2026-06-01T02:21:15Zhttps://docta.ucm.es/rest/oai/request
oai:docta.ucm.es:20.500.14352/1273632025-11-29T00:58:05Zcom_20.500.14352_14col_20.500.14352_15
Docta Complutense author Martínez Mingo, Alejandro author Jorge Botana, Guillermo de author Martinez Huertas, Jose Angel author Olmos, Ricardo 2025-11-28T13:30:02Z 2025-11-28T13:30:02Z 2023-07-01 Martínez-Mingo, A., Jorge-Botana, G., Martinez-Huertas, J. Á., & Albacete, R. O. (2023). Quantum projections on conceptual subspaces. Cognitive Systems Research, 82, 101154. DOI: 10.1016/j.cogsys.2023.101154 https://hdl.handle.net/20.500.14352/127363 https://doi.org/10.1016/j.cogsys.2023.101154 https://www.sciencedirect.com/science/article/abs/pii/S1389041723000827 One of the main challenges of cognitive science is to explain the representation of conceptual knowledge and the mechanisms involved in evaluating the similarities between these representations. Theories that attempt to explain this phenomenon should account for the fact that conceptual knowledge is not static. In line with this thinking, many studies suggest that the representation of a concept changes depending on context. Traditionally, concepts have been studied as vectors within a geometric space, sometimes called Semantic-Vector Space Models (S-VSMs). However, S-VSMs have certain limitations in emulating human biases or context effects when the similarity of concepts is judged. Such limitations are related to the use of a classical geometric approach that represents a concept as a point in space. Recently, some theories have proposed the use of sequential projections of subspaces based on Quantum Probability Theory (Busemeyer and Bruza, 2012; Pothos et al., 2013). They argue that this theoretical approach may facilitate accounting for human similarity biases and context effects in a more natural way. More specifically, Pothos and Busemeyer (2011) proposed the Quantum Similarity Model (QSM) to determine expectation in conceptual spaces in a non-monotonic logic frame. To the best of our knowledge, previous data-driven studies have used the QSM subspaces in a unidimensional way. In this paper, we present a data-driven method to generate these conceptual subspaces in a multidimensional manner using a traditional S-VSM. We present an illustration of the method taking Tversky’s classical examples to explain the effects of Asymmetry, Triangular Inequality, and the Diagnosticity by means of sequential projections of those conceptual subspaces. eng Quantum projections on conceptual subspaces journal article 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URL https://docta.ucm.es/bitstreams/13002401-e472-4d11-b669-88aec3656984/download File MD5 21da89dfaeafd730c297bf826efede62 2727012 application/pdf manuscript.pdf