Learning under noisy supervision is governed by a feedback-truth gap
arXiv:2602.16829v1 Announce Type: new Abstract: When feedback is absorbed faster than task structure can be evaluated, the learner will favor feedback over truth. A two-timescale model shows this feedback-truth gap is inevitable whenever the two rates differ and vanishes only when they match. We test this prediction across neural networks trained with noisy labels (30 datasets, 2,700 runs), human probabilistic reversal learning (N = 292), and human reward/punishment learning with concurrent EEG (N = 25). In each system, truth is defined operationally: held-out labels, the objectively correct option, or the participant's pre-feedback expectation - the only non-circular reference decodable from post-feedback EEG. The gap appeared universally but was regulated differently: dense networks accumulated it as memorization; sparse-residual scaffolding suppressed it; humans generated transient over-commitment that was actively recovered. Neural over-commitment (~0.04-0.10) was amplified tenfol
arXiv:2602.16829v1 Announce Type: new Abstract: When feedback is absorbed faster than task structure can be evaluated, the learner will favor feedback over truth. A two-timescale model shows this feedback-truth gap is inevitable whenever the two rates differ and vanishes only when they match. We test this prediction across neural networks trained with noisy labels (30 datasets, 2,700 runs), human probabilistic reversal learning (N = 292), and human reward/punishment learning with concurrent EEG (N = 25). In each system, truth is defined operationally: held-out labels, the objectively correct option, or the participant's pre-feedback expectation - the only non-circular reference decodable from post-feedback EEG. The gap appeared universally but was regulated differently: dense networks accumulated it as memorization; sparse-residual scaffolding suppressed it; humans generated transient over-commitment that was actively recovered. Neural over-commitment (~0.04-0.10) was amplified tenfold into behavioral commitment (d = 3.3-3.9). The gap is a fundamental constraint on learning under noisy supervision; its consequences depend on the regulation each system employs.
Executive Summary
This article presents a groundbreaking study on the inevitability of a 'feedback-truth gap' in learning systems, particularly when feedback is absorbed faster than task structure can be evaluated. The researchers employed a two-timescale model, which predicted that this gap is unavoidable when the rates of feedback absorption and task evaluation differ. The study tested this prediction across diverse systems, including neural networks, human probabilistic reversal learning, and human reward/punishment learning with concurrent EEG. The findings suggest that the feedback-truth gap is a fundamental constraint on learning under noisy supervision, with its consequences depending on the regulation employed by each system. The study's results have significant implications for the development of learning algorithms and the understanding of human learning processes.
Key Points
- ▸ The 'feedback-truth gap' is a universal phenomenon in learning systems when feedback is absorbed faster than task structure can be evaluated.
- ▸ The gap is inevitable when the rates of feedback absorption and task evaluation differ.
- ▸ The regulation of the gap depends on the learning system, with different systems employing distinct mechanisms to mitigate its effects.
Merits
Theoretical significance
The study presents a novel theoretical framework that explains the inevitability of the feedback-truth gap, providing a fundamental understanding of learning under noisy supervision.
Methodological rigor
The study employs a robust methodology, testing the prediction across diverse systems, including neural networks, human probabilistic reversal learning, and human reward/punishment learning with concurrent EEG.
Demerits
Limited scope
The study's focus on the feedback-truth gap may limit its scope, as other factors influencing learning under noisy supervision may be overlooked.
Need for further experimentation
Replication and extension of the study's findings in different contexts and with varied populations are necessary to confirm its generalizability.
Expert Commentary
The study's findings have significant implications for the development of learning algorithms and the understanding of human learning processes. The feedback-truth gap is a fundamental constraint on learning under noisy supervision, and its consequences depend on the regulation employed by each system. The study's results suggest that learning algorithms should be designed to account for this gap, incorporating mechanisms to mitigate its effects. Furthermore, the study's findings have policy implications, particularly in situations where noisy supervision is inevitable. The development of policies and guidelines for the use of learning algorithms in real-world applications requires a deep understanding of the feedback-truth gap and its consequences.
Recommendations
- ✓ Future studies should investigate the feedback-truth gap in different contexts and with varied populations to confirm its generalizability.
- ✓ Developing learning algorithms that can effectively learn from noisy labels and mitigate the effects of the feedback-truth gap is essential for real-world applications.
