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Canessa, E., Chaigneau, S. E., Moreno, S., & Lagos, R. (2020). Informational content of cosine and other similarities calculated from high-dimensional Conceptual Property Norm data. Cogn. Process., 21, 601–614.
Abstract: To study concepts that are coded in language, researchers often collect lists of conceptual properties produced by human subjects. From these data, different measures can be computed. In particular, inter-concept similarity is an important variable used in experimental studies. Among possible similarity measures, the cosine of conceptual property frequency vectors seems to be a de facto standard. However, there is a lack of comparative studies that test the merit of different similarity measures when computed from property frequency data. The current work compares four different similarity measures (cosine, correlation, Euclidean and Chebyshev) and five different types of data structures. To that end, we compared the informational content (i.e., entropy) delivered by each of those 4 x 5 = 20 combinations, and used a clustering procedure as a concrete example of how informational content affects statistical analyses. Our results lead us to conclude that similarity measures computed from lower-dimensional data fare better than those calculated from higher-dimensional data, and suggest that researchers should be more aware of data sparseness and dimensionality, and their consequences for statistical analyses.
Chaigneau, S. E., Canessa, E., Lenci, A., & Devereux, B. (2020). Eliciting semantic properties: methods and applications. Cogn. Process., 21(4), 583–586.
Abstract: 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.