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Research has repeatedly demonstrated that the use of interactive media is associated with recovery experiences, suggesting that engaging with media can help people to alleviate stress and restore mental and physical resources. Video games, in particular, have been shown to fulfil various aspects of recovery, not least due to their ability to elicit feelings of mastery and control. However, little is known about the role of cognitive task demand (i.e., the amount of cognitive effort a task requires) in that process. Toward this end, our study aimed to investigate how cognitive task demand during gameplay affects users’ recovery experiences. Results of a laboratory experiment suggest that different dimensions of the recovery experiences seem to respond to different levels of cognitive task demand. While control experiences were highest under low cognitive task demand, there was no difference between groups regarding experiences of mastery and psychological detachment. Nevertheless, both gaming conditions outperformed the control condition regarding experiences of mastery and psychological detachment. Controlling for personal gaming experiences, relaxation was higher in the low cognitive task demand condition compared to the control condition. Findings are discussed in terms of their implications for research on the multilayered recovery effects of interactive media.
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Media and Communication (ISSN: 2183–2439)
2019, Volume 7, Issue 4, Pages 166–175
DOI: 10.17645/mac.v7i4.2297
Article
Harder, Better, Faster, Stronger? The Relationship between Cognitive Task
Demands in Video Games and Recovery Experiences
Tim Wulf 1,*, Diana Rieger 1, Anna Sophie Kümpel 1and Leonard Reinecke 2
1Department of Media and Communication, LMU Munich, 80538 Munich, Germany; E-Mails: tim.wulf@ifkw.lmu.de (T.W.),
diana.rieger@ifkw.lmu.de (D.R.), anna.kuempel@ifkw.lmu.de (A.S.K.)
2Department of Communication, Johannes Gutenberg University Mainz, 55128 Mainz, Germany;
E-Mail: reineckl@uni-mainz.de
* Corresponding author
Submitted: 24 June 2019 | Accepted: 3 October 2019 | Published: 20 December 2019
Abstract
Research has repeatedly demonstrated that the use of interactive media is associated with recovery experiences, suggest-
ing that engaging with media can help people to alleviate stress and restore mental and physical resources. Video games,
in particular, have been shown to fulfil various aspects of recovery, not least due to their ability to elicit feelings of mastery
and control. However, little is known about the role of cognitive task demand (i.e., the amount of cognitive effort a task
requires) in that process. Toward this end, our study aimed to investigate how cognitive task demand during gameplay
affects users’ recovery experiences. Results of a laboratory experiment suggest that different dimensions of the recovery
experiences seem to respond to different levels of cognitive task demand. While control experiences were highest under
low cognitive task demand, there was no difference between groups regarding experiences of mastery and psychological
detachment. Nevertheless, both gaming conditions outperformed the control condition regarding experiences of mastery
and psychological detachment. Controlling for personal gaming experiences, relaxation was higher in the low cognitive task
demand condition compared to the control condition. Findings are discussed in terms of their implications for research on
the multilayered recovery effects of interactive media.
Keywords
cognitive task demand; gaming; interactive media; recovery experiences; video games
Issue
This article is part of the issue “Video Games as Demanding Technologies” edited by Nicholas David Bowman (Texas Tech
University, USA).
© 2019 by the authors; licensee Cogitatio (Lisbon, Portugal). This article is licensed under a Creative Commons Attribu-
tion 4.0 International License (CC BY).
1. Introduction
People adopt various strategies to recover from stress;
some engage in sports, others enjoy a special meal, or
just relax while watching their favorite series. Indeed,
previous research has identified the use of entertain-
ment media as an effective strategy to alleviate negative
affective states (e.g., Zillmann, 1988) and to recover from
stressful situations (e.g., Rieger, Reinecke, Frischlich, &
Bente, 2014). Video games in particular have been in
the focus of media research given their interactive na-
ture that challenges players to master quests, win out
over other players or the game itself and, in doing so,
fulfill psychological needs for recovery and well-being
(e.g., Reinecke, 2009a; Reinecke, Klatt, & Krämer, 2011).
While such research suggests that video games may be
a significant recovery resource, the gaming environment
also places continual demands on the player: Players
engage in continuous “input–output loops” (Klimmt &
Hartmann, 2006, p. 137) with the gaming environment
and need to react to new challenges. The cognitive task
demand of video games can be conceptualized as a func-
Media and Communication, 2019, Volume 7, Issue 4, Pages 166–175 166
tion of the cognitive skills required to play as well as
the cognitive resources needed to direct attention to-
wards the actions in the game (Bowman, 2018). The role
of the fit between a game’s cognitive task demand and
players’ abilities to recover through gaming has been
largely neglected by media psychological research thus
far. However, as suggested by previous gaming research,
experiences that fit media users’ (cognitive) capacities
may be particularly enjoyable, motivating users to push
further, thus helping them to address their recovery
needs (Schmierbach, Chung, Wu, & Kim, 2014).
Given that video games—not least due to their in-
teractive nature—demand that players allocate a certain
amount of their cognitive ability toward the game, the
current article aims to close the gap in current research
by testing the assumption that video games that demand
just the right level of cognitive capacity are best suited
to provide recovery experiences for their players. To this
end, we instructed participants to play a high demand,
low demand, or no video game and measured their cog-
nitive task demand as well as recovery experiences in an
experimental setting.
