Author-level subtypes

What happens when we cluster r/Aphantasia authors by the topics they actually engage with and the metaphor families they use, rather than reading sub-themes one at a time?

Methodology

• Inputs: pipeline/data/authors.parquet, pipeline/data/metaphor_matches.parquet, reddit/data/theme_tags.parquet, plus author joins from corpus.db.
• Author-level features (per author): per-theme tag rates (33 sub-themes, normalised to author's total tagged chunks), per-metaphor-family rates (12 families), comment_ratio, post_ratio, year_span. Raw contribution counts were excluded after a first run revealed they dominated clustering and produced "talkative vs quiet" clusters. The model selection CSV at reddit/data/author_cluster_summary.csv records the chosen feature set.
• Filter: authors with < 5 contributions, or with no tagged-theme/metaphor signal, are kept aside as insufficient_data rather than forced into a cluster. This is most of the corpus.
• Algorithm: K-means with k explored 3–8, k=6 chosen by inertia-elbow heuristic. The full author cluster table is at reddit/data/author_clusters.parquet.
• Inter-cluster comparison: each cluster's "top positive feature deltas" vs the population mean is in reddit/data/author_cluster_summary.csv.

This is not a clinical subtype model. It's a corpus-behavior clustering over available theme/metaphor signals. Clean clusters here mean "people who post about a particular topic a lot," not "people whose phenomenology is intrinsically X." See the caveats at the bottom.

Headline cluster sizes

(Median total contributions per cluster: cluster_00 = 13 rows, cluster_01 = 8, cluster_02 = 17, cluster_03 = 15, cluster_04 = 16, cluster_05 = 15, insufficient_data = 1.)

What the clusters actually show

1. The dominant pattern is "no clean subtype." 71.5% of authors have too little signal to classify, and another 26.4% land in two large mixed-discussion clusters (00 + 01) whose top themes overlap heavily and whose metaphor profiles are within ~5 percentage points of each other. Together these account for 97.9% of all authors. The brief's hypothesis that heterogeneity might form "2-4 recognizable clusters" is not supported at the population level.

2. Clean clusters exist, but they're tiny and topical, not phenomenological. Clusters 02–05 each have a single dominant sub-theme accounting for 80%+ of their tagged chunks:

• cluster_02 (192 authors, 0.4%) — working artists (creativity_artists 85%). The subreddit-native counter-narrative documented in outliers.md §5 has a small, stable author core. Median 17 contributions, year-span 1.4× the population baseline — these are sustained voices, not drive-by posters. They use camera/photo metaphors at 30%, the highest in any cluster, but voice/audio is also strong — the "I think in pictures and sound" register, not pure conceptual.
• cluster_03 (170 authors, 0.4%) — acquired aphantasia (acquired_after_event 83%). The fog/haze metaphor family appears at 6% — twice the rate of any other cluster — matching the metaphor inventory's finding that fog/haze is the home of degraded-imagery (hypophantasia and acquired) phenomenology. Median 14.5 contributions.
• cluster_04 (84 authors, 0.2%) — research-engaged (research_zeman 82%). Earliest median first-year (2020 vs 2022 elsewhere) — these are people who joined when the academic literature was ramping up. Notably, mind's eye metaphor rate is 8.4%, the highest of any cluster: research-engaged users use the canonical folk-technical vocabulary.
• cluster_05 (63 authors, 0.1%) — synesthesia-comorbid (comorbid_synesthesia 83%). Voice/audio metaphor rate is 31%, the highest of any cluster — consistent with multisensory framing of inner experience.

Summed, the "specialist" clusters total 509 authors (1.1% of all authors). They are real but narrow.

3. Cluster 00 vs cluster 01 is mostly a posting-style split, not a phenomenology split. Cluster 00 is comment-heavy with year-span 1.24× baseline and a wider topical spread; cluster 01 is post-heavy with year-span only 0.18× baseline and a tighter focus on dream/apple-test/image-streaming threads. Cluster 00 looks like long-tenured commenters who've explained aphantasia in many threads; cluster 01 looks like newer posters working through the discovery experience.

4. Metaphor signature is surprisingly stable across clusters. Camera/photo (~20–30%) and voice/audio (~19–31%) dominate every cluster's metaphor profile. The discriminating signal between clusters comes overwhelmingly from theme tags, not metaphor families. Even cluster_02 (artists) doesn't have a wildly different metaphor profile from cluster_00 (broad). This pushes back against any subtype claim phrased in metaphor terms.

What this answers and doesn't

Answers the brief's §7 (Heterogeneity and subtypes): do self-descriptions form 2–4 recognisable clusters, a continuum, or overlapping traits? At the corpus-behaviour level, mostly overlapping traits — with four small, highly topical author groups around acquired aphantasia, working artists, research-engagement, and synesthesia-comorbid as the only clean exceptions.

Does NOT answer: - Whether the phenomenology itself clusters. We're clustering posting behaviour, not inner experience. Two authors with identical phenomenology might post about it differently. - Whether the insufficient_data 71.5% — mostly one-time posters — would resolve into clean subtypes if surveyed directly. They might. They're invisible to this method. - Whether the four small specialist clusters contain phenomenological homogeneity, or just topical specialisation. A user who posts mostly about acquired aphantasia might still have any of the four LCD-phenomenology labels from lcd_phenomenology.md. Those signals were not jointly analysed here. - Whether longitudinal authors change cluster over time. The model treats each author as a single feature vector aggregated across all their contributions.

Implications

1. The brief's "five-component model" hypothesis — that aphantasic cognition divides into externalisation / semantic recall / procedural comfort / non-visual sense / loss — does not show up as a clean axis here. Across 13,118 clustered authors, the dominant pattern is mixed discussion of a wide topic space, not the specialisation a five-component model would predict.

2. The clean clusters that do exist (artists, acquired, research-engaged, synesthesia) are best described as identity / situation specialists, not as cognitive subtypes. They post a lot about one thing, not because their cognition is fundamentally different from cluster_00's, but because their identity-relevant topic is narrower.

3. The right next step for subtype work would be a smaller, deeper retrieval pass over cluster_03 (acquired) and cluster_05 (synesthesia) — those are the clusters whose specialisation might actually correlate with distinct phenomenology. Cluster_02 (artists) is identity-driven; cluster_04 (research) is engagement-driven; neither is intrinsically a phenomenological subtype.

See also

• reddit/data/author_clusters.parquet — full author-cluster mapping.
• reddit/data/author_cluster_summary.csv — cluster summary table this file draws from.
• reddit/data/author_cluster_model_selection.csv — k-selection diagnostics.
• reddit/outliers.md — manual outlier categorisation; cluster_02 (artists) overlaps cleanly with outliers §5; cluster_03 (acquired) overlaps with outliers §6.
• reddit/lcd_phenomenology.md — the four-label phenomenology classifier; not jointly analysed with these clusters yet.