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Are actions better than inactions? Positivity, outcome, and intentionality biases in judgments of action and inaction

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Abstract

Behavior varies along a continuum of activity, with effortful behaviors characterizing actions and restful states characterizing inactions. Despite the adaptive value of both action and inaction, we propose three biases that, in the absence of other information, increase the probability that people like, and want to pursue, action more than inaction: An action positivity bias, an action outcome bias, and an action intentionality bias. Across four experiments, participants not only evaluated actions more favorably than inactions (Experiment 1–3) but also chose to engage in actions more than inactions (Experiment 4). This action positivity bias was driven by the two interrelated biases of outcome positivity and intentionality (Experiments 1–3), such that actions (versus inactions) were spontaneously thought of as having more positive outcomes and as being more intentional. Moreover, these outcome differences played a stronger role in the action positivity bias than did the intentionality differences (Experiment 3). As balancing action and inaction is important for healthy human functioning, it is important to understand evaluative biases in this domain. All experiments were preregistered, and one involved a nationally representative sample.
Running Head: ARE ACTIONS BETTER THAN INACTIONS
Are Actions Better than Inactions?
Positivity, Outcome, and Intentionality Biases in Judgments of Action and Inaction
Aashna Sunderrajan & Dolores Albarracín
Department of Psychology
University of Illinois at Urbana-Champaign
In press: Journal of Experimental Social Psychology
Author Note
This research did not receive any specific grant from funding agencies in the public,
commercial, or not-for-profit sectors. The authors certify that they have no competing interests
that could influence their work. Correspondence concerning this article should be addressed to
Aashna Sunderrajan, Department of Psychology, University of Illinois at Urbana-Champaign, IL
61820. Contact: sunderr2@illinois.edu.
ARE ACTIONS BETTER THAN INACTIONS 2
Abstract
Behavior varies along a continuum of activity, with effortful behaviors characterizing actions and
restful states characterizing inactions. Despite the adaptive value of both action and inaction, we
propose three biases that, in the absence of other information, increase the probability that people
like, and want to pursue, action more than inaction: An action positivity bias, an action outcome
bias, and an action intentionality bias. Across four experiments, participants not only evaluated
actions more favorably than inactions (Experiment 1-3) but also chose to engage in actions more
than inactions (Experiment 4). This action positivity bias was driven by the two interrelated
biases of outcome positivity and intentionality (Experiments 1-3), such that actions (versus
inactions) were spontaneously thought of as having more positive outcomes and as being more
intentional. Moreover, these outcome differences played a stronger role in the action positivity
bias than did the intentionality differences (Experiment 3). As balancing action and inaction is
important for healthy human functioning, it is important to understand evaluative biases in this
domain. All experiments were preregistered, and one involved a nationally representative
sample.
Keywords: action, inaction, bias, evaluation, outcome, intentionality
ARE ACTIONS BETTER THAN INACTIONS 3
Are Actions Better than Inactions?
Positivity, Outcome, and Intentionality Biases in Judgments of Action and Inaction
Human behavior varies along a continuum of activity, with effortful behaviors
characterizing actions and restful states characterizing inactions (Albarracín, Sunderrajan, et al.,
2018; Albarracín et al., 2019; McCulloch et al., 2012; Zell et al., 2013). Despite the adaptive
value of both action and inaction (see Albarracín, 2020; Albarracín et al., 2019), there is tentative
evidence that people like action more than inaction. For example, in April 2015, Reddit launched
an April Fool's joke called "The Button" (Hern, 2015). It involved a button and 60 second timer
that reset each time a person, anywhere in the world, pressed this button. Although seemingly
straightforward, it took two months before the countdown was able to reach zero. Why did
people pursue such an inconsequential action? Did pressing this button appear to have more
positive outcomes than not pressing it? Did pressing this button feel more intentional than not
pressing it? If both, did positive outcomes or intentionality matter more? Although the literature
has offered some hints, there are surprisingly no clear answers to these important questions. In
this paper, we examined this action positivity bias and its behavioral consequences. In addition,
we proposed and examined two biases that, in the absence of information, should increase the
probability that people will like, and want to pursue, action over inaction: An action outcome
bias and an action intentionality bias.
