Abstract
Computer games have been proposed as effective tools for cognitive enhancement. Especially first-person shooter (FPS) games have been found to yield a range of positive effects, and these positive effects also apply to the domain of executive functioning. Only a particular area of executive functioning has been shown to resist training via FPS games, and this area is task-switching performance. Here, we tested whether games of a different genre, real-time strategy (RTS) games, offer a more promising approach to improve task-switching performance, because RTS games capitalize on precisely this behavior. A high-powered, quasi-experimental comparison of RTS and FPS players indicated reliable costs for task-switching across both player groups—with similar performance on multiple indicators, comprising switch costs, mixing costs, voluntary switch rates, and psychological refractory period effects. Performance of both groups further did not exceed the performance of a control group of Chess and Go players. These results corroborate previous findings on the robustness of cognitive costs of task-switching. At the same time, our results also suggest that the precise characteristics of different computer games might not be critical in determining potential training effects.
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Notes
A more recent meta-analysis was published during re-review of the second revision of the present paper (Bediou et al. 2017). This analysis replicated the conclusion that task-switching performance does not profit from video-game training. We thank the editor for drawing our attention to this study.
We report multivariate tests of all within-subjects effects in the ANOVA to counter possible violations of sphericity.
We thank an anonymous reviewer and the editor of this paper for prompting the collection of this additional group.
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Appendices
Appendix 1: Sample Characteristics
Distribution of players across continents
Appendix 2: Error Analyses
The analysis of the error data followed the same strategy as the RT analysis. Trials with response omissions and trials following either errors or response omissions were excluded from the analysis and PEs were computed by dividing the frequency of error trials by the frequency of error trials plus the frequency of correct trials. Resulting effects are plotted in Fig. 4 and descriptive statistics can be found in Table 4.
Results of the analysis of error percentages (PEs) for real-time strategy (RTS) and first-person shooter (FPS) players. CID denotes 95% confidence intervals for the between-group difference (Pfister and Janczyk 2013), and CIM denotes 95% confidence intervals for individual means. a Mixing costs and switch costs as measured in the cued task-switching paradigm, accompanied by the corresponding raw PEs. b Switch frequency and switch costs as measured in the voluntary task-switching paradigm, and corresponding raw PEs. c Effects of stimulus-onset asynchrony (SOA) on PE in the psychological refractory period paradigm, and corresponding raw PEs
Cued Task-Switching
PEs did not show any signs of mixing costs, neither for the RTS group, t(709) = 1.29, p = .197, d = 0.05, nor for the FPS group, t(110) = 0.25, p = .803, d = 0.02. The difference between both groups was not significant either, t(819) = 0.73, p = .469, d = 0.07, BF01 = 6.88. However, there were significant switch costs for both the RTS group, t(709) = 21.41, p < .001, d = 0.80, and the FPS group, t(110) = 7.43, p < .001, d = 0.71, but no between-group difference, t(819) = 0.47, p = .635, d = 0.05, BF01 = 7.95.
A 2 × 3 ANOVA with the between-subjects factor group (RTS vs. FPS) and the within-subjects factor task sequence (single-task block: task repetition, mixed block: task repetition, mixed block: task-switch) showed a main effect of task sequence, F(2, 818) = 156.83, p < .001, ηp2 = 0.28, but neither a main effect of group, F(1, 819) = 1.56, p = .212, ηp2 < 0.01, nor an interaction of both factors, F(2, 818) = 0.75, p = .475, ηp2 < 0.01.
Voluntary Task-Switching
Significant switch costs were present for both the RTS group, t(534) = 8.45, p < .001, d = 0.37, and the FPS group, t(86) = 4.50, p < .001, d = 0.48. Mirroring the results of the cued task-switching paradigm, switch costs did not differ between groups, t(620) = 0.73, p = .467, d = 0.08, BF01 = 6.10.
A 2 × 2 ANOVA with the between-subjects factor group (RTS vs. FPS) and the within-subjects factor task sequence (task repetition vs. task-switch) showed a main effect of task sequence, F(1, 620) = 50.57, p < .001, ηp2 = 0.08, and a descriptive trend toward higher error rates for the FPS group, F(1, 620) = 3.52, p = .061, ηp2 = 0.01. The interaction of both factors was not significant, F(1, 620) = 0.53, p = .467, ηp2 < 0.01.
Psychological Refractory Period (PRP) Paradigm
A trial was coded as erroneous if participants committed an error in at least one of the tasks. More errors occurred for the short SOA as compared to the long SOA and this was true for both the RTS group, t(743) = 9.84, p < .001, d = 0.36, and the FPS group, t(114) = 3.29, p = .001, d = 0.31. The between-groups comparison of this difference was not significant, t(857) = 0.08, p = .933, d = 0.01, BF01 = 8.98 (corrected for unequal variances due to a significant Levene test: p = .013; uncorrected values: t = 0.10, p = .923).