2. Media Use and Recovery Experiences
A plethora of daily activities—both in and out of work—
drain people’s mental and physical resources, which can
result in negative affect or physiological and psychologi-
cal fatigue (Fuller et al., 2003; Sluiter, de Croon, Meijman,
& Frings-Dresen, 2003). To restore these resources and
to avoid long-term stress or health ramifications, peo-
ple need to recover. In general, recovery refers to the
“process of replenishing depleted resources or rebal-
ancing suboptimal systems” (Sonnentag & Zijlstra, 2006,
p. 331), which is a vital factor for people’s performance
and psychological well-being. While the everyday use
of the word recover(ing) usually refers to relatively pas-
sive activities and a state of low activation (e.g., relax-
ing or resting), recovery experiences go beyond wind-
ing down. In an attempt to integrate the different facets
of recovery, Sonnentag and Fritz (2007) discuss four dis-
tinct recovery experiences that address different ways
to replenish depleted resources: psychological detach-
ment (i.e., mentally/physically distancing oneself from
stress-inducing tasks); relaxation (i.e., reducing activa-
tion/increasing positive affect); mastery (i.e., experienc-
ing competence and proficiency); and control (i.e., be-
ing able to choose activities at one’s discretion). While
the first two recovery experiences—psychological de-
tachment and relaxation—imply that no new demands
are imposed on the person, the latter two—mastery and
control—suggest that recovery can also result from en-
gaging in (new) activities that help to build up internal
resources such as knowledge or self-efficacy (see also
Reinecke & Eden, 2017).
Considering that recovery is a crucial self-regulatory
process and essential for health and well-being, it comes
as no surprise that determinants and antecedents of suc-
cessful recovery have attracted considerable scholarly
attention, particularly in industrial and organizational
psychology (for an overview see Sonnentag, Venz, &
Casper, 2017). While this line of recovery research has of-
ten marginalized the role of media use, research in me-
dia psychology and communication studies has demon-
strated that (entertainment) media use is a frequently
used and highly successful strategy to recover from stress
and strain (e.g., Janicke, Rieger, Reinecke, & Connor,
2018; Reinecke et al., 2011; Rieger & Bente, 2018; Rieger,
Reinecke, et al., 2014). Some studies explain these find-
ings by the cognitive challenges posed by the content
(e.g., Bartsch & Hartmann, 2017). These studies pro-
vide evidence that both interactive (e.g., video games)
and non-interactive (e.g., movies) entertainment media
can promote the four recovery experiences proposed
by Sonnentag and Fritz (2007), although to different ex-
tents. Specifically, it was shown that non-interactive and
interactive media elicit comparable levels of psychologi-
cal detachment and relaxation but differ in their effect
on experiencing mastery and control (Reinecke et al.,
2011). Being interactive in nature, video games demand
active participation from users and allow them to be in
control of their actions, thus making games particularly
suitable for fulfilling recovery needs (see also Reinecke,
2009a, 2009b).
3. Video Games, Cognitive Task Demand, and Recovery
Experiences
Video games are interactive entertainment media which
require players to use cognitive abilities to solve prob-
lems and tasks. Notably, such cognitive task demands are
referred to differently in the literature. While some au-
thors speak of “task load” (e.g., Moroney, Reising, Biers,
& Eggemeier, 1993; Rieger, Frischlich, Wulf, Bente, &
Kneer, 2015), some call it “task demand” (e.g., Bowman
& Tamborini, 2012) or “cognitive load” (Read, Lynch,
& Matthews, 2018). Considering that there are vari-
ous demands necessary when playing video games (see
Bowman, 2018), we have decided to stick to the term
cognitive task demand(s) in the current article to refer
broadly to the concept of “cognitive skills required to
play games as well as the cognitive resources that the
game pulls from the player in order to arrest attention
toward the myriad messages in a game’s environment”
(Bowman, 2018, p. 7).
Solving tasks while playing an interactive video game
may result in experiences of self-efficacy (Bandura,
1977), as players will directly experience how their ac-
tions affect the game. Early on, it was shown that such
experiences are related to game enjoyment (Klimmt,
Hartmann, & Frey, 2007; Trepte & Reinecke, 2011). In
terms of mood management theory (Zillmann, 1988),
being forced to act comes with intervention potential
that helps players to be distracted from aversive states
(Bowman & Tamborini, 2012; Bryant & Davies, 2006).
Rieger and colleagues (2015) instructed participants to
Media and Communication, 2019, Volume 7, Issue 4, Pages 166–175 167
either play a game of the arcade classic game Pac-Man
or let them watch a video of someone else playing it.
They found that playing participants regulated their neg-
ative mood more efficiently than the other groups and
that cognitive task demand fostered mood regulation.
Recently, similar results were found for quiz games’ abil-
ity to foster competence repair (Koban et al., 2018). Yet,
this picture appears simplistic. Bowman and Tamborini
(2012) confronted bored and stressed participants with
more nuanced cognitive task demand conditions. They
found that with increasing cognitive task demand partici-
pants’ mood regulation increased as long as players were
able to control the interactive environment. However,
once the game became too demanding, their mood re-
pair decreased. Altogether, the right match between cog-
nitive task demand and players’ cognitive capabilities
thus appears relevant for the impact of interactive me-
dia on mood repair.
