Cognitive Control & Skill Automation

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Abstractions about how cognitive processes govern skilled and automatic behavior, including architectures and dual-process models of cognition (ACT-R, dual-process theory), costs and failures of switching or over-monitoring skilled performance (task-switching cost, Humphrey's law, initiative loss), and learning approaches that transfer skill from experience (apprenticeship learning, model-free reinforcement learning, automation).

9 abstractions in this family — domain-specific abstractions that sit near one another in structural-signature space (k-means over structural-signature embeddings). Each is shown with its short description.

• ACT-R — A cognitive architecture that explains and predicts human task behavior by coordinating specialized modules through limited-capacity buffers and production rules over declarative chunks.
• Apprenticeship learning — Apprenticeship learning infers a policy, reward function, or task representation from expert demonstrations so an agent can reproduce or generalize expert behavior without receiving an explicit reward specification for every action.
• Automation — The engineered transfer of sensing, decision, sequencing, or actuation from continuing human performance to a technical or sociotechnical system operating under predetermined criteria and bounded oversight.
• Dual-process Theory — Models cognition as the interaction of a fast, automatic, associative process (System 1) supplying default responses and a slow, effortful, rule-based process (System 2) selectively recruited to endorse or override them under conflict, novelty, or stakes.
• Humphrey's Law — Consciously attending to a well-practiced automatic skill degrades it — declarative monitoring reinvested into a proceduralized pathway fragments and slows execution, which is why the expert chokes while the novice, who has no such pathway, does not.
• Initiative Loss — The failure mode in which an actor's decision cycle becomes structurally subordinated to an opponent's tempo, so every move is a forced reaction — a trap that faster reacting only deepens, recoverable only by changing the move-space.
• Model-Free Reinforcement Learning — Reinforcement learning that improves a policy or value estimate directly from sampled interaction without first learning an explicit transition-and-reward model for planning.
• Task-Switching Cost — Isolate the performance penalty paid at the moment of changing between rule-sets — separate from either task's steady-state difficulty — by contrasting switch trials against repeat trials in the same mixed block, and split it into a preparation-reducible part and an irreducible residual.
• Tetris Effect — The involuntary persistence of patterns from a recently, intensively practised activity into perception, imagery, and dreams — memory consolidation replaying strongly encoded content through a non-declarative pathway as a side-channel of skill learning.