聊天界面分析数据时效率低，因认知负荷过高导致出错

Chat has become the default interface for AI-assisted data analysis. For multi-step, state-dependent analytical tasks, this is a mistake. Building on Woods (1984) Keyhole Effect, the cognitive cost of viewing large information spaces through narrow viewports, I show that chat interfaces systematically degrade analytical performance through five mechanisms: (1) constant content displacement defeats hippocampal spatial memory systems; (2) hidden state variables exceed working memory capacity (approximately 4 chunks under load); (3) forced verbalization triggers verbal overshadowing, degrading visual pattern recognition; (4) linear text streams block epistemic action and cognitive offloading; (5) serialization penalties scale with data dimensionality. I formalize cognitive overload as O = max(0, m - v - W) where m is task-relevant items, v is visible items, and W is working memory capacity. When O > 0, error probability increases and analytical biases (anchoring, confirmation, change blindness) amplify. Eight hybrid design patterns address these failures: Generative UI, Infinite Canvas, Deictic Interaction, State Rail, Ghost Layers, Mise en Place, Semantic Zoom, and Probabilistic UI. Each pattern targets specific cognitive bottlenecks while preserving natural language for intent specification and synthesis. Well-scaffolded conversational systems that encode expert priors may reduce load for guided tasks; the framework applies most strongly to open-ended exploration. The paper concludes with falsifiable hypotheses and experimental paradigms for empirical validation.