Surgical Cognitive Interpreter (SCI): A Closed-Loop Metacognitive Controller for Regulating Interpretive Effort and Safety in Deep Learning Systems

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@article{1724919,
      author = {Dilip Sharma},
      title = {Surgical Cognitive Interpreter (SCI): A Closed-Loop Metacognitive Controller for Regulating Interpretive Effort and Safety in Deep Learning Systems},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {6},
      number = {7},
      pages = {630-639},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1724919.pdf},
      abstract = {Modern deep learning systems are typically deployed as open-loop function approximators: they map inputs to outputs in a single pass, without regulating how much computation or explanatory effort is spent on a given case. In safety-critical settings this is brittle: easy and ambiguous inputs receive identical processing, and uncertainty is only read off retrospectively from raw probabilities. This paper introduces the Surgical Cognitive Interpreter (SCI), a closed-loop metacognitive controller layer that wraps an existing stochastic model and turns prediction into a closed-loop process. SCI monitors a scalar interpretive state SP(t), here instantiated as a normalized entropy-based confidence signal, and adaptively decides whether to stop, continue sampling, or abstain. The objective is not to boost accuracy directly, but to regulate interpretive error ∆SP and expose a safety signal that tracks when the underlying model is likely to fail. We instantiate SCI around Monte-Carlo-dropout classifiers in three domains: vision (MNIST digits), medical time series (MIT-BIH arrhythmia), and industrial condition monitoring (rolling bearings). In all cases, the controller allocates more inference steps to misclassified inputs than to correct ones (up to ≈ 3−4× on MNIST and bearings, and 1.4× on MIT-BIH). The resulting ∆SP acts as a usable safety signal for detecting misclassifications (AUROC 0.63 on MNIST, 0.70 on MIT-BIH, 0.86 on bearings), and on MIT-BIH a simple risk-coverage sweep that rejects high-∆SP cases halves the error rate (from 13.22% to 6.24%). These results show that even simple SCI controllers can endow existing models with a basic form of metacognitive regulation: easy cases are handled cheaply, difficult cases trigger longer deliberation, and the same mechanism yields an explicit notion of ”I am unsure” that downstream systems can act on. This work provides a lightweight, model-agnostic pathway toward safer deployment of deep learning in high-stakes environments.},
      keywords = {Closed-loop inference, interpretability, metacognitive control, Monte-Carlo dropout, risk-aware AI, safety signal, uncertainty estimation},
      month = {January},
      doi = {https://doi.org/10.64388/ire1724919}
  }