Alan Bush

Patients who participate in intracranial neuroscience research make invaluable contributions to our understanding of the brain, accelerating the development of neurotechnological interventions. Engagement of patients as part of this research presents unique challenges, where study goals can be distant from immediate clinical applications and require specialized domain knowledge. Yet methods for meaningfully integrating patient communities as part of these research efforts are essential, as intracranial neuroscience guides the application of artificial intelligence (AI) for understanding and enhancing human cognition. In order to identify what patients consider meaningful research engagement we interviewed individuals who participated in a study during their Deep Brain Stimulation (DBS) surgery and attended a group event where they interacted with our research team. Analysis of semi-structured interviews identified four main themes: interest in science and the future of clinical care, contributing to science to improve lives, connecting with others, and accessibility considerations. Based on these insights, we propose strategies for transformational participation of patient communities in intracranial neuroscience research with respect to engagement objectives, communication and scope. This approach offers a foundation for sustaining relationships between scientists and communities rooted in trust and transparency, to ensure that impacts of AI-driven neurotechnology on human health and cognition are aligned with patient needs as well as desired public values.

This dissertation presents a general framework for understanding, applying, comparing, and creating techniques for constructing representational semantics. The work begins with John Etchemendy's positive account of how model-theoretic semantics illuminates the consequence relation for specific languages. Etchemendy uses the term "representational semantics" to identify the approach to logical consequence he advocates. We abstract away from the particulars of Etchemendy's account, and construct a general conceptual framework we call the "representational schema." The representational schema gives a general form for techniques used to construct theories of logical consequence implementing representational semantics. ;We show how the representational schema is capable of subsuming not just modeltheoretic semantics, but also a class of techniques for constructing theories of logical consequence whose central concept can be abstracted from a corollary to Lindenbaum's Lemma; a class we call order-consistency semantics. We use the schema to describe a general methodology for applying representational techniques to construct theories of logical consequence for particular interpreted languages. That methodology is used to apply techniques subsumed by the representational schema to propositional logic, feature logics, and languages in which feature structures are considered as assertions in their own right. We show how the schema helps us to compare and contrast differing techniques of representational semantics across a number of important dimensions including the mode by which they explain the consequence relation; the range of interpreted languages to which they are applicable; their degree of epistemological commitment; and the ease with which they can be used in particular applications. We show how the representational schema serves as a basis for various vectors of extension, and use the schema to construct several new techniques of representational semantics. One reduces the epistemological commitments of the specific model-theoretic technique described by Etchemendy, another gives a model-theoretic technique capable of handling partial models, and a third is an order-consistency technique which does not require the assumption of Lindenbaum's Lemma. Applications and benefits of these extensions are described. tensions are described.