Paper List

期刊: ArXiv Preprint
发布日期: 2025-12-04
Cognitive NeuroscienceComputational Neuroscience

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

Supriya Bordoloi, Cota Navin Gupta, Shyamanta M. Hazarika
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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.

核心创新

  • 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.

主要结论

  • 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.
研究空白： Prior neuroimaging studies on action language and affordance have largely relied on correlational measures (e.g., co-activation patterns), which cannot establish the direction of information flow or causal influence between brain regions, leaving the mechanistic dynamics of how affordance cues shape semantic comprehension unresolved.

摘要: 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.