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Active route learning in virtual environments: disentangling movement control from intention, instruction specificity, and navigation control

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Abstract

Active navigation research examines how physiological and psychological involvement in navigation benefits spatial learning. However, existing conceptualizations of active navigation comprise separable, distinct factors. This research disentangles the contributions of movement control (i.e., self-contained vs. observed movement) as a central factor from learning intention (Experiment 1), instruction specificity and instruction control (Experiment 2), as well as navigation control (Experiment 3) to spatial learning in virtual environments. We tested the effects of these factors on landmark recognition (landmark knowledge), tour-integration and route navigation (route knowledge). Our findings suggest that movement control leads to robust advantages in landmark knowledge as compared to observed movement. Advantages in route knowledge do not depend on learning intention, but on the need to elaborate spatial information. Whenever the necessary level of elaboration is assured for observed movement, too, the development of route knowledge is not inferior to that for self-contained movement.

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Notes

  1. This computation was chosen for continuity reasons throughout this research (i.e., the tour integration task used in Experiments 2–3). However, it does not exclude the possibility that a participant can first draw one wrong turn followed by correct turns, which results in a potential underestimation of this participant’s performance. We considered several alternative computation approaches, for example, to count whether two subsequent turns were correctly indicated (e.g., the original route included first a left turn, followed by a right turn. A correct indication of those turns would account one point). However, the overall quality of the sketched paths was rather low, so that it was impossible to determine which drawn turn corresponded to which turn of the original route. Due to the difficulties to establish a meaningful objective scoring criterion, we decided to rate subjectively how similar the sketched path was as compared to the original route (ranging from 1 = no similarity to 5 = absolute congruence; M = 2.23, SD = .86). The same ANOVA as reported in the results section showed no significant effects, all Fs < 1.18, ns. We conclude that participants of all experimental conditions were unable to deduce the cross-shape of the route from their egocentric encounter of the environment. In other words, they did not develop a cognitive map of the environment.

  2. There was an interaction of intention and landmark relevance, F(1,78) = 4.49, p < 0.05, η 2 = 0.05, indicating that intentional learning more strongly affected memory for landmarks passed straight than for those indicating turns. We refrain from further interpretation of this interaction, however, because it is of little interest for active navigation effects.

  3. Similar tasks have been used as measures of survey knowledge (van Asselen et al., 2006), which correlated with established survey knowledge tasks such as landmark pointing (von Stülpnagel & Steffens, 2012). However, the tour-integration task resembles a route knowledge task in many regards, as it can be solved by recalling the consecutive order of differently shaped rooms. Thus, in the present experimental setting, an advantage in tour integration performance represents an advantage in the ability to reconstruct a specific route (i.e., route knowledge) rather than in development of a cognitive map (i.e., survey knowledge).

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Acknowledgments

The writing of this article was supported by grants from the Deutsche Forschungsgemeinschaft (German Science Foundation, Ste 938/7-1, 7-2). We are indebted to Sascha Poppitz for his support in planning and preparing the experiments. We also thank Eryk Noji, Karsta Sporbert, Peter Teichert, Martin Weber, Linda Köhler, Nele Fischer, Dave Cromm, and Linda Seidemann for help with data collection, and Janette Schult and Alaina Brenick for valuable comments on previous drafts of the manuscript.

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  1. Institut für Psychologie, Friedrich Schiller University Jena, Am Steiger 3, Haus 1, 07743, Jena, Germany

    Rul von Stülpnagel & Melanie C. Steffens

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  1. Rul von Stülpnagel
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Correspondence to Rul von Stülpnagel.