Previous research on the recovery potential of video
games suggests that the benefit of playing cognitively de-
manding video games goes beyond mere mood repair.
Data from a survey study by Reinecke (2009b) demon-
strate that video games can contribute to all four re-
covery dimensions within Sonnentag and Fritz’s (2007)
conceptualization. We suggest that cognitive task de-
mand resonates with Reinecke’s (2009a) reasoning. First,
Reinecke (2009a) argues that video games contribute to
psychological detachment by forcing users “to focus their
full attention on the game” as they “do not leave much
room for thoughts that are not directed toward the gam-
ing environment” (Reinecke, 2009a, p. 128). This argu-
ment is supported by authors describing video games as
coming with varying task difficulties which players have
to adapt to (e.g., Klimmt & Hartmann, 2006) as well as
research showing that players immerse into the game
and forget about the reality outside of the gaming nar-
rative (e.g., Sherry, Lucas, Greenberg, & Lachlan, 2006).
Accordingly, we expect that with increasing cognitive task
demand players will experience higher levels of psycho-
logical detachment, as they are better able to distract
themselves from the sources of their stress:
H1a: Players in a high cognitive task demand condition
will experience more psychological detachment com-
pared to players in a low cognitive task demand con-
dition and participants in a control group not playing
a game;
H1b: Players in a low cognitive task demand condition
will experience more psychological detachment com-
pared to participants in a control group not playing
a game.
Reinecke (2009a) further argues that video games can
help people to relax—although studies often attest that
playing games comes with a rise in physiological arousal
(e.g., Rieger et al., 2015). The relaxing effects of gaming
may function based on similar mechanisms as physical
activities, which are followed up by a decrease in anxi-
ety and tension after the actual experience (e.g., Taylor,
Sallis, & Needle, 1985). As high cognitive task demand
may come with high levels of arousal, it appears counter-
intuitive that it could be experienced as particularly relax-
ing. Thus, we pose the following research question with
regard to relaxation:
RQ1: Does the experience of relaxation differ be-
tween players in high, low, and no cognitive task de-
mand conditions?
More intuitively, players experience control when solv-
ing in-game tasks. First, the experience of self-efficacy
(Bandura, 1977; Klimmt et al., 2007; Trepte & Reinecke,
2011) may help people to experience control over certain
gaming tasks. Second, video games provide players with
a plethora of decisions, ranging from customization of
avatars (Trepte & Reinecke, 2010) to freely exploring the
in-game world. For the experience of control, cognitive
task demand should play a crucial role. Under low cogni-
tive task demand, players may easily keep all problems
in check and experience high levels of control, whereas
a game with high cognitive task demand may overstrain
players and inhibit control experiences. However, even
under conditions of high cognitive task demand, games
are likely to provide more opportunities to experience
control than the complete absence of current tasks. Thus,
we pose the following set of hypotheses:
H2a: Players in a low cognitive task demand condition
will experience more control compared to players in
a high cognitive task demand condition and partici-
pants in a control group not playing a game;
H2b: Players in a high cognitive task demand condi-
tion will experience more control compared to partic-
ipants in a control group not playing a game.
Finally, video games help players to experience mastery
by allowing them to solve challenges, achieve success,
and feel generally competent when playing the game
(Klimmt et al., 2007; Rieger, Wulf, Kneer, Frischlich, &
Bente, 2014; Ryan, Rigby, & Przybylski, 2006; Sherry et
al., 2006). For the experience of mastery, cognitive task
demand may play an important part. The more challeng-
ing the game, the more triumphant is the experience of
having solved it. Hence, we expect:
H3a: Players in a high cognitive task demand condi-
tion will experience more mastery compared to play-
ers in a low cognitive task demand condition and par-
ticipants in a control group not playing a game;
H3b: Players in a low cognitive task demand condi-
tion will experience more mastery compared to par-
ticipants in a control group not playing a game.
Media and Communication, 2019, Volume 7, Issue 4, Pages 166–175 168
Furthermore, researchers have argued that individual ex-
periences playing video games differ between players as
a function of their skills. A game which overexerts the
player may lead to frustration (Bowman & Tamborini,
2012) and a game high in cognitive task demand may
be too challenging for some players, thus impeding mas-
tery or control experiences. If players are too inexperi-
enced, such games might induce stress rather than help-
ing them to recover from it. On the other hand, if the
task is too easy, the experience of recovery may also be
impeded, given that the task could have been solved by
anyone without special effort. Thus, we pose as our final
research question:
RQ2: Does controlling for previous experience with
video games impact recovery experiences between
conditions of different cognitive task demand?
