Table 2: General linear models reported in this article.

From: Hippocampal and prefrontal processing of network topology to simulate the future

ModelTime periodModulatory parametersTableFigure
1Street Entrydegree centralityS4 
2Street Entry[Δdegree centrality]S23
3Street Entrybetweenness centralityS4 
4Street Entrycloseness centralityS4 
5Street Entry[Δbetweenness centrality]S2 
6Street Entry[Δcloseness centrality]S2 
7Street Entry[Δdegree centrality]
[Δbetweenness centrality]
[Δcloseness centrality]
S3S2–S4
8Street Entry[Δdegree centrality]
POI]*
 S5
9Travel Period Events[Δdegree centrality] 4
10Decision Points[Δdegree centrality] 4
11Street Entry[Δdegree centrality]
[Δpath distance at detours]
S9S6
12Street Entry[Δdegree centrality] S7
13Street EntryBFS for degree centrality 5
14Street EntryBFS for betweenness centrality 5
15Street EntryBFS for closeness centrality 5
  1. BFS, breadth-first search.
  2. General linear models indicate the time point of the event (time period, see Table 1), the modulatory parameters and their reference to tables and figures in the main manuscript and supplementary documents.
  3. Models 1 and 2 were conducted to examine our main question of interest. Subsequent models were control analyses conducted to determine the specificity. Δparam refers to change of value between previous segment and current segment (value at current segment minus value at previous segment). [Δparam] refers to categorical change of param with −1 for Δparam<0, 0 for Δparam=0 and 1 for Δparam>0.
  4. *POI refers to other parameters of interest: visible junction, visible connecting street, path distance, Euclidean distance to goal, step depth to goal, step depth to boundary, light of sight, street width, street length, number of visible people, number of visible vehicles and number of visible shops.
  5. For this model events in which [Δparam]=0 was excluded. This was conducted as a follow-up to our behavioural experiment, see Methods.