aiqbench analysis
This research introduces the Perceptual Reality Transformer (PRT), a framework designed to help AI models accurately represent complex, subjective perceptual experiences beyond simple visual recognition. By analyzing neural representations and generated illustrations alongside original accounts, the study highlights critical limitations in current metrics for capturing nuances like vividness and emotion without altering meaning. The findings underscore the need for better evaluation criteria that distinguish between missing information and inaccessible semantic meaning in generative AI systems.
Perceptual Reality Transformer: What Must an Illustration Preserve?
arXiv:2508.09852v2 Announce Type: replace-cross Abstract: How can models help people communicate unusual perceptual experiences without changing what they mean? A recognizable image is only part of the answer: accounts also express vividness, duration, uncertainty, and emotion. We introduce Perceptual Reality Transformer (PRT), an evidence-linked workflow and descriptive atlas that retain the source account alongside representations and generated illustrations. Three studies examine successive parts of this transformation. In 3,145 public Ganzflicker records, frozen neural representations predict complex content well (AUROC 0.887--0.894), but vividness, persistence, and unpleasantness are less recoverable; separate imagery/context responses improve vividness prediction relative to narrative embeddings alone. In 672 synthetic contrasts, similarity judgments depend strongly on wording, while exploratory probes recover some distinctions that cosine handles poorly. Finally, 72 matched illustrations from 24 reports compare direct prompting, evidence in prose, and structured PRT. The automated coverage metric does not demonstrate a structured-format advantage; near-ceiling scores, an apparent judge error, and an analogy-to-object scoring ambiguity limit its interpretation. Together, the studies motivate separating missing information, inaccessible meaning, and an inadequate evaluation criterion. The resulting atlas makes these choices inspectable, without treating generated images as measurements of private perception.
Abstract:How can models help people communicate unusual perceptual experiences without changing what they mean? A recognizable image is only part of the answer: accounts also express vividness, duration, uncertainty, and emotion. We introduce Perceptual Reality Transformer (PRT), an evidence-linked workflow and descriptive atlas that retain the source account alongside representations and generated illustrations. Three studies examine successive parts of this transformation. In 3,145 public Ganzflicker records, frozen neural representations predict complex content well (AUROC 0.887--0.894), but vividness, persistence, and unpleasantness are less recoverable; separate imagery/context responses improve vividness prediction relative to narrative embeddings alone. In 672 synthetic contrasts, similarity judgments depend strongly on wording, while exploratory probes recover some distinctions that cosine handles poorly. Finally, 72 matched illustrations from 24 reports compare direct prompting, evidence in prose, and structured PRT. The automated coverage metric does not demonstrate a structured-format advantage; near-ceiling scores, an apparent judge error, and an analogy-to-object scoring ambiguity limit its interpretation. Together, the studies motivate separating missing information, inaccessible meaning, and an inadequate evaluation criterion. The resulting atlas makes these choices inspectable, without treating generated images as measurements of private perception.
Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Neural and Evolutionary Computing (cs.NE)
arXiv:2508.09852 [q-bio.NC]
(or arXiv:2508.09852v2 [q-bio.NC] for this version)
Full text under the publisher's open licence · 210 words
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