## 3 Results
### Network Semantic Analysis  
We modeled the responses of the pareidolic task for each condition in a group-level semantic network as described before. This resulted in an unweighted, undirected network from which we built the semantic networks (see Figure 3). The organization of the semantic network has been analyzed using three different topological quantifiers: Modularity (Q), Clustering Coefficient (CC), and Average Shortage Path Length (ASPL).  
Figure 2: Bar plot of word frequencies showing the 40 most common pareidolic stimuli identified by participants in two experimental conditions: Virtual cave (**top panel**) and open space (**bottom panel**). Translations of responses (left to right) in the **top panel**: face, man, eye, hand, horse, dog, fish, bird dragon, nose, skull, heart, woman, snake, letter, wolf, turtle, eagle, hat, dinosaur, cat, mask, skeleton, mouth, rabbit, flower, leaf, lion, statue, mouse tree, whale, child, dolphin, drawing, pig, monster, bear, bone, shark. **Bottom panel**: face, fish, man, eye, bird, dog, skull, hand, mask, bat, shark, mouth, horse, cat, woman, dragon, nose, snake, tree, leaf, letter, wolf, mountain, heart, dinosaur, flower, arrow, ape, crocodile, demon, turtle, animal, eagle, drawing, butterfly, lion, glasses, bone, foot, whale.  
The results show (qualitative and quantitative) differences between the semantic networks obtained by the pareidolic task in the two different conditions (virtual cave vs. open space, see Figure 4). Quantitatively, we found significative differences between the aforementioned quantifiers, with a shorter ASPL, a smaller Q, and a lower CC in the cave (ASPL = 3.36, Q = 0.65, CC = 0.38) compared to the open space condition (ASPL = 3.43, Q = 0.66, CC = 0.40). The difference was statistically significant in all three topological quantifiers measured using the LONO procedure, while, using the LOSO procedure, the difference in Modularity only reached statistical significance (See Table 1).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Measures**} & \multicolumn{4}{c}{**LONO**} & \multicolumn{4}{c}{**LOSO**} \\ \cline{2-9}  & **Cave** & **Open** & \multirow{2}{*}{**\(p\)**} & **d** & **Cave** & **Open** & \multirow{2}{*}{**\(p\)**} & **d** \\ \cline{2-9}  & **M (SD)** & **M (SD)** & & & **M (SD)** & & **M (SD)** & \\ \hline ASPL & 3.355 & 3.414 & \textless{}0.0001 & 1.25 & 3.418 & 3.412 & 0.82 & 0.05 \\ CC & 0.383 & 0.397 & \textless{}0.00001 & 2.80 & 0.389 & 0.391 & 0.36 & 0.22 \\ Q & 0.652 & 0.663 & \textless{}0.00001 & 1.43 & 0.649 & 0.656 & 0.03 & 0.55 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of the permutation \(t\)-test in cave and open space VR environments. LONO: leave-one-node-out; LOSO: leave-one-subject-out; ASPL: average shortest path length; CC: clustering coefficient; Q: modularity index. \(p\) = \(p\)-value, d = Cohen’s d effect size: 0.2, small; 0.5, moderate; 0.8, large; 1.1, very large.  
Figure 3: Semantic networks statistical results and topological quantifiers. (**A**) Spring layout of the semantic networks, undirected and unweighted, of the open space and cave conditions, with nodes as pareidolic unique responses and edges as cosine similarity; (**B**) Raincloud plots of the statistical results of LONO and LOSO procedures on the topological quantifiers; and (**C**) Bar plots representing the topological quantifiers: in blue is represented open space, and in orange is represented the virtual cave.