Paper List
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SSDLabeler: Realistic semi-synthetic data generation for multi-label artifact classification in EEG
This paper addresses the core challenge of training robust multi-label EEG artifact classifiers by overcoming the scarcity and limited diversity of ma...
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Decoding Selective Auditory Attention to Musical Elements in Ecologically Valid Music Listening
This paper addresses the core challenge of objectively quantifying listeners' selective attention to specific musical components (e.g., vocals, drums,...
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Physics-Guided Surrogate Modeling for Machine Learning–Driven DLD Design Optimization
This paper addresses the core bottleneck of translating microfluidic DLD devices from research prototypes to clinical applications by replacing weeks-...
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Mechanistic Interpretability of Antibody Language Models Using SAEs
This work addresses the core challenge of achieving both interpretability and controllable generation in domain-specific protein language models, spec...
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The Effective Reproduction Number in the Kermack-McKendrick model with age of infection and reinfection
This paper addresses the challenge of accurately estimating the time-varying effective reproduction number ℛ(t) in epidemics by incorporating two crit...
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Fluctuating Environments Favor Extreme Dormancy Strategies and Penalize Intermediate Ones
This paper addresses the core challenge of determining how organisms should tune dormancy duration to match the temporal autocorrelation of their envi...
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Covering Relations in the Poset of Combinatorial Neural Codes
This work addresses the core challenge of navigating the complex poset structure of neural codes to systematically test the conjecture linking convex ...
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Revealing stimulus-dependent dynamics through statistical complexity
This paper addresses the core challenge of detecting stimulus-specific patterns in neural population dynamics that remain hidden to traditional variab...
Influence of Object Affordance on Action Language Understanding: Evidence from Dynamic Causal Modeling Analysis
Centre for Linguistic Science and Technology (CLST), Indian Institute of Technology Guwahati | Neural Engineering Lab, Department of Bio Sciences and Bio Engineering, IIT Guwahati | Biomimetic Robotics and Artificial Intelligence Lab (BRAIL), Department of Mechanical Engineering, IIT Guwahati
The 30-Second View
IN SHORT: This study addresses the core challenge of moving beyond correlational evidence to establish the *causal direction* and *temporal dynamics* of how object affordance processing in sensorimotor brain regions drives the comprehension of action-related language.
Innovation (TL;DR)
- Methodology Applies Dynamic Causal Modeling (DCM) to EEG data to infer *directed, causal connectivity* between key brain regions during affordance-language processing, moving beyond traditional correlational analyses.
- Biology Identifies a specific feedforward causal architecture where the Ventral Premotor Cortex (PMv) acts as a driver, causally influencing the Inferior Parietal Lobule (IPL) and Posterior Superior Temporal Gyrus (pSTG) during action language comprehension.
- Theory Provides direct, mechanistic evidence supporting grounded/embodied cognition theories by showing that affordance-related motor regions (PMv) actively *drive* semantic hubs (pSTG, IPL), rather than merely co-activating with them.
Key conclusions
- Bayesian Model Selection identified a dominant model (M6, exceedance probability = 0.91) featuring strong modulatory influences from PMv to IPL (mean coupling strength = 0.28 Hz ± 0.05) and PMv to pSTG, establishing a causal feedforward pathway from motor to semantic regions.
- The video+text condition significantly strengthened the causal influence from PMv to IPL and pSTG compared to the text-only condition, demonstrating that multimodal (visual+linguistic) affordance cues amplify the driving role of premotor cortex.
- Source localization (LORETA) and DCM together delineate a core left-hemisphere network (LOC, pSTg, PMv, IPL) where visual input (LOC) feeds into premotor affordance processing (PMv), which in turn causally drives semantic integration in parietal (IPL) and temporal (pSTG) hubs.
Abstract: This study investigates the causal neural dynamics by which affordance representations influence action language comprehension. In this study, 18 participants observed stimuli displayed in two conditions during the experiment: text-only (e.g., ‘Hit with a hammer’) and video+text (visual clips with matching phrases). EEG data were recorded from 32 channels and analyzed for event-related potentials and source localization using LORETA, which identified four left-hemisphere regions of interest: the Lateral Occipital Cortex (LOC), Posterior Superior Temporal Gyrus (pSTG), Ventral Premotor Cortex (PMv), and Inferior Parietal Lobule (IPL). A space of dynamic causal modeling (DCM) was constructed with driving inputs to LOC and pSTG, and multiple connectivity configurations were tested. Bayesian Model Selection revealed a dominant model in which PMv causally influenced IPL and pSTG, reflecting a feedforward architecture from affordance-related motor regions to semantic hubs. Bayesian Model Averaging further confirmed strong endogenous connections from LOC to PMv and IPL, and significant modulation from PMv to IPL. These findings provide direct evidence that affordance processing in premotor regions drives action language understanding by engaging downstream parietal and temporal areas. The results support grounded cognition theories and offer a mechanistic account of how sensorimotor information contributes to linguistic comprehension.