4. Method
4.1. Participants and Procedure
Participants were 148 German adults (77% female; age:
M=24.82, SD =5.49; 95% students) recruited via stu-
dent mailing lists and ad hoc on-campus recruitment at
a large German University. All students received course
credit for participation. After obtaining informed con-
sent and introducing participants to the procedure of
the experiment, participants worked on the KLT-R (Düker,
Lienert, Lukesch, & Mayrhofer, 2001), a fatiguing concen-
tration test, for about 5 to 10 minutes. The KLT-R con-
sists of a large number of relatively easy arithmetic prob-
lems that need to be solved under time pressure. Similar
tasks have been successfully used in previous media psy-
chological studies to induce fatigue (e.g., Reinecke et al.,
2011; Rieger, Hefner, & Vorderer, 2017).
Following this task, we randomly assigned partici-
pants to one of three conditions. Participants in the two
gaming conditions played either a low (easy mode) or
high (hard mode) cognitive task demand version of Tetris
on a computer. During gameplay, participants had to
react to a secondary reaction time task as an implicit
measure for cognitive task demand (see Section 4.2).
The control group did not play a video game, but its
members were instructed to relax and react to the au-
dio signals. After finishing the game, participants in
the two treatment conditions reported their subjective
cognitive effort and recovery experiences. Finally, we
assessed demographic variables and debriefed partici-
pants. Originally, we also employed a second manipula-
tion. Participants in both gaming conditions were given
false feedback on their performance (i.e., they were
told that they performed either better or worse than
most other players). However, this manipulation failed,
as 68.2% rated the authenticity of the feedback poorly.
We tested whether feedback manipulation had an im-
pact on any of the target variables when also account-
ing for task demand. These analyses yielded additional
information that this second manipulation failed. Thus,
we decided to exclude this variation from our final analy-
ses. This second manipulation is included in the data set
provided in our Open Science Framework (OSF) reposi-
tory and may be used for further analyses (the respective
variable is labelled as “feedback”).
4.2. Measures
4.2.1. Cognitive Task Demand
We used a secondary reaction time task programmed
with OpenSesame (Mathôt, Schreij, & Theeuwes, 2012)
as an implicit measure for cognitive task demand. We
instructed participants in all three conditions to press
a button with their free hand (as Tetris kept only one
of their hands busy) whenever they heard an audio sig-
nal (altogether, 20 trials). This procedure is an estab-
lished measure for cognitive task demand and has been
used, for example, within research on message process-
ing (Hefner, Rothmund, Klimmt, & Gollwitzer, 2011; Lang
& Basil, 1998; Lang, Bradley, Park, Shin, & Chung, 2006).
For the evaluation of reaction times, we identified miss-
ing values and outliers. From all trials (N=2,960 possible
data points), 542 data points were missing values. These
missing values are either due to: a) technical issues; or
b) participants not reacting at all during a trial. Among
the remaining 2418 data points, we identified 79 trials
within two standard deviations of the respective trial
mean as outliers (Baayen & Milin, 2010), which corre-
sponds to roughly 3.3% of the measured and non-missing
data points. Notably, all of these outliers were located
two standard deviations above (not below) the respec-
tive trial mean. This can be problematic if the right tail
is responsible for the effects found in the data (Baayen
& Milin, 2010; Luce, 1986). However, given that we ex-
cluded less than 5% of cases, this should not pose a prob-
lem (Baayen & Milin, 2010; Ratcliff, 1993). From the valid
2,339 trials, we calculated the mean reaction time (in sec-
onds) for each participant (M=.76, SD =.33).
Additionally, we measured subjective cognitive task
demand (cognitive effort) with four items of the NASA
Task Load Index (NASA-TLX), assessed by default on
a 20-point Likert scale ranging from “Low” to “High”
(Moroney et al., 1993). Reliability analyses suggested
excluding one item (asking participants how satisfied
they were with the overall performance) to reach ade-
quate reliability of the remaining three items (M=9.93,
SD =5.01, 𝛼 = .83). This scale has been used in previous
gaming studies focusing on cognitive task demand (e.g.,
Bowman & Tamborini, 2015). Both indices for cognitive
task demand (reaction times and cognitive effort) corre-
lated significantly (r=.27, p=.01).
4.2.2. Recovery Experiences
We measured recovery experiences with the scale devel-
oped by Sonnentag and Fritz (2007). This scale measures
Media and Communication, 2019, Volume 7, Issue 4, Pages 166–175 169
recovery experiences on four subscales, psychological
detachment, relaxation, mastery, and control (each on a
5-point scale). Both the scale overall (recovery) and the
previously mentioned subscales showed good internal
consistency (recovery, M=2.87, SD =.78, 𝛼 = .89; psy-
chological detachment, M=3.22, SD =1.03, 𝛼 = .81; re-
laxation, M=2.89, SD =1.11, 𝛼 = .88; mastery, M=2.49,
SD =.99, 𝛼 = .83; control, M=2.88, SD =1.02, 𝛼 = .79).
4.2.3. Previous Gaming Experiences
As a covariate of interest, we asked participants to indi-
cate how often they play video games in their everyday
life. This scale ranged from 1 (never) to 5 (often; M=2.00,
SD =1.23).
5. Results
Data, as well as analysis scripts of the current project, can
be accessed in an OSF repository at https://osf.io/jgp58.
Zero-order correlations between all variables of interest
are presented in Table S1 in the supplementary material.
All items used in the questionnaire can be accessed in
Table S2 within the same file.
