Disentangling objects' contextual associations from perceptual and conceptual attributes using time-resolved neural decoding

Humans effortlessly relate what they see to what they know, drawing on existing knowledge of the perceptual, conceptual, and contextual attributes of objects while searching for and recognizing objects. Although prior studies have investigated the temporal dynamics of perceptual and conceptual object properties in the neural signal, it remains unclear whether and when contextual associations are uniquely represented. In this study, we used representational similarity analysis on electroencephalography (EEG) data to explore how the brain processes the perceptual, conceptual, and contextual dimensions of object knowledge over time. Using human similarity judgments of 190 naturalistic object concepts presented as either images or words, we constructed separate behavioral models of the perceptual, conceptual, and contextual properties of objects. We correlated these models with neural patterns from two EEG datasets, one publicly available and one newly collected, both recorded while participants passively viewed the same object stimuli. Across both datasets, we found that perceptual features dominated the early EEG response to object images, and conceptual features emerged later. Contextual associations were also reflected in neural patterns, but their explanatory power largely overlapped with that of conceptual models, suggesting limited unique representation of the contextual attributes of objects under passive viewing conditions. These results highlight the integration of perceptual and conceptual information by the brain when processing visual objects. By combining high temporal resolution EEG with behaviorally derived models, this study advances our understanding of how distinct dimensions of object knowledge are encoded in the human brain.