Preregistered Evaluation of the R.A.M. Framework in Human-AI Interaction: Cognitive Decision Study (v1.0)

This preregistration describes Cognitive Decision Study (v1.0), an online, survey-based study designed to evaluate the R.A.M. framework (The Rhythmic Architecture of Mind) in human-AI interaction. The core objective is to test whether interaction structures aligned with R.A.M. principles are associated with improved cognitive clarity, reduced perceived friction, and more effective decision flow compared with non-aligned interaction patterns. The study collects anonymized participant responses through a structured web instrument hosted on the project website. The protocol includes vignette-based comparisons, Likert-scale psychometric items, attention checks, quality flags, and open-text responses. Data are captured in Google Sheets via a scripted pipeline and transformed into analysis-ready variables through predefined scoring rules, including reverse scoring where applicable and transparent quality filtering (attention failure, low-effort text, and straightlining indicators). Primary outcomes include: mean cognitive friction score, mean framework-alignment subscale scores (e.g., mental space, decision flow, pattern mapping, rhythm-aligned action), directional differences between interaction conditions in clarity, cognitive load/heaviness, and perceived fit, quality-pass vs. total response indicators. Expected outcomes are directional and theory-driven rather than confirmatory of a guaranteed effect: responses aligned with R.A.M.- consistent interaction structure are expected to show lower friction and better clarity/fit profiles at aggregate level. This preregistration fixes the study intent, operationalization, and analysis logic before full launch, supporting transparency, reproducibility, and stronger inferential credibility for later reporting. The project is linked to prior public materials archived on Zenodo: Palade, I. (2026). The Rhythmic Architecture of Mind (R.A.M.): A Rhythm-Based Cognitive Architecture for Decision-Making and Human-AI Interaction (v1.0). https://doi.org/10.5281/zenodo.18198847 Palade, I. (2026). R.A.M. A Meta-Cognitive Alignment Architecture for Reducing Cognitive Friction in Human and Human-AI Interaction. https://doi.org/10.5281/zenodo.18803288 This registration refers specifically to the empirical online protocol and analysis plan for Cognitive Decision Study (v1.0).