5.1. Manipulation Check(s)
To check whether the gaming conditions successfully in-
duced different levels of cognitive task demand, we con-
ducted a MANOVA with condition (low demand game
vs. high demand game vs. control condition) as the in-
dependent variable and both measures of cognitive task
demand (NASA-TLX for subjective cognitive task demand
and reaction times as an implicit measure of cognitive
task demand) as dependent variables. There was a signif-
icant effect of condition on subjective and implicit cog-
nitive task demand, Wilk’s Λ = .46, F(4, 256) =30.07,
p<.001, 𝜂2
p=.32. Separate follow-up univariate ANOVAs
revealed that condition had a significant impact on both
the NASA-TLX, F(2, 129) =54.58, p<.001, 𝜂2
p=.46,
and on reaction times, F(2, 129) =14.95, p<.001,
𝜂2
p=.19. Sidak post hoc tests for both cognitive task de-
mand measures showed that while subjective cognitive
task demand (NASA-TLX) differed between all conditions
following a linear trend in the expected direction (high,
M=13.36, SD =3.37; low, M=9.65, SD =3.97; no
game, M=4.32, SD =3.45), the high-demand (M=.80,
SD =.29) and low-demand (M=.86, SD =.32) condition
did not significantly differ in reaction times. However,
participants in both gaming conditions showed slower re-
action times compared to the control condition (M=.47,
SD =.28). Altogether, the manipulation can be regarded
as successful.
5.2. Cognitive Task Demand and Recovery Experiences
To test the impact of cognitive task demand on recovery
experiences (H1–H3 and RQ1), we conducted a second
MANOVA with condition as the independent variable
and the four recovery dimensions (Sonnentag & Fritz,
2007) as dependent variables. There was a significant ef-
fect of condition on recovery experiences, Wilk’s Λ = .74,
F(8, 284) =5.67, p<.001, 𝜂2
p=.14. Separate follow-up
univariate ANOVAs revealed that condition had a signifi-
cant impact on each dimension of recovery. For ease of
interpretation, details and Sidak post hoc tests are pre-
sented in Table 1. Post hoc tests revealed that for psycho-
logical detachment and mastery experiences, both gam-
ing conditions achieved higher recovery scores than the
control condition, but there was no difference between
the low cognitive demand and high cognitive demand
gaming condition. Thus, H1a and H3a had to be rejected,
whereas the findings supported H1b and H3b. There was
no significant difference between the three conditions
for relaxation (RQ1). For control experiences, all groups
differed significantly from each other, with the low cogni-
Table 1. Univariate analyses and simple comparisons between conditions for all recovery experiences.
Conditions
No Game Low Cognitive High Cognitive
Control Demand Game Demand Game F-Test p η2
p
M M M F
(SE) (SE) (SE) (df )
Psychological Detachment 2.60a3.49b3.30b8.51 <.001 .11
(.18) (.13) (.13)(2,145)
Relaxation 2.65a3.15a2.78a2.57 .080 .03
(.20) (.15) (.14)(2,145)
Mastery Experiences 1.95a2.63b2.64b6.25
(.17) (.13) (.12)(2,145) .002 .08
Control Experiences 2.15a3.30c2.88b15.26 <.001 .17
(.17) (.13) (.12)(2,145)
Note: Within rows, means (M) with no superscript in common differ significantly.
Media and Communication, 2019, Volume 7, Issue 4, Pages 166–175 170
tive demand gaming condition scoring highest, the con-
trol condition lowest, and the high cognitive demand
gaming condition in between, supporting H2a and H2b.
5.3. The Influence of Previous Gaming Experiences
To account for individual differences in the use of
video games (RQ2), we repeated the MANOVA above
and introduced the measure for previous gaming ex-
periences as a covariate to the model. When account-
ing for participants’ gaming frequency, the MANCOVA
yielded both condition (Λ = .68, F(8, 282) =7.50,
p<.001, 𝜂2
p=.18) and gaming frequency (Λ = .84,
F(4, 141) =6.82, p<.001, 𝜂2
p=.16) as significant pre-
dictors of recovery. Follow-up univariate ANOVAs for
all recovery dimensions by both condition and gaming
frequency were significant. For condition: psychologi-
cal detachment, F(2,144) =12.79, p<.001; relaxation,
F(2,144) =3.73, p=.026; mastery, F(2,144) =7.59,
p=.001; control, F(2,144) =20.26, p<.001. For casual
game play: psychological detachment, F(1,144) =19.62,
p<.001; relaxation, F(1,144) =3.73, p=.006; mas-
tery, F(1,144) =5.43, p=.021; control, F(1,144) =15.15,
p<.001. Keeping gaming frequency constant, the ob-
served effects of cognitive task demand remained. In ad-
dition, there now was a significant effect of condition on
relaxation. That is, the low cognitive demand gaming con-
dition was more relaxing than the no-game control con-
dition (see Table 2).
