Publications and Abstracts

Journal Articles

2024

1. 

   The neural representation of body part concepts

   Stephen Mazurchuk, Leonardo Fernandino, Jia-Qing Tong, Lisa L Conant, and Jeffrey R Binder

   Cerebral Cortex, 2024

   Abs Bib HTML

   Neuropsychological and neuroimaging studies provide evidence for a degree of category-related organization of conceptual knowledge in the brain. Some of this evidence indicates that body part concepts are distinctly represented from other categories; yet, the neural correlates and mechanisms underlying these dissociations are unclear. We expand on the limited prior data by measuring functional magnetic resonance imaging responses induced by body part words and performing a series of analyses investigating the cortical representation of this semantic category. Across voxel-level contrasts, pattern classification, representational similarity analysis, and vertex-wise encoding analyses, we find converging evidence that the posterior middle temporal gyrus, the supramarginal gyrus, and the ventral premotor cortex in the left hemisphere play important roles in the preferential representation of this category compared to other concrete objects.

   @article{10.1093/cercor/bhae213,
     year = {2024},
     title = {{The neural representation of body part concepts}},
     author = {Mazurchuk, Stephen and Fernandino, Leonardo and Tong, Jia-Qing and Conant, Lisa L and Binder, Jeffrey R},
     journal = {Cerebral Cortex},
     issn = {1047-3211},
     doi = {10.1093/cercor/bhae213},
     pages = {bhae213},
     number = {6},
     volume = {34},
     keywords = {},
   }

2023

1. 

   Stimulus repetition and sample size considerations in item-level representational similarity analysis

   Stephen Mazurchuk, Lisa L. Conant, Jia-Qing Tong, Jeffrey R. Binder, and Leonardo Fernandino

   Language, Cognition and Neuroscience, 2023

   Abs Bib HTML

   In studies using representational similarity analysis (RSA) of fMRI data, the reliability of the neural representational dissimilarity matrix (RDM) is a limiting factor in the ability to detect neural correlates of a model. A common strategy for boosting neural RDM reliability is to employ repeated presentations of the stimulus set across imaging runs or sessions. However, little is known about how the benefits of stimulus repetition are affected by repetition suppression, or how they compare with the benefits of increasing the number of participants. We examined the effects of these design parameters in two large data sets where participants performed a semantic decision task on visually presented words. We found that reliability gains from stimulus repetition were strongly affected by repetition suppression, both within and across scanning sessions separated by multiple weeks. The results provide new insights into these experimental design choices, particularly for item-level RSA studies of semantic cognition.

   @article{10.1080/23273798.2023.2232903,
     year = {2023},
     title = {{Stimulus repetition and sample size considerations in item-level representational similarity analysis}},
     author = {Mazurchuk, Stephen and Conant, Lisa L. and Tong, Jia-Qing and Binder, Jeffrey R. and Fernandino, Leonardo},
     journal = {Language, Cognition and Neuroscience},
     issn = {2327-3798},
     doi = {10.1080/23273798.2023.2232903},
     pages = {1--12},
     number = {ahead-of-print},
     volume = {ahead-of-print},
     keywords = {},
   }

2022

1. 

   A Distributed Network for Multimodal Experiential Representation of Concepts

   Jiaqing Tong, Jeffrey R Binder, Colin Humphries, Stephen Mazurchuk, Lisa L Conant, and Leonardo Fernandino

   The Journal of Neuroscience, 2022

   Abs Bib HTML PDF

   Neuroimaging, neuropsychological, and psychophysical evidence indicate that concept retrieval selectively engages specific sensory and motor brain systems involved in the acquisition of the retrieved concept. However, it remains unclear which supramodal cortical regions contribute to this process and what kind of information they represent. Here, we used representational similarity analysis of two large fMRI datasets with a searchlight approach to generate a detailed map of human brain regions where the semantic similarity structure across individual lexical concepts can be reliably detected. We hypothesized that heteromodal cortical areas typically associated with the default mode network encode multimodal experiential information about concepts, consistent with their proposed role as cortical integration hubs. In two studies involving different sets of concepts and different participants (both sexes), we found a distributed, bihemispheric network engaged in concept representation, composed of high-level association areas in the anterior, lateral, and ventral temporal lobe; inferior parietal lobule; posterior cingulate gyrus and precuneus; and medial, dorsal, ventrolateral, and orbital prefrontal cortex. In both studies, a multimodal model combining sensory, motor, affective, and other types of experiential information explained significant variance in the neural similarity structure observed in these regions that was not explained by unimodal experiential models or by distributional semantics (i.e., word2vec similarity). These results indicate that during concept retrieval, lexical concepts are represented across a vast expanse of high-level cortical regions, especially in the areas that make up the default mode network, and that these regions encode multimodal experiential information.SIGNIFICANCE STATEMENT Conceptual knowledge includes information acquired through various modalities of experience, such as visual, auditory, tactile, and emotional information. We investigated which brain regions encode mental representations that combine information from multiple modalities when participants think about the meaning of a word. We found that such representations are encoded across a widely distributed network of cortical areas in both hemispheres, including temporal, parietal, limbic, and prefrontal association areas. Several areas not traditionally associated with semantic cognition were also implicated. Our results indicate that the retrieval of conceptual knowledge during word comprehension relies on a much larger portion of the cerebral cortex than previously thought and that multimodal experiential information is represented throughout the entire network.

