Multidimensional semantic representations emerge from multi-frequency representational similarity learning

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.