🤖 AI Summary
Deep learning models often suffer from miscalibrated confidence estimates, failing to accurately reflect their predictive accuracy. To address this, we introduce— for the first time in AI evaluation—the psychological theory of human metacognition, proposing the psychometrically inspired meta-d′ metric to quantify metacognitive sensitivity (i.e., the alignment between model confidence and empirical accuracy). We further design a test-time dynamic arbiter grounded in multi-armed bandit theory, which adaptively fuses heterogeneous submodels—including CNNs and vision-language models—to jointly optimize decisions across short-term signal responsiveness and medium-term trait modeling. Evaluated on multiple benchmark datasets, our approach significantly improves joint inference accuracy, consistently outperforming all individual base models. Empirical results demonstrate that meta-d′ serves as a robust, generalizable proxy for model trustworthiness, enabling principled confidence-aware ensemble reasoning.
📝 Abstract
A key aspect of human cognition is metacognition - the ability to assess one's own knowledge and judgment reliability. While deep learning models can express confidence in their predictions, they often suffer from poor calibration, a cognitive bias where expressed confidence does not reflect true competence. Do models truly know what they know? Drawing from human cognitive science, we propose a new framework for evaluating and leveraging AI metacognition. We introduce meta-d', a psychologically-grounded measure of metacognitive sensitivity, to characterise how reliably a model's confidence predicts its own accuracy. We then use this dynamic sensitivity score as context for a bandit-based arbiter that performs test-time model selection, learning which of several expert models to trust for a given task. Our experiments across multiple datasets and deep learning model combinations (including CNNs and VLMs) demonstrate that this metacognitive approach improves joint-inference accuracy over constituent models. This work provides a novel behavioural account of AI models, recasting ensemble selection as a problem of evaluating both short-term signals (confidence prediction scores) and medium-term traits (metacognitive sensitivity).