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新论文 · v1arXiv:2611.13691

Seeing Without Seeing: A Methodological Review of Visual Mental Imagery Reconstruction from Brain Activity

Shiyi Zhang, Dong Liang, Yihang Zhou

原文摘要

Reconstructing visual mental imagery from brain activity differs from reconstructing perceived images. Imagery is generated top-down, its signal is weak, and the imagined content is never observed, so decoders are trained and scored against a proxy such as a cue or a seen exemplar. This criterion-guided methodological review of mainly non-invasive studies (fMRI, EEG, MEG) is narrative rather than systematic. We organise the field into a two-stage pipeline, two design axes and four method families, and formalise target-free evaluation, in which the proxy gap is a systematic bias rather than noise. A two-dimensional admissibility criterion crosses trial-level separability from working-memory maintenance with the memory source of the imagined content. Among the studies reviewed, every image-scored reconstruction concerns a recalled exemplar, none builds a scorable target for concept-generated content, and all come from fMRI, consistent with missing image-level targets in electrophysiological data. Published values for five decoders, compared under a common human forced-choice framing, show seen-to-imagined drops of 5.9 to 26.1 percentage points and a change in method ordering. We treat this as a diagnostic signal, not a controlled benchmark result, and specify a within-subject test for an altered neural code. Beyond this synthesis, we make two proposals. First, because frozen generative priors can produce photorealistic images weakly tied to neural signals, we combine existing null-signal controls into a normalised neural dependence with a within-category null, an unvalidated diagnostic quantity. Second, we propose a candidate ten-item reporting checklist with illustrative pass criteria. In an exploratory application to ten studies, none reports all items in a fully checkable form; this describes reporting practice, not study quality.

cs.HC

版本更新 · v2arXiv:2605.05755

Transformers Provably Implement In-Context Reinforcement Learning with Policy Improvement

Haodong Liang, Lifeng Lai

原文摘要

We investigate the ability of transformers to perform in-context reinforcement learning (ICRL), where a model must infer and execute learning algorithms from trajectory data without parameter updates. We show that a linear self-attention transformer block can provably implement policy-improvement methods, including semi-gradient SARSA and actor-critic, via explicit parameter constructions. Beyond existence, we design a teacher-mimicking training procedure, analyze its gradient-flow dynamics, and establish the first convergence guarantee in the ICRL literature: under suitable richness conditions on the training MDP distribution, gradient flow converges locally and exponentially to an optimal parameter manifold corresponding to the desired RL update. Empirically, training transformers on randomly generated tabular MDPs confirms these predictions: the learned models recover the parameter structure of our explicit constructions and, when deployed on unseen MDPs, deliver strong in-context control performance. Together, these results illuminate how transformer architectures internalize and execute classical reinforcement learning algorithms in context, bridging mechanistic understanding and training dynamics in ICRL.

stat.ML · cs.AI · cs.LG