Action Positivity, Action Outcome, and Action Intentionality Biases
According to the classic assumptions in decision making, people make behavioral
decisions based on their evaluation of the outcomes of a behavior (Ajzen et al., 1980; Ajzen et
al., 2019; Bar-Eli et al., 2007; Fishbein & Ajzen, 2011; Karsh et al., 2016; Osgood, 1962). For
example, when people are given the option to choose between A and B, and are informed that
ARE ACTIONS BETTER THAN INACTIONS 4
both will lead to a negative outcome, the probability between choosing one or the other should
be equal. Contrary to this expectation, however, the outcomes of action and inaction are assessed
differently, generating different emotional responses, even when their outcomes are the same (see
action effect, Kahneman & Tversky, 1982; omission bias, Ritov & Baron, 1990; Spranca et al.,
1991; actor effect, Landman, 1987; see also Rosen, 2003 for Mill’s notions of utilitarianism on
action, which describes how actions are right in proportion to how much happiness they
promote).
One asymmetry between action and inaction is that people choose inaction over action
when the action is costly (e.g., Baron & Ritov, 2004; Kahneman & Tversky, 1982; Ritov &
Baron, 1990; Spranca et al., 1991), or there is uncertainty about the outcome of the action (e.g.,
Feldman et al., 2018; Tversky & Kahneman, 1991, 1992). Therefore, when given the option,
people prefer harm by omission (e.g., withholding the truth) over harm by commission (e.g.,
lying; omission bias, Baron & Ritov, 2004; Ritov & Baron, 1990; Spranca et al., 1991), often
because they regret an action with negative outcomes more than an inaction with negative
outcomes (action effect; Kahneman & Tversky, 1982; for an analysis of how exceptions to the
norm contribute to regret for action and inaction, see Feldman & Albarracín, 2017; Fillon et al.,
2020). The other proposed asymmetry is that people choose action over inaction when they
expect positive outcomes from the action (Kahneman & Tversky, 1982; Landman, 1987). Here,
people anticipate greater pleasure, and perhaps pride, when they expect to achieve positive
outcomes through an action than through an inaction (actor effect, Landman, 1987). Hence, they
make a choice that leads to more pleasure and better outcomes.
Although this past research has identified some specific asymmetries, it has not examined
evaluations and choices concerning action and inaction in the absence of evaluative information
ARE ACTIONS BETTER THAN INACTIONS 5
about the outcomes of these behaviors. First, emotions rather than evaluations have typically
been measured. Second, the studies of regret have involved harm (e.g., Baron & Ritov, 2004;
Kahneman & Tversky, 1982; Ritov & Baron, 1990; Spranca et al., 1991) or risk (e.g., Feldman et
al., 2018; Tversky & Kahneman, 1991, 1992), whereas the studies of anticipated pleasure have
involved positive outcomes (Landman, 1987). Therefore, the evaluations of action and inaction
in the absence of evaluative information about their outcomes, and the degree to which people
anticipate more or less positive outcomes for action and inaction, have not been ascertained.
Therefore, this paper concerns how relatively neutral actions and inactions are evaluated.
Imagine that participants are presented with the classic Kahneman and Tversky (1982)
investment scenario describing two stock traders working for a financial firm: One who switches
investments (the action condition) and one who refrains from switching investments (the inaction
condition). Participants are then told that both stockbrokers earn as much money as they initially
invested, breaking even. In this scenario, will participants' evaluation of the stockbrokers’
behavior still vary as a function of whether the behavior is an action or an inaction? Is there an
action positivity bias in the absence of outcome information?
Some research suggests that action is indeed more positive than inaction, but this research
has either not been conclusive, or it has not examined specific behaviors. For example, work by
McCulloch and colleagues (2012) shows that verbs and nouns related to action are perceived as
more positive than verbs and nouns related to inaction. Yet, many of the words used in this
research had evaluative implications, such as dance, create, and connect (words with positive
evaluations), or hit, bored, and judge (words with negative evaluations). Other research has
shown more positive general attitudes toward action and inaction, especially in Western cultures
(Ireland et al., 2015; McCulloch et al., 2012; Zell et al., 2013). These notions of action and
ARE ACTIONS BETTER THAN INACTIONS 6
inaction, however, were abstract (e.g., action is important in people's lives and inaction offers
many benefits) and connected to the Protestant work ethic (see McCulloch et al., 2012).
