Semantic variability predicts neural variability of object concepts

Abstract

The prevailing approach to the neuroscientific study of concepts is to characterize the neural pattern evoked by a given concept, averaging over any variation that might occur upon multiple retrieval attempts (e.g., across time, tasks, or people). This approach—which diverges substantially from approaches to studying conceptual processing with other methods—treats all variation as noise. Here, our goal is to determine whether variation in neural patterns evoked by semantic retrieval of a given concept is more than just measurement error, and instead reflects variation arising from contextual variability. We measured each concept's diversity of semantic contexts (“SV”) by analyzing its word frequency and co-occurrence statistics in large text corpora. To measure neural variability, we conducted an fMRI study and sampled neural activity associated with each concept when it appeared in three separate, randomized contexts. We predicted that concepts with low SV would exhibit uniform activation patterns across stimulus presentations, whereas concepts with high SV would exhibit more dynamic representations over time. We observed that a concept's SV score predicted its corresponding neural variability. This finding supports a flexible, distributed organization of semantic memory, where a concept's meaning and its neural activity patterns both continuously vary across contexts.