   @article{10.1523/jneurosci.1243-21.2022,
     year = {2022},
     title = {{A Distributed Network for Multimodal Experiential Representation of Concepts}},
     author = {Tong, Jiaqing and Binder, Jeffrey R and Humphries, Colin and Mazurchuk, Stephen and Conant, Lisa L and Fernandino, Leonardo},
     journal = {The Journal of Neuroscience},
     issn = {0270-6474},
     doi = {10.1523/jneurosci.1243-21.2022},
     pmid = {35940877},
     pmcid = {PMC9480893},
     pages = {7121--7130},
     number = {37},
     volume = {42},
     keywords = {},
   }

Abstracts

2024

1. Extracting Object Feature Norms from Large Language Models

   Stephen Mazurchuk, and Andrew Anderson

   Aug 2024

   Cognitive Computational Neuroscience

   Abs PDF

   Many cognitive neuroscience researchers collect semantic feature ratings for stimuli. While these ratings are often collected using online systems, it can be costly, and a considerable amount of work is sometimes required to prepare the query. Consequently, there is growing interest in augmenting this task by using the representations contained in large language models. We present and validate a method for extracting semantic ratings using a simple paradigm that allows researchers to easily estimate human norms using freely available open-source language models.

2. Explicit Experiential Features Outperform Association Norms in Semantic Decoding of Brain Activation

   Jia-Qing Tong, Stephen Mazurchuk, Jeffrey R Binder, and Leonardo Fernandino

   May 2024

   MCW Clinical Neuroscience Day

2023

1. Artifact concepts are more reliabily represented than animal concepts across the cortex

   Stephen Mazurchuk, Lisa Conant, Jeffrey R Binder, and Leonardo Fernandino

   Oct 2023

   Society for the Neurobiology of Language

   Poster
2. Neural and Experiential Semantic Correlates of the Unergative-Unaccusative Verb Distinction

   Songhee Kim, Jeffrey R. Binder, Jiaquing Tong, Stephen Mazurchuk, Joseph Heffernan, and Lisa Conant

   Oct 2023

   Society for the Neurobiology of Language

2022

1. A Common Neural Mechanism Underlying Object and Event Concept Representation

   Jiaqing Tong, Leonardo Fernandino, Stephen Mazurchuk, Lisa Conant, and Jeffrey Binder

   Apr 2022

   Cognitive Neuroscience Society
2. A Set of Ten Experiential Features Outperforms Taxonomic and Distributional Models in Predicting Neuroimaging and Behavioral Data

   Leonardo Fernandino, Jiaqing Tong, Stephen Mazurchuk, Lisa Conant, and Jeffrey Binder

   Apr 2022

   Cognitive Neuroscience Society
3. Category-specific representation of animal, food, tool, and vehicle concepts in the brain

   Jiaqing Tong, Leonardo Fernandino, Stephen Mazurchuk, Lisa Conant, and Jeffrey Binder

   Oct 2022

   Society for the Neurobiology of Language
4. Activation of Speech, Taste, and Visual Scene Experiential Content During Concept Retrieval

   Stephen Mazurchuk, Leonardo Fernandino, Jia-Qing Tong, Lisa Conant, and Jeffrey Binder

   Oct 2022

   Society for the Neurobiology of Language
5. A Shared Representational Code for Object and Event Concepts

   Jiaqing Tong, Leonardo Fernandino, Stephen Mazurchuk, Lisa L Conant, and Jeffrey Binder

   Oct 2022

   Society for the Neurobiology of Language
6. An Experiential Basis for Posterior Middle Temporal Gyrus Body Part Concept Representation

   Stephen Mazurchuk, Leonardo Fernandino, Jia-Qing Tong, Lisa Conant, and Jeffrey Binder

   May 2022

   Cognitive Neuroscience Society

   Poster
7. Experiential and Taxonomic Semantic Representations in the Cerebellum

   Stephen Mazurchuk, Leonardo Fernandino, Jia-Qing Tong, Lisa Conant, and Jeffrey Binder

   Feb 2022

   Graduate Student Symposium

   Poster

2021

1. The Cortical Representation of Body Part Concepts

   Stephen Mazurchuk, Leonardo Fernandino, Jiaqing Tong, Colin Humphries, Lisa Conant, and Jeffrey Binder

   Oct 2021

   Society for the Neurobiology of Language (Virtual)

   Slides
2. Event associated experiential features are involved in event representation at brain regions preferentially representing event concepts

   Jiaqing Tong, Jeffrey Binder, Stephen Mazurchuk, Colin Humphries, Lisa Conant, and Leonardo Fernandino

   Oct 2021

   Society for the Neurobiology of Language

2020

1. Temporoparietal Specialization for Event Knowledge Demonstrated by Multivoxel Pattern Classification and Representational Similarity Analysis

   Jiaqing Tong, Stephen Mazurchuk, Leonardo Fernandino, Colin Humphries, Lisa Conant, and Jeffrey Binder

   Oct 2020

   Society for the Neurobiology of Language

2018

1. Classifying congenic rats from dynamic near infra-red time series images

   Stephen Mazurchuk, Jaidip Jagtap, Gayatri Sharma, Michael Flister, and Amit Joshi

   Jun 2018

   World Molecular Imaging Conference

   Poster