Natural environments are widely associated with cognitive restoration. Existing accounts, including Attention Restoration Theory, emphasize phenomenological qualities such as fascination and coherence, but offer limited formalization of how environmental information is structured at level of semantic representation. This study introduces semantic complexity as a computationally tractable framework for characterizing environmental meaning. Using an image dataset (MIT LabelMe, approximately 1,000 images, including 500 natural and 500 urban scenes), object annotations are mapped to WordNet synsets to construct scene-level semantic representations. I derive multiple structural metrics, including entropy, branching, and hierarchical depth, to quantify distinct dimensions of semantic organization, and conduct group comparisons alongside normalization procedures to distinguish structural complexity from object-count effects. Contrary to a unidimensional expectation of reduced complexity in natural scenes, the results reveal a systematic dissociation: urban environments exhibit higher semantic density (more objects and categories), whereas natural environments exhibit greater semantic dispersion (higher branching and per-object informational richness). Critically, differences in raw entropy are fully accounted for by object count, indicating that urban scenes are not intrinsically more complex in their semantic structure, but rather more crowded. These findings suggest that semantic complexity is multidimensional, comprising at least density and structural distribution, and that these dimensions may have distinct implications for cognitive processing. By formalizing environmental semantics at scale, this study offers a computational approach to environmental cognition and refines restoration theory by demonstrating that the organization of semantic information shapes cognitive experience.
Stanley Yi (Mon,) studied this question.