Muhammed Bilgin
conference 2026
tldr Human participants were non-Bayesian but architecture-sensitive: they updated early for individually diagnostic cues and later for relational evidence. Most evaluated LLMs showed much weaker sensitivity to this diagnostic structure, despite non-trivial final-choice accuracy - a failure mode we call information-architecture blindness.
M. Emre Bilgin
conference 2026
tldr Most reasoning fMRI studies ask which brain regions activate. This project asks a different question: can trial-wise brain activity during reasoning be explained by a small number of latent neural factors? We compare syllogistic and transitive reasoning, estimate ROI-level activity for each trial, and use PCA/factor analysis to search for stable low-dimensional structure. Functional connectivity is used as a complementary check: do these latent factors reflect coordinated network activity?
M. Emre Bilgin
conference 2026
tldr This study examines whether people, when making decisions under uncertainty, are sensitive not only to the question “How much evidence is there?” but also to how the evidence is organized. Initial pilot findings suggest that redundant information is utilized more directly, while synergistic information is more difficult to process but becomes actionable once connections are established. In short: What matters in reasoning is not just the amount of information, but its architecture.
M. Emre Bilgin
conference 2026
tldr LLM agents should not answer causal questions immediately when the necessary evidence is missing. We propose a query-conditioned causal agent that first asks: Do I have enough evidence to answer this specific causal question? If not, it selects the most useful next action—observe, intervene, retrieve, ask, or simulate—to reduce uncertainty about the target answer. The goal is not to learn the whole causal graph, but to gather just enough causal evidence to answer well.