 Figure 1: Paper surveys consisting of the above photograph and the instructions: (1) "Please draw an "x" at the center of the subject of this image" and (2) "write down a few words to describe it." |
 Figure 2: Scatter plot of locations marked by human subjects normalized to photograph dimensions. |
 Figure 3: Histogram of the horizontal components of the positions chosen by human subjects plot of locations (normalized by photograph width).  Figure 4: Histogram of the vertical components of the positions chosen by human subjects plot of locations (normalized by photograph height). |
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K-means cluster analysis performed on the normalized horizontal and vertical location data found three significant spatial taxons (x: F(2,104)=146, p < .001 and y: F=(2,104) = 69.77 p <.001). To help the reader visualize the resulting spatial taxons, they were segmented into separate layers. Scatter plots of the location data for each spatial taxon were overlaid on top of the spatial taxon regions. The bottom layer shows the original image overlaid with a scatter plot of all the data. The cumulative term count for each spatial taxon was not significant. However, when word order was taken into consideration, the cumulative word count was significant for the moon taxon and (df=2, x2=18.52, p<.01 and heart taxon(df=2,x2 23.59 p<.01), but not frame taxon. 
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 Figure 6: Frequency of subject locations classified in a spatial taxon as a function of the rank of that spatial taxon plotted in log-log coordinates. A rank of one, refers to the most common spatial taxon. We chose a log-log coordinate system for easy comparison against the word frequency vs. rank plots shown below. If the visual system’s spatial taxon structure within scenes is information normative with respect to word usage within human natural language, one would expect these curves to be similar. | |
 Figure 8: As explained above, k-means cluster analysis separated the data into spatial taxons. To help the reader visualize how our operational definition of spatial taxon applies to the data, the visual taxons are shown as layers. Scatter plots of the location data for each spatial taxon are overlaid on top of the spatial taxon regions. The bottom layer shows the original image overlaid with a scatter plot of all the data. |
 Figure 9: As explained above, k-means cluster analysis separated the data into spatial taxons based on location. For each survey included in the cluster, the words collected by the second survey question were counted and ranked according to the procedure first introduced by George Zipf (Zipf, George Kingsley (1932): Selected Studies of the Principle of Relative Frequency in Language. Cambridge (Mass). The trend line equations and R-squared statistic are shown in the upper right corners. Though power-law functions provide a good fit for some of the clusters, they do not provide a good fit for others. Check back here for a follow-up analysis. | |
