Exploring the relations between Quantum-Like Bayesian Networks and decision-making tasks with regard to face stimuli

- Representation of objects in an arbitrary n-dimensional vector space.
- Computation of quantum interference effects through vector similarity functions.
- Usage of contents of images to compute quantum interference parameters.
- Application of a Quantum-Like Bayesian Network to make predictions.

In this work, we propose to model the Categorization/Decision experiment from Busemeyer et al. (2009) with a Quantum-Like Bayesian Network. We also propose the representation of objects (or events) in an arbitrary n-dimensional vector space, enabling their comparison through similarity functions. The computed similarity value is used to set the quantum parameters in the Quantum-Like Bayesian Network model. Just like in the work of Pothos et al. (2013), we are not restricting our model to a vector in a two-dimensional space, but to an arbitrary multidimensional space.In the end, we conclude that the vector representation of the contents of the images can explain the paradoxical findings and the violations of the laws of classical probability that were found in some works of the literature, suggesting that the contents of the images can already produce some quantum effects.