A5053482082- Neural dynamics and brain function
- Face Recognition and Perception
- Functional Brain Connectivity Studies
- Visual perception and processing mechanisms
- Neural Networks and Applications
- Neural and Behavioral Psychology Studies
- Child and Animal Learning Development
- Action Observation and Synchronization
- EEG and Brain-Computer Interfaces
- Memory and Neural Mechanisms
- Cognitive Science and Education Research
- Science Education and Pedagogy
- Language, Metaphor, and Cognition
- Semantic Web and Ontologies
- Neurobiology of Language and Bilingualism
- Epilepsy research and treatment
- Multisensory perception and integration
- Visual Attention and Saliency Detection
- Face and Expression Recognition
- Advanced Neuroimaging Techniques and Applications
- Advanced Image and Video Retrieval Techniques
- Language and cultural evolution
- Language Development and Disorders
- Aesthetic Perception and Analysis
- Cultural Differences and Values
Dartmouth College
2013-2025
Dartmouth Hospital
2011-2025
University of Pennsylvania
2006
Recent years have seen an increase in the popularity of multivariate pattern (MVP) analysis functional magnetic resonance (fMRI) data, and, to a much lesser extent, magneto- and electro-encephalography (M/EEG) data. We present CoSMoMVPA, lightweight MVPA (MVP analysis) toolbox implemented intersection Matlab GNU Octave languages, that treats both fMRI M/EEG data as first-class citizens. CoSMoMVPA supports all state-of-the-art MVP techniques, including searchlight analyses, classification,...
Evidence of category specificity from neuroimaging in the human visual system is generally limited to a few relatively coarse categorical distinctions—e.g., faces versus bodies, or animals artifacts—leaving unknown neural underpinnings fine-grained structure within these large domains. Here we use fMRI explore brain activity for set categories animate domain, including six animal species—two each three very different biological classes: primates, birds, and insects. Patterns throughout...
Current models of the functional architecture human cortex emphasize areas that capture coarse-scale features cortical topography but provide no account for population responses encode information in fine-scale patterns activity. Here, we present a linear model shared representational spaces captures distinctions among with response-tuning basis functions are common across brains and neural individual-specific topographic functions. We derive space whole using new algorithm, searchlight...
Major theories for explaining the organization of semantic memory in human brain are premised on often-observed dichotomous dissociation between living and nonliving objects. Evidence from neuroimaging has been interpreted to suggest that this distinction is reflected functional topography ventral vision pathway as lateral-to-medial activation gradients. Recently, we observed similar gradients also reflect differences among stimuli consistent with dimension graded animacy. Here, address...
Humans prioritize different semantic qualities of a complex stimulus depending on their behavioral goals. These features are encoded in distributed neural populations, yet it is unclear how attention might operate across these representations. To address this, we presented participants with naturalistic video clips animals behaving natural environments while the attended to either behavior or taxonomy. We used models representational geometry investigate attentional allocation affects...
Summary Objectives Interictal epileptiform discharges ( IED s) have been linked to memory impairment, but the spatial and temporal dynamics of this relationship remain elusive. In present study, we aim systematically characterize brain areas times at which s affect memory. Methods Eighty epilepsy patients participated in a delayed free recall task while undergoing intracranial electroencephalography EEG ) monitoring. We analyzed locations timing relative behavioral data order measure their...
This study explores how the lack of first-hand experience with color, as a result congenital blindness, affects implicit judgments about “higher-order” concepts, such “fruits and vegetables” (FV), but not others, “household items” (HHI). We demonstrate differential diagnosticity color across our test categories interacts visual to produce, in effect, category-specific difference similarity. Implicit pair-wise similarity were collected by using an odd-man-out triad task. Pair-wise...
Common or folk knowledge about animals is dominated by three dimensions: (1) level of cognitive complexity “animacy;” (2) dangerousness “predacity;” and (3) size. We investigated the neural basis perceived aggressiveness animals, which we refer to more generally as “perception threat.” Using functional magnetic resonance imaging (fMRI), analyzed activity evoked viewing images animal categories that spanned dissociable semantic dimensions threat taxonomic class. The results reveal a...
