Informational content of cosine and other similarities calculated from high-dimensional Conceptual Property Norm data

Neural and behavioral signatures of the multidimensionality of manipulable object processing

Understanding how we recognize objects requires unravelling the variables that govern the way we think about objects and the neural organization of object representations. A tenable hypothesis is that the organization of object knowledge follows key object-related dimensions. Here, we explored, behaviorally and neurally, the multidimensionality of object processing. We focused on within-domain object information as a proxy for the decisions we typically engage in our daily lives - e.g., identifying a hammer in the context of other tools. We extracted object-related dimensions from subjective human judgments on a set of manipulable objects. We show that the extracted dimensions are cognitively interpretable and relevant - i.e., participants are able to consistently label them, and these dimensions can guide object categorization; and are important for the neural organization of knowledge - i.e., they predict neural signals elicited by manipulable objects. This shows that multidimensionality is a hallmark of the organization of manipulable object knowledge.

Eliciting semantic properties: methods and applications

Asking subjects to list semantic properties for concepts is essential for predicting performance in several linguistic and non-linguistic tasks and for creating carefully controlled stimuli for experiments. The property elicitation task and the ensuing norms are widely used across the field, to investigate the organization of semantic memory and design computational models thereof. The contributions of the current Special Topic discuss several core issues concerning how semantic property norms are constructed and how they may be used for research aiming at understanding cognitive processing.