Browsing by Author "Smith, Ryan"

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    Active Inference in Multi-Objective Dynamic Environments
    (2022) Hodson, Rowan; Shock, Jonathan; Smith, Ryan; Solms, Mark
    Artificial Intelligence holds the promise of not only creating intelligent entities, but also unlocking the mysteries of our brains, and the nature of the subjective consciousness that accompanies them. Many paradigms of artificial intelligence are attempting to push the boundaries of the field, in order to catch a glimpse of the secrets behind general intelligence and the nature of the human mind. A less explored, yet promising paradigm is that of Active Inference - a theory which details a first-principled explanation of how agents use action and perception to successfully operate within an external environment. Much work has been done to explore the framework's viability in modelling scenarios both related to neural process theory and more classical agent-based machine learning. However, due to the relative recency of the theory, there are still many areas of comparison and evaluation to explore. This dissertation aims to investigate Active Inference's algorithmic capacity to solve more complex decision-based environments. Specifically, with varying degrees of complexity, I make use of a dynamic environment with a multi-objective reward function to investigate the Active Inference agent's ability to learn and plan while balancing exploration and exploitation, and compare this to other Bayesian Machine Learning algorithms. In doing so, I investigate some novel approaches and additions to Active Inference's algorithmic structure which include a dynamic preference distribution, a two-tiered hierarchical approach to the state space (using model-free Reinforcement Learning to solve the lower level), and the introduction of the Propagated Parameter Belief Search algorithm - a modification to Active Inference which allows the agent to perform more complex counterfactual reasoning.
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    Affective agnosia: a core affective processing deficit in the alexithymia spectrum
    (2020-09-04) Lane, Richard D; Solms, Mark; Weihs, Karen L; Hishaw, Alex; Smith, Ryan
    Abstract Affective agnosia, an impairment in knowing how one feels emotionally, has been described as an extreme deficit in the experience and expression of emotion that may confer heightened risk for adverse medical outcomes. Alexithymia, by contrast, has been proposed as an over-arching construct that includes a spectrum of deficits of varying severity, including affective agnosia at the more severe end. This perspective has been challenged by Taylor and colleagues, who argue that the concept of affective agnosia is unnecessary. We compare these two perspectives by highlighting areas of agreement, reasons for asserting the importance of the affective agnosia concept, errors in Taylor and colleagues’ critique, and measurement issues. The need for performance-based measures of the ability to mentally represent emotional states in addition to metacognitive measures is emphasized. We then draw on a previously proposed three-process model of emotional awareness that distinguishes affective response generation, conceptualization and cognitive control processes which interact to produce a variety of emotional awareness and alexithymia phenotypes - including affective agnosia. The tools for measuring these three processes, their neural substrates, the mechanisms of brain-body interactions that confer heightened risk for adverse medical outcomes, and the differential treatment implications for different kinds of deficits are described. By conceptualizing alexithymia as a spectrum of deficits, the opportunity to match specific deficit mechanisms with personalized treatment for patients will be enhanced.