Multidimensional semantic representations emerge from multi-frequency representational similarity learning
Year: 2026
Type: preprint
Abstract: Current theories of semantic representation are agnostic about how fine-grained neurophysiological activity corresponds to representation of multidimensional semantic information. By applying an innovative multivariate technique (representational similarity learning; RSL), we adjudicated three hypotheses: (1) that multidimensional semantic structure is represented within a single frequency range (e.g., gamma/high gamma); (2) that each semantic dimension is independently represented within a different frequency range; and (3) that multidimensional semantic information is “transfrequency” (at least some information emerges only when multiple frequencies are considered together). RSL was applied to time-frequency power and phase data extracted from electrocorticography (ECoG) grid electrodes on the surface of human ventral anterior temporal lobe (vATL). We found significant decoding of graded, multidimensional semantic information from a wide range of frequencies (4 – 200 Hz), but not from individual frequency bands, providing clear evidence that multidimensional semantic information is coded in a transfrequency fashion.
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
Awards: G2533725952, Comprehensive ECoG and neuroimaging brain connectomics to elucidate language network reorganization after brain surgery, G3206092632, G4356720366, Minimally invasive brain network mapping for preserving brain functions and predicting plasticity, Developing a neuroscience-led basis for diagnosis, prognosis, management and therapy for aphasia post-stroke., The flexible and interactive neural, computational and neurobiological mechanisms underpinning semantic cognition and its disorders., G8910634966
OpenAlex ID: W7214348942