Modelling the Mind

Conversational Diagnostic Artificial Intelligence promises to reduce opacity in medical AI by producing explanations that resemble human clinical reasoning. This presentation argues, however, that this promise gives rise to a trust–alignment divide. AI-generated chains of thought can be highly persuasive, making recommendations appear coherent and worthy of trust, while remaining only weakly faithful to the processes that produced them. This divergence matters because, as evidence shows, users are more likely to rely on systems that can articulate reasons, even when those reasons do not reliably disclose the basis of the recommendation. The presentation argues that the persuasiveness–faithfulness gap is not merely a contingent failure of current systems, but partly reflects the autoregressive and probabilistic architecture of large language models, which generates reasons through the same sequence-based mechanisms that generate outputs. In clinical settings, this creates the distinctive risk that persuasive but unfaithful explanations may reinforce automation bias by making reliance on AI appear not only convenient, but epistemically justified. The challenge for explainable medical AI is therefore not simply to produce more fluent and human-like explanations, but to distinguish explanations that merely elicit trust from explanations that can genuinely justify it.

Computations are abstract operations defined formally and independently from the physical media, which can implement them. This suggests that, if the brain is a computing system, then the computations it performs must be medium-independent operations. But whether the brain is a computing system and whether its computations are medium-independent are contentious issues. Here, I address these issues by focusing on neurotransmitters, whose activities have limited medium-independence, and whose functional roles in neural computing remain underexplored in the philosophy of the mind sciences. After distinguishing three functional roles of neurotransmitters in neural computation, I argue that some neural computations are not medium-independent, because neurotransmitters play a constitutive role in defining them. 

Deep neural networks have increasingly become objects of scientific and philosophical investigation in their own right. In this talk, I examine the possibilities and challenges of applying key concepts from the cognitive sciences to the domain of advanced connectionist systems. In particular, I focus on whether and in what way the notions of internal representation and cognitive ontology can play a role in the project of better understanding how today’s large language models work.

Abstract

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