A central goal in neuroscience is to interpret neural activation and, moreover, do so a way that captures universal principles by generalizing across individuals. Recent research multivoxel pattern-based fMRI analysis has led considerable success at decoding within individual subjects. However, the of being able decode subjects still challenging: It remained unclear what population-level regularities representation there might be. Here, we present novel and highly accurate solution this...
Abstract Recent years have seen an increase in the popularity of multivariate pattern (MVP) analysis functional magnetic resonance (fMRI) data, and, to a much lesser extent, magneto-and electro-encephalography (M/EEG) data. We present CoSMoMVPA, lightweight MVPA (MVP analysis) toolbox implemented intersection Matlab and GNU Octave languages, that treats both fMRI M/EEG data as first-class citizens. CoSMoMVPA supports all state-of-the-art MVP techniques, including searchlight analyses,...
Cortical resources are allocated into systems specialized for processing ecologically relevant features of the world. These typically studied in isolation using controlled experimental stimuli, making it difficult to assess relative importance different kinds that tend overlap during natural vision. In current study, we evaluated contributions action, agent, and scene predicting cortical activity while participants viewed a 1-hour nature documentary. We tested four sets model features:...
Abstract Traditional tests of concept knowledge generate scores to assess how well a learner understands concept. Here, we investigated whether patterns brain activity collected during task could be used compute neural ‘score’ complement traditional an individual’s conceptual understanding. Using novel data-driven multivariate neuroimaging approach—informational network analysis—we successfully derived score from across the that predicted individual differences in multiple tasks physics and...
Abstract Mental models provide a cognitive framework allowing for spatially organizing information while reasoning about the world. However, transitive studies often rely on perception of stimuli that contain visible spatial features, possibility associated neural representations are specific to inherently content. Here, we test hypothesis mental generated through frontoparietal network irrespective nature stimulus Content within three ranges from expressly visuospatial abstract. All...
Encoding models for mapping voxelwise semantic tuning are typically estimated separately each individual, limiting their generalizability. In the current report, we develop a method estimating encoding that generalize across individuals. Functional MRI was used to measure brain responses while participants freely viewed naturalistic audiovisual movie. Word embeddings capturing agent-, action-, object-, and scene-related content were assigned imaging volume based on an annotation of film. We...
We present a new discriminant analysis (DA) method called Multiple Subject Barycentric Discriminant Analysis (MUSUBADA) suited for analyzing fMRI data because it handles datasets with multiple participants that each provides different number of variables (i.e., voxels) are themselves grouped into regions interest (ROIs). Like DA, MUSUBADA (1) assigns observations to predefined categories, (2) gives factorial maps displaying and (3) optimally categories. cases more than can project portions...
DATA REPORT article Front. Neurosci., 15 May 2018Sec. Perception Science Volume 12 - 2018 | https://doi.org/10.3389/fnins.2018.00316
The detection of epileptiform activity, such as interictal spikes, in electrical brain recordings has important clinical and research applications. Identification spikes is often carried out manually by trained neurologists. It a time-consuming process can exhibit variability between experts. In this work, we develop evaluate an automated spike detector. We implement template-matching approach improve its accuracy on one set using supervised machine-learning algorithm. Evaluation with two...
Abstract How does STEM knowledge learned in school change students’ brains? Using fMRI, we presented photographs of real-world structures to engineering students with classroom-based and hands-on lab experience, examining how their brain activity differentiated them from “novice” peers not pursuing degrees. A data-driven MVPA machine-learning approach revealed that neural response patterns were convergent each other distinct novices’ when considering physical forces acting on the structures....
Abstract Humans prioritize different semantic qualities of a complex stimulus depending on their behavioral goals. These features are encoded in distributed neural populations, yet it is unclear how attention might operate across these representations. To address this, we presented participants with naturalistic video clips animals behaving natural environments while the attended to either behavior or taxonomy. We used models representational geometry investigate attentional allocation...