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| Main Author: | |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.23382 |
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| _version_ | 1866918359055990784 |
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| author | Walsh, Mark |
| author_facet | Walsh, Mark |
| contents | A central prediction of the accompanying theoretical framework is that metacognitive calibration can vary even when content-level performance is held approximately fixed, depending on whether support structure is preserved in a globally reusable broadcast state. We provide a minimal computational test of this claim using a two-channel probabilistic cue-integration task with regime shifts that induce systematic miscalibration in one channel. We compare content-dominated architectures, in which confidence is calibrated by a single global mapping from evidence strength to probability, to an auditor architecture that learns a regime-conditioned calibration mapping from an audit trail of outcomes. We then couple confidence to control by implementing a policy that either acts immediately or requests one additional sample when confidence falls below a threshold. Across matched evidence streams, the auditor substantially improves calibration, particularly in the degraded regime, and produces qualitatively different control behavior by selectively requesting additional evidence under low-support conditions. These results demonstrate a concrete, testable dissociation between content performance and system-level confidence and policy that arises from globally reusable support summaries. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_23382 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Audited calibration under regime shift as a computational test of support-structured broadcast Walsh, Mark Neurons and Cognition A central prediction of the accompanying theoretical framework is that metacognitive calibration can vary even when content-level performance is held approximately fixed, depending on whether support structure is preserved in a globally reusable broadcast state. We provide a minimal computational test of this claim using a two-channel probabilistic cue-integration task with regime shifts that induce systematic miscalibration in one channel. We compare content-dominated architectures, in which confidence is calibrated by a single global mapping from evidence strength to probability, to an auditor architecture that learns a regime-conditioned calibration mapping from an audit trail of outcomes. We then couple confidence to control by implementing a policy that either acts immediately or requests one additional sample when confidence falls below a threshold. Across matched evidence streams, the auditor substantially improves calibration, particularly in the degraded regime, and produces qualitatively different control behavior by selectively requesting additional evidence under low-support conditions. These results demonstrate a concrete, testable dissociation between content performance and system-level confidence and policy that arises from globally reusable support summaries. |
| title | Audited calibration under regime shift as a computational test of support-structured broadcast |
| topic | Neurons and Cognition |
| url | https://arxiv.org/abs/2602.23382 |