Person:
Susi García, María Del Rosario

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First Name
María Del Rosario
Last Name
Susi García
URI
https://hdl.handle.net/20.500.14352/77434
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Estudios estadísticos
Department
Estadística y Ciencia de los Datos
Area
Estadística e Investigación Operativa
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2007 - 200912020 - 20221
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    Bayesian reasoning with emotional material in patients with schizophrenia
    (Frontiers in Psychology, 2022) Susi García, María Del Rosario; Romero-Ferreiro, Verónica; Sánchez-Morla, Eva M.; Marí-Beffa, Paloma; Rodríguez-Gómez, Pablo; Amador Pacheco, Julia; Moreno, Eva M.; Romero, Carmen; Martínez-García, Natalia; Rodriguez-Jimenez, Roberto
    Delusions are one of the most classical symptoms described in schizophrenia. However, despite delusions are often emotionally charged, they have been investigated using tasks involving non-affective material, such as the Beads task. In this study we compared 30 patients with schizophrenia experiencing delusions with 32 matched controls in their pattern of responses to two versions of the Beads task within a Bayesian framework. The two versions of the Beads task consisted of one emotional and one neutral, both with ratios of beads of 60:40 and 80:20, considered, respectively, as the "difficult" and "easy" variants of the task. Results indicate that patients showed a greater deviation from the normative model, especially in the 60:40 ratio, suggesting that more inaccurate probability estimations are more likely to occur under uncertainty conditions. Additionally, both patients and controls showed a greater deviation in the emotional version of the task, providing evidence of a reasoning bias modulated by the content of the stimuli. Finally, a positive correlation between patients' deviation and delusional symptomatology was found. Impairments in the 60:40 ratio with emotional content was related to the amount of disruption in life caused by delusions. These results contribute to the understanding of how cognitive mechanisms interact with characteristics of the task (i.e., ambiguity and content) in the context of delusional thinking. These findings might be used to inform improved intervention programs in the domain of inferential reasoning.
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    Sensitivity Analysis in Gaussian Bayesian Networks Using a Divergence Measure
    (Communications in statistics. Theory and methods, 2007) Gómez Villegas, Miguel Ángel; Main Yaque, Paloma; Susi García, María Del Rosario
    This article develops a method for computing the sensitivity analysis in a Gaussian Bayesian network. The measure presented is based on the Kullback–Leibler divergence and is useful to evaluate the impact of prior changes over the posterior marginal density of the target variable in the network. We find that some changes do not disturb the posterior marginal density of interest. Finally, we describe a method to compare different sensitivity measures obtained depending on where the inaccuracy was. An example is used to illustrate the concepts and methods presented.