Causality is hard. But prediction is measurable. If one signal helps predict another, we can at least say that information is flowing in a directed way. This paper measures that directed predictability, and it measures how “sticky” the system’s coarse states are, inside a non-trained cognitive runtime. Technically, we analyze a 15,040-tick unified log table and three empirically-defined epochs: E1 (lowentropy baseline), E2 (high-entropy plateau), and E3 (low-entropy baseline). Directed predictability is quantified via a fast Granger pipeline 1, 2, 3 over six scalar observables (entropy, complexity, firing variability, reward mean, TD error, and novelty/uniqueness). We report causal density 4, 5 as the fraction of significant directed edges at α = 0.01 (lag p = 5). Macrostate dynamics are separately summarized as a Markov chain over coarse labels (high/low entropy × output band), and we report stationary entropy H(π) and entropy rate Hrate in bits 6, 7, 8. Results show a pronounced regime dependence: causal density drops from 0.80 in E1 to 0.30 in E2 and partially recovers to 0.47 in E3, while the macrostate stationary entropy collapses from 1.56 bits (≈ 2.95 effective states) in E1 to 0.10 bits (≈ 1.07 effective states) in E2. The included bundle provides deterministic scripts and hashes for all tables and figures referenced here.
Justin Lietz (Sat,) studied this question.