Participants
A total of 518 cadets (89 women, 429 men), in the second year of their West Point program, completed a required large-scale navigation task to assess their wayfinding skills. A total of 186 participants completed various self-reported navigational skills and strategies assessments, from which we used the Santa Barbara Sense of Direction (SBSOD; α = .706) questionnaire (Hegarty et al., 2002). The cadets who completed the navigational strategies assessment were 66.5% (124) men (coded 1) and 33.5% (69) women (coded 0).
Procedure
In a large-scale wayfinding task, cadets located flags in rugged, wooded terrain (the same area used in Malinowski & Gillespie, 2001). Participants were given a day of training to familiarize them with the task prior to data collection, and all participants demonstrated competency with the task and the map and compass use prior to data collection. Participants navigated to five sets of latitude/longitude coordinates of flags, using a map and compass, while a screen-less GPS tracker sampled once a second for the entirety of the wayfinding task (on average, 2.37 hours).
Target locating (navigational success)
Participants were told to locate flag locations in the wooded environment using only their map and compass. There were 30 possible flag locations, each a quarter mile or more apart, with clusters of three flags that were approximately 50–100 feet apart at each location. Distance and difficulty of the terrain to reach the flags were roughly equivalent across participants. No more than three participants received the same set, in order to lower the likelihood of coordination of search patterns to aid in performance. Each course (with two exceptions) was assigned to at least one male and one female cadet, to control for differences in task difficulty across genders.
Participants marked scorecards to indicate which flag they had found. These indicators gave two levels of assessment of performance—the general ability to identify the correct flag in the correct cluster (1 point), a correct cluster but not correct flag (0.5 point), or neither (0 points), according to the coordinates given to the participant.
Measures of wayfinding behaviors
The GPS tracks were analyzed to provide different conceptualizations of how the cadets moved through the space, which taken together provides a cohesive description of cadets’ wayfinding behaviors. The development of the measures used herein (Gagnon et al., 2016; Gagnon et al., 2018) was motivated by extensive research in behavioral ecology devoted to the study and quantification of animal movement and how it relates to achieving adaptive goals like foraging for food and finding mates (Turchin, 1998). How people explore virtual environments and perform in virtual navigation tasks was well described by measures of directional persistence, pausing, and revisiting (Gagnon et al., 2016; Gagnon et al., 2018). These measures allow us to understand how dynamic wayfinding behaviors may relate to navigational success. We expect that the way in which people explore environments will be related to their navigational performance such that males exhibit more efficient and confident wayfinding behaviors (less revisiting and pausing), and these more effective wayfinding behaviors will mediate the oft-observed gender differences in navigational success. Using specialty scripts written in R, these separate measures were computed for the GPS tracks produced by the cadets.
Directional persistence
Directional persistence offers a way to extract relative distance information from a spatial layout, on a scale proportional to the length of the directional persistence. Directional persistence was calculated by multiplying the individual’s speed (change in distance over change in time) by the cosine of their turning angle. Therefore, traveling at maximum speed in a straight line would yield a directional persistence estimate of the participants’ maximum speed. Traveling at maximum speed, but making a 90° turn, would yield a directional persistence estimate of zero. Directional persistence was calculated over 1-second intervals for the entirety of each cadet’s navigation, then averaged to create an overall directional persistence score for each participant. We argue that directional persistence (as measured here) can, in part, provide an index of confidence in navigation. Further, as with the analogous measure (diffusion) in Gagnon et al. (2018), directional persistence gives spatial information about objective patterns of behavior that, along with the following two measures, may represent caution in wayfinding as compared with self-reports of caution.
Pausing
Pausing was calculated by dividing the total number of seconds spent not moving by the total time spent navigating or exploring during the task. A pause was defined as consecutive samples with the same X, Y, and Z values, and the number of consecutive samples with the same position values was used to infer the number of seconds for a given pause. The lengths (in seconds) of the pauses were summed and divided by the total number of seconds in the wayfinding trajectory.
Revisiting
To calculate cadets’ revisiting behaviors during navigation, we computed a 45-ft radius around each location along a participant’s trajectory (see Gagnon et al., 2018, for use of this measure in virtual navigation). The decision of 45 feet was based on the distance between flags at locations, the rate of movement throughout the task, and the visual density of the environment cadets were exploring. For each position in the participant’s trajectory, the euclidean distance between the current position and all other previous positions was calculated, and all positions with a euclidean distance equal to or less than 45 ft were identified. Revisiting positions were defined as any of the positions within the 45-ft radius in which the participant left and then reentered that area. Finally, the average number of revisiting positions across all samples in the wayfinding trajectory was computed, indicating the overall amount of revisiting behavior
Analysis strategy
The goals of this study are to (1) categorize how participants moved in a real-world environment during a large-scale navigation task, and (2) to determine whether the characterizations of movement patterns (directional persistence, revisiting, and pausing) relate to navigational success in the task. Using structural equations modeling (path diagram shown in Fig. 1), the wayfinding behaviors (pausing, revisiting, and directional persistence) were loaded onto a latent variable, conceptualized as a comprehensive measure of wayfinding behaviors, which was predicted by gender and predicted navigational success, consistent with mediation. We expected that men would produce on average more directional persistence, less pausing, and less revisiting than women, which in turn would account for the male advantage on navigational performance (accurately locating targets). SBSOD was correlated with gender and predicted ability to accurately locate the target, testing if self-reported navigational ability uniquely predicted target location. Self-report of comfort with navigational aids was included as a covariate, to control for potential gender and individual differences in comfort with navigational tool use. The proposed model tests the connections between self-report of navigational ability (SBSOD) and ability to locate a target, while providing behavioral quantifications of the proposed differences in how men and women move through space. Importantly, although there is a theoretical connection between production of different wayfinding behaviors and navigational success, there is no linear dependency between these measures. Participants could theoretically pause more, revisit more, and have less directional persistence, but still find the correct flag, and thus get full points for navigational success. Thus, we are testing empirically whether how cadets move throughout the environment (wayfind) is related to locating the correct target (navigational success).
Models were run in Mplus 7.31 using a maximum likelihood estimator. Model fit was assessed using CFI (>.90) and SRMR (<0.08 indicating good fit; Hu & Bentler, 1999). Missingness ranged from 9%– to 12.5%, 10 multiply imputed data files accounted for this relatively low missingness (see Graham, 2009). A significant bootstrapped indirect effect (10,000 samples) tested mediational plausibility (for meditation details, see Kenny, 2008). The data and R scripts for the experiments reported here are available by request, and the experiment was not preregistered.