Therefore, they did not represent evaluations of mundane actions.
There is also research that shows that, when given the option to do nothing but spend
time thinking, people choose both actions like reading a book and receiving electric shocks
(Wilson et al., 2014). Although superficially this evidence might suggest that people prefer
action over inaction, a closer look does not support this idea. First, reading a book and receiving
an electric shock are not necessarily active, nor is thinking necessarily passive. In fact, the study
by McCulloch and colleagues (2012) showed that words like decide and plan, which are forms of
thinking, were perceived as actions. Therefore, the work by Wilson and colleagues (2014) likely
does not compare action with inaction, but rather, different levels of stimulation or the greater
feelings of boredom or dread associated with the challenges of thinking versus receiving an
electric shock (see Pfattheicher et al., 2020 for the association between boredom and sadistic
behaviors). Hence, establishing if action is more positive than inaction when no outcomes are
presented remains critical to our understanding of decision making in this context.
In this paper, we hypothesized that, in the absence of any information about a behavior,
people would evaluate action as more positive than inaction due to two related biases: An action
outcome bias and an action intentionality bias. When people are asked to evaluate a behavior,
they base their decision on what outcome it produces (Baron & Hershey, 1988; see Kahneman &
Tversky, 1979 for the importance of outcome information in framing effects), and, importantly,
place greater weight on positive outcomes when making these evaluations (Baron & Hershey,
1988; Hastie & Dawes, 2009). Moreover, when people are asked to explain why they would
engage in a behavior, they spontaneously come up with positive outcomes for actions and
ARE ACTIONS BETTER THAN INACTIONS 7
negative outcomes for inaction (Albarracín, 2020). These evaluative outcomes become salient
even if no outcome information is provided and may provide the foundation for the action
positivity bias (Albarracín, 2020; Sunderrajan & Albarracín, 2020). This paper examines a
possible action outcome bias in relation to an action positivity bias.
Another important issue about judgments of action and inaction is that action may be
perceived as more intentional than inaction. For example, when participants are presented with
sentences that make use of either mental verbs (e.g., like, notice) or behavioral verbs (e.g., help,
cheat), greater causal weight is given to the agent of the argument when using a behavioral verb
(e.g., Ted in Ted helps Paul), relative to those sentences using mental verbs (Brown & Fish,
1983). Cursorily, these findings show that greater causal weight is given to the agent of the
argument when using verbs that are more active.1 This perception may be because people pay
more attention to dynamic objects, such as objects that move, and to goals involving action
(Albarracín et al., 2019). Furthermore, paying attention to any behavior often leads to attributing
more intentionality to it (Malle & Knobe, 1997), an attribution that happens automatically
(intentionality bias, Rosset, 2008). For example, descriptions of behaviors like “She set the
table,” or “She scratched herself” can have dual meanings. “Scratching oneself” may be due to
an intentional behavior when somebody is bitten by a mosquito, or accidental when somebody
hits a sharp corner. However, people who read descriptions of these behaviors are more likely to
think that the behaviors are intentional, even if they could have occurred accidentally (Rosset,
2008). Although Rosset’s work provides hints of an association between behavior and
intentionality, the research does not explicate the relation between action/inaction biases and
intentionality. Rosset (2008) only used examples of actions, which in all cases appeared
1 This work, however, fails to show how differences in behavior (action, inaction) affect evaluation, and instead,
places more emphasis on how differences in verb usage can affect the grammatical weight given to agents in
arguments.
ARE ACTIONS BETTER THAN INACTIONS 8
intentional in the absence of information about whether they were intentional or accidental. This
paper examines whether actions are automatically seen as more intentional and if this action
intentionality bias is associated with an action positivity bias.
