Effects of Structured Contemplative Content on Large Language Model Task Performance

Pre-registered experimental study testing whether the semantic content of tokens in a large language model's context window affects downstream task performance. Compares sacred geometry contemplative prose, dot-filler tokens, lorem ipsum, technical warmup text, Marcus Aurelius Meditations excerpts, and no-prefill baseline across 16 tasks spanning reasoning, insight, coding, and ethical domains. Two model architectures tested: Claude Sonnet 4.5 and GPT-OSS-120B. Primary outcome is blind-scored response quality. Study A tests content effects across 1,280 trials; Study B (conditional) tests placement effects if Study A shows signal.