In this paper, we aim to integrate findings from the fields of machine learning and network science to argue URLanisms can be modeled as hierarchical Bayesian control systems with small world and bottleneck (bow tie ) network structure. The nested URLanization of such networks URLanisms to form increasingly integrated percepts and concepts of their inner and outer context, which can be compared to increasingly encompassing predictive models of the world (goal states), to allow for an optimal controlof actions . We argue that hierarchical Bayesian inference produces a hierarchy of goal states, from which it follows URLanisms must have some form of ` highest goals'. For URLanisms, these predictive models involve interior (self) models, exterior (social) models and overarching (normative) models. We show how such goals are constructed from progressively lesser goal states and that goal hierarchies tend to decompose in a top-down manner under severe and prolonged levels of stress. This loss of high-level control leads to a disinhibition of subordinate hierarchical levels, producing `critical' behavior and tipping points ( a sudden loss of homeostasis). Such phase transitions amount either to disease or the death of URLanism . This model can be used to URLanisms of any type, including humans. In humans, learning higher-level world models corresponds to personality development . A top-down collapse of high-level integrative goal states under stress is identified as a common factor in all forms of mental disease (psychopathology). The paper concludes by discussing ways of testing these hypotheses empirically.
In this paper, we show URLanisms can be modeled as hierarchical Bayesian control systems with small world and information bottleneck (bow-tie ) network structure. Such systems combine hierarchical perception with hierarchical goal setting and hierarchical action control . We argue that hierarchical Bayesian control systems produce deep hierarchies of goal states, from which it follows URLanisms must have some form of ' highest goals'. For URLanisms, these involve internal (self) models, external (social) models and overarching (normative) models. We show that goal hierarchies tend to decompose in a top-down manner under severe and prolonged levels of stress. This produces behavior that favors short-term and self-referential goals over long term, social and/or normative goals. The collapse of goal hierarchies is universally accompanied by an increase in entropy (disorder) in control systems that can serve as an early warning sign for tipping points ( disease or death of URLanism ). In humans, learning goal hierarchies corresponds to personality development (maturation). The failure of goal hierarchies to mature properly corresponds to personality deficits. A top-down collapse of such hierarchies under stress is identified as a common factor in all forms of episodic mental disorders (psychopathology). The paper concludes by discussing ways of testing these hypotheses empirically.
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aim to integrate findings from the fields of machine learning and network science to argue
show
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bottleneck (bow tie
information bottleneck (bow-tie
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The nested URLanization of such networks URLanisms to form increasingly integrated percepts and concepts of their inner and outer context, which can be compared to increasingly encompassing predictive models of the world (goal states), to allow for an optimal controlof actions
Such systems combine hierarchical perception with hierarchical goal setting and hierarchical action control
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inference produces a hierarchy
control systems produce deep hierarchies
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predictive models involve interior
involve internal
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exterior
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how such goals are constructed from progressively lesser goal states and
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loss of high-level control leads to a disinhibition of subordinate hierarchical levels, producing `critical' behavior and
produces behavior that favors short-term and self-referential goals over long term, social and/or normative goals. The collapse of goal hierarchies is universally accompanied by an increase in entropy (disorder) in control systems that can serve as an early warning sign for
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a sudden loss of homeostasis). Such phase transitions amount either to disease or the
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. This model can be used to URLanisms of any type, including humans.
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higher-level world models
goal hierarchies
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(maturation). The failure of goal hierarchies to mature properly corresponds to personality deficits.
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high-level integrative goal states
such hierarchies
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mental disease
episodic mental disorders