Lateralized Neural Valuation of Urban Ground-Layer Vegetation Informs Evidence-Based Planning Thresholds

Urban ground-layer vegetation dominates eye-level urban experiences, yet planners lack evidence-based thresholds to optimize its restorative potential amid global city densification. Here, we pioneer a neuro-design paradigm using immersive virtual reality (VR) coupled with functional near-infrared spectroscopy (fNIRS) to unravel how groundcover attributes i.e. type (spontaneous, lawn, meadow), diversity (low vs. high), color richness (single- vs. multicolor), and height (≤ 30 cm vs. >30 cm) shape aesthetic valuation in a full-factorial set of 24 scenes viewed by 40 adults. Spontaneous types led preferences, amplified by high-diversity, multicolor, > 30 cm synergies that elevated ratings by ΔM = 0.85 (21% on a 1–5 scale; P < 0.001). fNIRS uncovered a striking lateralized neural code: right visual-prefrontal activation surged with preference, while left visual-association cortex showed inverse coupling, enabling a multivariate model (PLSR R²=0.252) to predict outcomes from cortical dynamics. Translating these signatures, we derive immediately actionable thresholds (≥ 3 dominant species m⁻², ≥ 3 seasonal color families, and > 30 cm low-stratum near paths) to foster legible, micro-salient greening. By linking perceptual fluency to auditable KPIs, this brain-to-policy framework empowers equitable, neuro-informed urban landscapes that enhance well-being and biodiversity.