6. Discussion
The current study was guided by one overarching ques-
tion: How does cognitive task demand in a gaming situ-
ation impact users’ recovery experiences? First, we ex-
pected that with increasing cognitive task demand, psy-
chological detachment would increase, as more cogni-
tive capacities would be required. As anticipated, the
results indicate that playing a game (low and high cog-
nitive demand gaming conditions) leads to significantly
higher psychological detachment than not playing a
game. However, there was no difference between both
gaming conditions, even when controlling for previous
gaming experiences. The simplest explanation for this
finding would be that the amount of cognitive task de-
mand does not affect psychological detachment as previ-
ous work has suggested (e.g., Bowman, 2018; Klimmt &
Hartmann, 2006; Reinecke, 2009a; Sherry et al., 2006).
Another explanation for this unexpected finding could
be that while subjective cognitive task demand actu-
ally differed between all conditions in the expected di-
rection, objective measures (reaction times) only indi-
cated a difference between playing (either high or low
cognitive demand) and not playing (control condition).
In other words, while players differed in their subjec-
tive experience between the two gaming conditions, the
objective cognitive task demand was comparable and
response speed to the audio signal was similar across
gaming conditions. These findings may indicate that psy-
chological detachment could depend on the actual al-
location of cognitive resources rather than the subjec-
tive experience of doing a cognitively demanding task.
Furthermore, we might assume that psychological de-
tachment does not directly connect to cognitive task de-
mand or task difficulty but rather to other characteristics
of the game, such as the complexity of the game world,
story narration, or control elements that encourage peo-
ple to immerse deeper into the game (e.g., Sherry et al.,
2006). In the current study, participants in both gaming
conditions used the same controls to play the same game
which had the same visual features.
Second, we found no difference between any of the
conditions regarding relaxation outcomes. It could be
that video games—due to their interactive nature—are
Table 2. Univariate analyses and simple comparisons between conditions for all recovery experiences with previous gaming
experiences held constant at a value of 2.
Conditions
No Game Low Cognitive High Cognitive
Control Demand Game Demand Game F-Test p η2
p
M M M F
(SE)(SE)(SE)(df )
Psychological Detachment 2.50a3.55b3.29b12.79 <.001 .16
(.17) (.12) (.12)(2,144)
Relaxation 2.57a3.19b2.78a,b 3.73 .026 .06
(.20) (.15) (.14)(2,144)
Mastery Experiences 1.90a2.66b2.64b7.59 .001 .10
(.17) (.13) (.12)(2,144)
Control Experiences 2.06a3.35c2.87b20.26 <.001 .22
(.16) (.12) (.11)(2,144)
Notes: Within rows, means with no superscripts in common differ significantly. 1 =never play games; 5 =often play games.
Media and Communication, 2019, Volume 7, Issue 4, Pages 166–175 171
not able to reduce arousal levels below a certain point.
For relaxation, non-interactive media such as movies
might be the better choice (Rieger & Bente, 2018).
However, controlling for previous gaming experiences,
there was a difference in relaxation between the low cog-
nitive demand gaming and the no-game control condi-
tion (with the high cognitive demand gaming condition
scoring in between the two, but without being signifi-
cantly different from any of them). For participants who
play games at least seldomly, the low cognitive demand
gaming condition achieved more relaxation than the no-
game condition. These findings imply that relaxation as a
recovery experience from playing video games depends
on the fit between pre-experiences/skill and the cogni-
tive task demand of a given game. To investigate this
further, future research might consider targeting several
player samples with different skill levels and assign them
to conditions with different cognitive task demands.
For the experience of control, our findings showed
that participants in the low cognitive demand gaming
condition experienced most control, participants not
playing a game reported the lowest control, and play-
ers in the high cognitive demand condition scored in be-
tween these conditions. These results are in line with re-
search connecting experiences of self-efficacy with play-
ing video games (e.g., Klimmt et al., 2007; Trepte &
Reinecke, 2011). Participants in the low cognitive de-
mand condition experienced the highest levels of control
given that they were able to successfully respond to all in-
game challenges, whereas participants in the high cogni-
tive demand condition struggled to keep the gaming en-
vironment under control. Surprisingly, controlling for pre-
vious gaming experiences did not change these findings.
It appears plausible that experienced players might find
it easier to master a high cognitive demand version of the
game, resulting in more control experiences compared to
the same game being played by an inexperienced player.
However, this finding may also imply that the experience
of control is not connected to skill but to the perceived
freedom of being able to choose how much subjective ef-
fort is invested in a given task. Again, subjective cognitive
task demand (and not implicit measures) significantly dif-
fered between the high and low cognitive demand gam-
ing condition. All players—independent of their gaming
experiences—realized that in the hard condition more
demands were placed on their cognition in order for
them to control the situation.
Finally, we found a significant difference in mastery
experiences between the two gaming conditions and the
control condition, but not between the low and high cog-
nitive demand gaming conditions. While it appears plau-
sible that playing a game comes with more mastery ex-
periences than just reacting to an audio signal, we had
expected that cognitive task demand in the gaming situ-
ation would affect the experience of mastery. Although
previous research suggests that cognitive task demand—
as a proxy for challenge and difficulty—affects feelings
of mastery and competence (Klimmt et al., 2007; Rieger,
Wulf, et al., 2014; Ryan et al., 2006; Sherry et al., 2006),
our findings imply that such experiences may occur in-
dependent of a game’s cognitive demands or might de-
pend more heavily on related outcomes such as success.
