mediated by rhythmic cortical feedback. In EEG and fMRI experiments, we experimentally manipulated the demand for integration by changing the spatiotemporal coherence of natural videos presented across visual hemifields. Our EEG data revealed that information about incongruent videos is coded in feedforward-related gamma activity while information about congruent videos is coded in feedback-related alpha activity, indicating that integration is indeed mediated by rhythmic feedback. Our fMRI data identified scene-selective cortex as a likely source of this feedback. Analytically combining our EEG and fMRI data further revealed that feedback-related representations in the alpha band shape the earliest stages of visual processing in cortex. Together, our findings indicate that the construction of coherent visual experiences relies on rhythmic cortical feedback that fully traverses the visual hierarchy.

neuroscience↗