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Flow and enjoyment beyond skill-demand balance: The role of game pacing curves and personality

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Abstract

According to flow theory, skill-demand balance is optimal for flow. Experimentally, balance has been tested only against strong overload and strong boredom. We assessed flow and enjoyment as distinct experiences and expected that they (a) are not optimized by constant balance, (b) experimentally dissociate, and (c) are supported by different personality traits. Beyond a constant balance condition (“balance”), we realized two dynamic pacing conditions where demands fluctuated through short breaks: one condition without overload (“dynamic medium”) and another with slight overload (“dynamic high”). Consistent with assumptions, constant balance was not optimal for flow (balance ≤ dynamic medium < dynamic high) and enjoyment (balance ≤ dynamic high < dynamic medium). Action orientation enabled high flow even under the suboptimal condition of balance. Sensation seeking increased enjoyment under the suboptimal but arousing dynamic high condition. We discuss dynamic changes in positive affect (seeking and mastering challenge) as an integral part of flow.

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Notes

  1. In addition, we subtracted a single-item measure of skills (“I think that my competence in this area is …” to be rated on a scale from 1 = “low” to 7 = “high”) from a single-item measure of task difficulty (“Compared to all other activities which I partake in, this one is …” to be rated on a scale from 1 = “easy” to 7 = “difficult”). This difference measure of balance was highly correlated (r = .64, p < .001) with the single-item measure of balance and yielded conceptually identical results in all analyses.

  2. This is different from the pacing condition of Tetris in the studies by Keller and Bless (2008) and Keller and Blomann (2008) where the speed may be increased and decreased to an infinite degree. We included these boundaries to have consistent pacing boundaries with the experimental conditions of dynamic medium and dynamic high pacing.

  3. We used a two-step method to determine participants’ performance level. In a first step, we determined a raw performance \( \widetilde{e} \). In a second step, we computed the filtered performance e by dampening too drastic changes in the measured raw performance \( \widetilde{e} \) over time. The measured raw performance \( \widetilde{e} \) is based on the player behavior in the last 8 s. We calculated a sliding score within this time interval where every collected fly added one point and every collected bee subtracted one point. If this value fell below zero it was clamped to zero. That score was divided by the total number of flies the player encountered during that period of time. The resulting value was the measured raw performance \( \widetilde{e} \). If we executed a series of filtered performance estimates e 0, e 1,… over time, each of the performance estimates being a constant time step \( \Delta t = 0.01666{\kern 1pt} \,{\text{s}} \) apart, we used the following relation between filtered and raw performance and, thus, guaranteed that the filtered performance did not change more than 0.2 in one second: \( e_{i + 1} = \left\{ {\begin{array}{*{20}l} {\widetilde{{e_{i + 1} }}} \hfill & {\left| {\widetilde{{e_{i + 1} }} - e_{i} } \right| \le 0.2 \cdot\Delta t} \hfill \\ {e_{i} + 0.2 \cdot\Delta t} \hfill & {\widetilde{{e_{i + 1} }} > e_{i} + 0.2 \cdot\Delta t} \hfill \\ {e_{i} - 0.2 \cdot\Delta t} \hfill & {\widetilde{{e_{i + 1} }} < e_{i} + 0.2 \cdot\Delta t} \hfill \\ \end{array} } \right. \)

  4. Because the revised flow model (Csikszentmihalyi and LeFevre 1989) predicts flow only under balance achieved for high skills and high demands, we performed a median split for subjective task difficulty and tested whether squared balance was a significant predictor of flow and enjoyment in each subsample. In the subsample with low task difficulty (M = 2.05, SD = 0.84, range 0–3), squared balance significantly predicted flow (ß = −.40, t(1, 48) = −2.53, p < .02) and enjoyment (ß = −.54, t(1, 48) = −3.51, p < .001). In the subsample with high task difficulty (M = 4.98, SD = 0.99, range 4–7), squared balance significantly predicted flow (ß = −.65, t(1, 35) = −3.29, p < .002) but not enjoyment (ß = −.25, t(1, 35) = −1.07, p = .29). The results show that the effect of balance on flow does not substantially alter for high difficulties compared to low difficulties. In addition, including subjective task difficulty (and/or performance) as a further control variable in our main analyses did not change any of the results or made the findings even stronger.

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Authors and Affiliations

  1. Differential Psychology, Personality Psychology, and Diagnostics, Department I, University of Trier, 54286, Trier, Germany

    Nicola Baumann

  2. Trier University of Applied Sciences, Trier, Germany

    Christoph Lürig

  3. Friedrich-Schiller-University Jena, Jena, Germany

    Stefan Engeser

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  1. Nicola Baumann
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  2. Christoph Lürig
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  3. Stefan Engeser
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Correspondence to Nicola Baumann.

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The research was conducted during the last author’s affiliation at the University of Trier.

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Baumann, N., Lürig, C. & Engeser, S. Flow and enjoyment beyond skill-demand balance: The role of game pacing curves and personality. Motiv Emot 40, 507–519 (2016). https://doi.org/10.1007/s11031-016-9549-7

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