If players are able to successfully cope with in-game chal-
lenges in both gaming conditions, those in the high cogni-
tive demand condition should experience more mastery
because they solved the more difficult task. Whereas, if
the high cognitive demand condition was too difficult,
players in the low cognitive demand condition should ex-
perience more mastery than the high cognitive demand
condition. Given that controlling for gaming experiences
did not change the results, both of these explanations do
not seem to apply to our findings. Indeed, video game
researchers found that success plays a crucial role in
the experience and appraisal of gaming episodes (e.g.,
Rieger et al., 2015; Schmierbach et al., 2014; Trepte &
Reinecke, 2011). Future research, therefore, might be in-
terested to use the game score as an indicator for suc-
cess and further explore its relationship with recovery
outcomes (given that the current article focusses on task
demand and recovery, we decided to refrain from ana-
lyzing the success/score variable; however, we encour-
age researchers to use the data in our OSF repository for
further exploratory analyses). Noteworthy in this regard,
Tetris offers only limited feedback and cues about actual
performance beyond the game score (which is difficult to
evaluate if players do not have the scores of other play-
ers for comparison). Thus, players have to rely on their
own gut feeling of how successful they were at playing
which may influence experiences of mastery (to account
for the possibility that the dropped false-feedback factor
may have impacted this analysis, we conducted an ad-
ditional t-test with feedback condition—positive vs. neg-
ative performance feedback—on mastery experiences;
there was no significant difference for mastery experi-
ences between participants in the positive [M=2.55,
SD =.97] and negative [M=2.72, SD =.98] feedback
condition, t[114] =-.95, p=.34). Another explanation
why the gaming groups did not differ might be that both
conditions are not optimal for the experience of mastery:
The low cognitive demand condition might have been
too easy while the high cognitive demand condition may
have been too difficult. Bowman and Tamborini (2012)
found similar levels of affect in their low and high cog-
nitive demand conditions with the medium cognitive de-
mand condition between those two showing highest af-
fect ratings (see Bowman & Tamborini, 2012). Future re-
search might apply such a design instead of using a non-
gaming condition to identify the optimal cognitive task
demand for mastery experiences.
Taken together, these findings suggest that it is neces-
sary for future research to differentiate between the sub-
jective levels of cognitive task demand (as measured with
the NASA-TLX in the current study) and the actual cogni-
tive capacity objectively demanded (as measured with an
implicit reaction time task). Our findings imply that these
dimensions of cognitive task demand and its appraisal
Media and Communication, 2019, Volume 7, Issue 4, Pages 166–175 172
hold different implications for psychological constructs,
in our case recovery experiences. While the distraction
from a fatiguing task in terms of psychological detach-
ment seems connected to the actual cognitive workload,
experiences of control within the gaming experience ap-
pear to depend on subjective levels of cognitive task de-
mand. Thus, future research should account for these nu-
ances in the experience of cognitive task demand.
Beyond the open questions already raised above,
some further methodological limitations of the present
study have to be considered. First, there may be some
questions regarding how our findings can be applied to
real gaming situations. We instructed participants to play
a certain game in a predefined mode. While we chose
Tetris because cognitive task demand could easily be
modified, participants were not able to not choose a
game or a difficulty level to suit their needs on their own,
threatening external validity. Another limitation in this
regard is the assessment of cognitive task demand with
a secondary reaction time task. Usually, players do not
have to react on an audio signal while playing games. This
task might have impacted the actual recovery experience
of players in our study. Finally, for ecological reasons,
we used a single-item measure as a proxy to account
for variance in participants’ previous gaming experiences.
However, a single item cannot broadly map participants’
complex history, skills, and encounters with video games.
Thus, the covariate analysis conducted herein should be
interpreted carefully and extended by future research.
7. Conclusion
The current study replicated and extended previous work
on the intersection of video games, cognitive task de-
mand, and recovery experiences. Findings showed that
playing interactive video games could have beneficial ef-
fects on recovery. However, one has to account for differ-
ent dimensions of recovery to see the whole picture. In
the current study, playing a game contributed to psycho-
logical detachment and mastery experiences (indepen-
dent of its difficulty). For the control dimension of recov-
ery, findings indicate that difficulty plays a crucial role
in the way that low cognitive task demand contributes
particularly to control recovery experiences. Finally, ac-
counting for previous gaming experiences, the low cogni-
tive task demand condition also showed higher levels of
relaxation than no gameplay. These findings show that
research on entertainment media and recovery experi-
ences should account for the different dimensions of re-
covery and their interaction with different levels of cogni-
tive task demand. This might help to further uncover the
underlying processes of recreation from everyday stress
and strain.
Acknowledgments
We thank Dr. Daniel Roth for his support in programming
the experimental setup in Python and OpenSesame.
Conflict of Interests
The authors declare no conflict of interests.
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About the Authors
Tim Wulf (Dr. phil., University of Cologne) is a Postdoctoral Researcher at the Department of Media
and Communication at LMU Munich in Germany. His research interests include experiences and ef-
fects of media-induced nostalgia, the psychology of playing and watching video games,and persuasion
through narrative media content. For more information, see https://www.tim-wulf.de
Diana Rieger is an Associate Professor at the Department of Media and Communication at LMU
Munich. Her current work addresses the characteristics and effects of hate speech, extremist online
communication, and counter voices (e.g., counter-narratives and counter-speech). Furthermore, she
focuses on entertainment research, investigating meaningful media content, e.g., how meaning is por-
trayed in movies or online content (e.g., memes).