A 2 × 2 ANOVA with the between-subjects factor group (RTS vs. FPS) and the within-subjects factor SOA (0 vs. 1000 ms) on the raw PEs showed a main effect of SOA, F(1, 857) = 50.24, p < .001, ηp2 = 0.06, and also a main effect of group, F(1, 857) = 8.14, p = .004, ηp2 = 0.01, with overall more errors in the FPS group. The interaction was not significant, F(1, 857) = 0.01, p = .923, ηp2 < 0.01.
Appendix 3: Exploratory Analyses of the DotA 2 Subsample
To assess how task-switching performance is influence by gaming experience (in terms of the self-reported number of hours played per week), we split the large sample of DotA 2 players into 11 subgroups of 5, 10, 15, … 50, and more than 50 (51+) hours per week. Mixing and switch costs of the cued task-switching paradigm were reliable across subgroups (Fig. 5). For the voluntary task-switching paradigm, there was a slight trend toward lower switch frequencies with more gaming experience, whereas switch costs were as robust as for the cued task-switching paradigm (Fig. 6). The effects in the PRP paradigm were also independent of subgroup (Fig. 7).
Detailed analyses of the DotA 2 players for the cued task-switching paradigm. Mixing costs (upper panels) and switch costs (lower panels) for reaction times (RTs; left panels) and error percentages (PEs; right panels) are plotted for subgroups of varying self-reported gaming experience. Error bars represent 95% confidence intervals for the individual means, and sample sizes (n) are attached to the x axis
Detailed analyses of the DotA 2 players for the voluntary task-switching paradigm. Switch frequencies are plotted in the upper-central panel, whereas switch costs are plotted in the lower panels (left for reaction times, RTs, right for error percentages, PEs). Error bars represent 95% confidence intervals for the individual means, and sample sizes (n) are attached to the x axis
Detailed analyses of the DotA 2 players for the psychological refractory period paradigm. The upper panels show the effect of stimulus-onset asynchrony (SOA) on reaction times (RTs) for the first task (ΔRT1) and the second task (ΔRT2), whereas the lower-central panel shows the effect of SOA on error percentages (ΔPE). Error bars represent 95% confidence intervals for the individual means, and sample sizes (n) are attached to the x axis
Appendix 4: Analysis of Control Group
Error Data
In the cued task-switching paradigm, the control group did not show significant mixing costs in PEs, t(23) = 0.70, p = .490, d = 0.14, but there were significant switch costs, t(23) = 4.38, p < .001, d = 0.89 (see Table 5). There were no significant differences between groups as measured by one-way ANOVAs (RTS vs. FPS vs. control) or contrasting the control against the two experimental groups (all ps > .276).
In the voluntary task-switching paradigm, switch rates of the control group (14.5%) were significantly lower than would have been expected by chance (50%), t(22) = 10.22, p < .001, d = 2.13. This effect did not differ between groups in the one-way ANOVA, F(2, 1005) = 0.03, p = .970, ηp2 < 0.01, or when contrasting the control and the experimental groups (Table 6). There were no significant switch costs in errors for those control participants who opted to switch at least once, t(13) = 1.06, p = .311, d = 0.28, and switch costs did not differ between groups, neither in the one-way ANOVA, F(2, 633) = 0.47, p = .628, ηp2 < 0.01, nor for the contrast analysis (p = .463).
In the PRP paradigm, the PEs of the control participants did not differ significantly between SOAs, t(22) = 1.85, p = .078, d = 0.39. However, the control participants showed a smaller effect of SOA on PEs, giving rise to a significant main effect of group, F(2, 879) = 4.98, p = .007, ηp2 = 0.01, as well as a significant contrast control vs. experimental (p = .002). Finally, visual inspection of the raw PEs suggested overall fewer mistakes in the control group as compared to both experimental group. To qualify this impression, we computed a one-way ANOVA on the mean PEs across SOAs, and this analysis also yielded a significant main effect of group, F(2, 879) = 5.52, p = .004, ηp2 = 0.01, and a significant contrast of the control group relative to both experimental groups (p = .029).
Appendix 5: Extreme Groups
An analysis of more strictly separated experimental groups with at least 90% of overall gaming time spent on the assigned genre and less than 5 h per week spent on the other genre corroborated the findings of the main analysis (see Tables 8, 9, 10, and 11 below).
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Klaffehn, A.L., Schwarz, K.A., Kunde, W. et al. Similar Task-Switching Performance of Real-Time Strategy and First-Person Shooter Players: Implications for Cognitive Training. J Cogn Enhanc 2, 240–258 (2018). https://doi.org/10.1007/s41465-018-0066-3
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DOI: https://doi.org/10.1007/s41465-018-0066-3