Johann Roland Kleinbub1, Alberto Testolin2,3, Arianna Palmieri1,4, Sergio Salvatore5. 1. Department of Philosophy, Sociology, Education, and Applied Psychology, University of Padua, Padua, Italy. 2. Department of General Psychology, University of Padova, Padua, Italy. 3. Department of Information Engineering, University of Padova, Padua, Italy. 4. Padova Neuroscience Center, University of Padua, Padua, Italy. 5. Department of Dynamic and Clinical Psychology, and Health Studies, Sapienza Università di Roma, Rome, Italy.
Abstract
INTRODUCTION: The hypothesis of a general psychopathology factor that underpins all common forms of mental disorders has been gaining momentum in contemporary clinical research and is known as the p factor hypothesis. Recently, a semiotic, embodied, and psychoanalytic conceptualisation of the p factor has been proposed called the Harmonium Model, which provides a computational account of such a construct. This research tested the core tenet of the Harmonium model, which is the idea that psychopathology can be conceptualised as due to poorly-modulable cognitive processes, and modelled the concept of Phase Space of Meaning (PSM) at the computational level. METHOD: Two studies were performed, both based on a simulation design implementing a deep learning model, simulating a cognitive process: a classification task. The level of performance of the task was considered the simulated equivalent to the normality-psychopathology continuum, the dimensionality of the neural network's internal computational dynamics being the simulated equivalent of the PSM's dimensionality. RESULTS: The neural networks' level of performance was shown to be associated with the characteristics of the internal computational dynamics, assumed to be the simulated equivalent of poorly-modulable cognitive processes. DISCUSSION: Findings supported the hypothesis. They showed that the neural network's low performance was a matter of the combination of predicted characteristics of the neural networks' internal computational dynamics. Implications, limitations, and further research directions are discussed.
INTRODUCTION: The hypothesis of a general psychopathology factor that underpins all common forms of mental disorders has been gaining momentum in contemporary clinical research and is known as the p factor hypothesis. Recently, a semiotic, embodied, and psychoanalytic conceptualisation of the p factor has been proposed called the Harmonium Model, which provides a computational account of such a construct. This research tested the core tenet of the Harmonium model, which is the idea that psychopathology can be conceptualised as due to poorly-modulable cognitive processes, and modelled the concept of Phase Space of Meaning (PSM) at the computational level. METHOD: Two studies were performed, both based on a simulation design implementing a deep learning model, simulating a cognitive process: a classification task. The level of performance of the task was considered the simulated equivalent to the normality-psychopathology continuum, the dimensionality of the neural network's internal computational dynamics being the simulated equivalent of the PSM's dimensionality. RESULTS: The neural networks' level of performance was shown to be associated with the characteristics of the internal computational dynamics, assumed to be the simulated equivalent of poorly-modulable cognitive processes. DISCUSSION: Findings supported the hypothesis. They showed that the neural network's low performance was a matter of the combination of predicted characteristics of the neural networks' internal computational dynamics. Implications, limitations, and further research directions are discussed.