Anna Sophie Kümpel (Dr. rer. soc., LMU Munich) is a Postdoctoral Researcher at the Department of
Media and Communication at LMU Munich. Her research interests are focused on media effects, par-
ticularly in the context of social media, (incidental exposure to) online news, and digital games. Her
research has been published in Journal of Communication,Journal of Media Psychology, and Social
Media + Society, among others. More information: http://anna-kuempel.de
Leonard Reinecke is an Associate Professor for Media Psychology in the Department of Communi-
cation at Johannes Gutenberg University Mainz, Germany. His research interests include media uses
and effects, media entertainment, and online communication, with a special focus on the interplay of
media use, self-control, and psychological well-being.
Media and Communication, 2019, Volume 7, Issue 4, Pages 166–175 175
... There are many ways people can recover from stress, such as sleeping (Sonnentag, Binnewies and Mojza, 2008), exercising (Feuerhahn, Sonnentag and Woll, 2014), and having a vacation (Fritz and Sonnentag, 2006). Entertainment media, such as computer and digital games, have also shown stress recovery potential and positive recovery outcomes (Collins and Cox, 2014;Reinecke, 2009ab;Reinecke and Trepte, 2008;Wulf, Rieger, Kümpel, and Reinecke, 2019). However, this line of research primarily focuses on people in workplaces and examines work-related stress and its relation to the use of computer or digital games. ...
... A more fundamental reason of why social mobile games showed the highest recovery potential is probably related to the features that defined this game cluster, namely high sociability and low complexity. A recent study found that participants achieved more control experience in the low cognitive demand gaming condition than in the high cognitive demand condition (Wulf et al., 2019). Therefore, the feature of low control complexity may suggest that social mobile games could generate higher control experience for players, which is a critical component of a successful stress recovery. ...
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Presents an integrative theoretical framework to explain and to predict psychological changes achieved by different modes of treatment. This theory states that psychological procedures, whatever their form, alter the level and strength of self-efficacy. It is hypothesized that expectations of personal efficacy determine whether coping behavior will be initiated, how much effort will be expended, and how long it will be sustained in the face of obstacles and aversive experiences. Persistence in activities that are subjectively threatening but in fact relatively safe produces, through experiences of mastery, further enhancement of self-efficacy and corresponding reductions in defensive behavior. In the proposed model, expectations of personal efficacy are derived from 4 principal sources of information: performance accomplishments, vicarious experience, verbal persuasion, and physiological states. Factors influencing the cognitive processing of efficacy information arise from enactive, vicarious, exhortative, and emotive sources. The differential power of diverse therapeutic procedures is analyzed in terms of the postulated cognitive mechanism of operation. Findings are reported from microanalyses of enactive, vicarious, and emotive modes of treatment that support the hypothesized relationship between perceived self-efficacy and behavioral changes. (21/2 p ref)
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Job-stress recovery during nonwork time is an important factor for employee well-being. This article reviews the recovery literature, starting with a brief historical overview. It provides a definition of recovery that differentiates between recovery as a process and recovery as an outcome. Empirical studies have shown that recovery activities (e.g., physical exercise) and recovery experiences (e.g., psychological detachment from work) are negatively associated with strain symptoms (e.g., exhaustion) and positively associated with positive well-being indicators (e.g., vigor). Recovery activities and recovery experiences suffer when employees face a high level of job stressors. Psychological mechanisms underlying recovery seem to be similar across different temporal recovery settings (e.g., work breaks, free evenings, vacations) and seem to be enhanced in natural environments. Intervention studies have pointed to a diverse set of strategies for how everyday job-stress recovery can be supported. This article discusses 5 avenues for future research, with a particular focus on individual and contextual factors that may influence recovery as well as highlighting more complex temporal patterns than those uncovered in previous research. (PsycINFO Database Record
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This chapter refines the conceptual and operational definitions underlying secondary task reaction times (STRTs) in an attempt to clarify exactly what STRTs measure. The intent is to make the measure more useful to researchers studying changes in attention and effort during communication activities. Perusal of the communication literature yields four frequently stated operational definitions of what STRTs measure: (a) the resources required by a message, (b) the resources allocated to the message, (c) the resources available for processing, and (d) the resources remaining in the system. It is demonstrated that these pieces of the resource pie do not always covary. It is suggested that the published research best supports the interpretation of STRTs as available resources. However, interpreting STRTs as available resources cannot explain instances found in the literature where STRTs are fast and memory for messages is slow. A further modification of the model is proposed that involves integrating the limited-capacity approach to television viewing with the four definitions of the STRT. This integration yields a model that interprets STRTs as an index of resources available to the encoding subprocess and interprets memory as an index of resources available at storage. This model is then used to predict the effects of the various independent variables found in the published research on STRTs and memory. The model accurately predicts 16 out of the 20 (84%) of the results of published reaction time studies and 12 out of 14 (85%) of the published tests of memory performance.