Appendix

Appendix

Experiment 1

There is evidence for gender differences in spatial abilities and orientation strategies, with men performing better than women (Iachini, Ruotolo, & Ruggiero, 2009; Lawton, 1994; Pazzaglia & De Beni, 2001, but see Rossano & Reardon, 1999). A meta-analysis showed that gender differences are found only in about 50 % of studies on spatial abilities (Coluccia & Louse, 2004). We checked for potential confounds of participants gender with separate one-way (female vs. male) ANOVAs for the self-reports of sense of orientation and computer game experience, as well as all dependent variables. Women (M = 2.77, SD = 0.18) rated their sense of orientation lower than men (M = 3.41, SD = 0.18), F(1,79) = 6.48, p < 0.02, \( \eta_{\text{p}}^{2} \) = 0.08. Women (M = 1.95, SD = 0.21) also reported less computer game experience than men (M = 3.09, SD = 0.20), F(1,81) = 15.72, p < 0.001, \( \eta_{\text{p}}^{2} \) = 0.16. These differences did not affect the dependent variables in general, as there were no gender differences for number of hits, pointing accuracy, and path-sketching, all Fs < 3.61, ns. However, women (M = .10, SD = 0.11) made more false alarms than men (M = 0.05, SD = 0.07), F(1,77) = 4.45, p < 0.01, \( \eta_{\text{p}}^{2} \) = 0.06. Additionally, men (M = 94 s, SD = 39) were faster in the route navigation task than women (M = 126 s, SD = 55), F(1,76) = 6.19, p < 0.001, \( \eta_{\text{p}}^{2} \) = 0.08. Gender differences were not the primary focus of the present research, and we thus refrain from further interpretations of these effects. An exploratory inclusion of participant gender as an additional factor did not change the result patterns. Consequently, we abandoned a balanced proportion of participant gender in the Experiments 2–3, and distributed male participants around equally over the experimental conditions.

We also checked for potential confounds of sense of orientation and computer game experience, as indicated by the items “How good is your general sense of orientation?” (M = 3.10, SD = 1.17) and “How often do you play computer games?” (M = 2.54, SD = 1.43) with two separate 2 (movement control) × 2 (learning intention) ANOVAs. General sense of orientation and computer game experience did not differ between the experimental groups, all Fs < 1.32, ns. A correlation analysis of these items with all dependent variables (landmark recognition hits and false alarms, pointing accuracy, path-sketching performance, and route navigation performance) revealed significant correlations of general sense of orientation with the number of recognition false alarms (r = 0.24, p = 0.03) and with route navigation performance (r = 0.28, p = 0.01), as well as significant correlations of computer game experience with the number of recognition hits (r = 0.24, p = 0.03) and with route navigation performance (r = 0.27, p = 0.02). To account for these correlations, we included sense of orientation and computer game experience as covariates in the respective analyses after linear relationships between covariates and dependent variables as well as homogeneity of regressions were tested and confirmed. Statistical data regarding the covariates of this and all following experiments are reported in Table 4.

Table 4 Statistical data of all covariates, separately presented for the respective dependent variables and experiments
Full size table

Experiment 2

As a 2 (movement control) × 2 (instruction control) × 2 (instruction specificity) ANOVA showed, computer game experience (M = 1.18, SD = 0.92) did not differ between the experimental groups, all Fs < 2.83, ns. A correlation analysis of computer game experience with all dependent variables (landmark recognition hits and false alarms, tour-integration performance, and route navigation performance) revealed a significant correlation of computer game experience with route navigation performance (r = 0.32, p < 0.01). We included computer game experience as a covariate in the respective analysis after confirming that all assumptions held, see Table 4.

Experiment 3

Computer game experience (M = 2.15, SD = 0.97) did not differ between the experimental groups, as a 2 (movement control) × 3 (navigation control) ANOVA showed (all Fs < 1). A correlation analysis of computer game experience with all dependent variables revealed a significant correlation of computer game experience with route navigation performance (r = 0.26, p < 0.01). We included computer game experience as a covariate in the respective analysis after confirming that all assumptions held, see Table 4.

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von Stülpnagel, R., Steffens, M.C. Active route learning in virtual environments: disentangling movement control from intention, instruction specificity, and navigation control. Psychological Research 77, 555–574 (2013). https://doi.org/10.1007/s00426-012-0451-y

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