This visualization places Linear A — the undeciphered writing system of Minoan Crete (~1800–1450 BCE) — at the centre of a 1,926-node phylogenetic tree of the world’s languages. A neural phonetic model was trained against each of 16 candidate languages, producing a raw closeness score measuring phonetic similarity. Those scores are encoded as pulsing fiber-optic beams connecting the central Linear A cluster to each candidate’s position in the tree.
The PhaiPhon model (adapted from Luo et al. 2021) learns character-level sound correspondences between an unknown script and a known language using differentiable dynamic programming. It is fully unsupervised — no translations or cognate labels are used during training. For each candidate language, the model produces a closeness score representing how well Linear A’s phonetic patterns can be mapped to that language’s sound system.
All closeness-based statistics (z-scores, p-values, Bayes factors) shown in tooltips use a placeholder null distribution (μ=0.05, σ=0.03) that is far too tight. Under this null, all 16 candidates — including obvious negative controls — appear significant. A preliminary recalibration with 4 negative controls (μ=0.253, σ=0.040) found no candidate significant after Bonferroni correction. Raw closeness values are valid relative to each other but their statistical significance cannot be assessed until a proper null calibration is completed.
The one finding independent of null calibration: a chi-squared test of root–suffix vowel co-occurrence shows statistically significant vowel harmony in Linear A (χ²=50.2, p=0.000021, Cramér’s V=0.210). This is a moderate effect from a small corpus (48 inscriptions) and should be interpreted cautiously.