The Relation between the Action Positivity, Action Outcome, and Action Intentionality
Biases
An important goal of our research involved understanding the relation between the
proposed action positivity bias, action outcome bias, and action intentionality bias. On the one
hand, these biases could all occur in parallel. The Protestant social ethic that permeates Western
cultures prescribes work and condemns laziness (Miller et al., 2002). Therefore, the greater effort
and intent involved in actions could result in people finding actions more desirable and more
conducive to positive outcomes than inactions. The IKEA effect (Norton et al., 2012) refers to
situations in which merely investing effort leads to more positive evaluations of an object. For
example, when participants built a storage box (versus simply inspecting one that had already
been built), they were not only willing to pay more for it but also liked it more (Norton et al.,
2012; see also Aronson & Mills, 1959; Brehm et al., 1983). In other words, changes to the
positivity, outcome, and intentionality of actions and inactions may all coexist without either
outcomes or intentionality judgments being the primary driver of differences in evaluations of
action and inaction.
On the other hand, either the action outcome bias or the action intentionality bias could
be primary. Observing that we intentionally pursue behaviors we like (Ajzen et al., 2019), the
action outcome bias could produce the action positivity bias. We might conclude that if an action
has positive outcomes, it is positive and also intentional. Correspondingly, if an inaction has
negative outcomes, we might conclude that it is negative and also unintentional. In contrast,
ARE ACTIONS BETTER THAN INACTIONS 9
observing that we intentionally perform behaviors to seek positive outcomes, we might conclude
that if an action is intentional, it will have positive outcomes and thus be positive overall (for the
creation of “if-then” implicational molecules based on observing regular patterns, see Wyer,
2019; Wyer & Albarracín, 2005). Similarly, we might conclude that if an inaction is
unintentional, it will have negative outcomes and thus be negative overall. The research we
report in this paper was conducted to explore these mutual influences and determine the extent to
which outcomes or intentionality judgments influence evaluations of action and inaction.
The Present Research
This research was designed to create a strong experimental paradigm to examine the
concepts of action, outcome, and intentionality. Although some past research suggests
associations of action with outcome positivity and intentionality judgments, these biases have not
been directly demonstrated nor have the underlying processes been investigated. Therefore, the
present research examined these biases and their interrelations by carefully manipulating action
and inaction, outcomes, and intentionality in the context of mundane behaviors like flipping a
switch and pressing a button. Experiment 1 was designed to empirically assess whether
manipulating outcome positivity affected evaluations of action and inaction. We manipulated
action and inaction, as well as the valence of the outcomes (positive, negative, and unspecified),
and measured evaluations and intentionality. Experiment 2 implemented some of the procedures
of Experiment 1, but also assessed if manipulating intentionality affected evaluations of action
and inaction. We manipulated action and inaction, as well as intentionality (high, low, and
unspecified), and measured evaluations and intentionality. Experiment 3 combined the
procedures of Experiment 1 and 2 to assess the relative role of outcomes and intentionality in
producing evaluations of action and inaction. We manipulated action and inaction, the valence of
ARE ACTIONS BETTER THAN INACTIONS 10
the outcomes (positive, negative, and unspecified), and intentionality (high, low, and
unspecified), and compared their effects on evaluations of action and inaction. Experiment 4
tested the implications of the action positivity bias on behavior, by examining whether people
showed a preference for action versus inaction in behavioral choices. All experiments were
preregistered and appropriately powered.
Experiment 1
Experiment 1 was designed to empirically assess the presence of an action positivity bias,
an action outcome bias, and an action intentionality bias, and to begin to understand the relation
between the three. We hypothesized that in the absence of information about the outcome of a
behavior (a) actions would be judged as more positive (action positivity bias) and more
intentional (action intentionality bias) than inactions and (b) actions would be expected to have
more positive outcomes and inactions to have more negative outcomes (action outcome bias). In
contrast, in the presence of outcome information, (c) evaluations of actions and inactions would
be based on whether the resultant outcome was positive or negative. That is, evaluations of
actions paired with a negative outcome would be judged more negative than evaluations of
inactions paired with a negative outcome, whereas evaluations of actions paired with a positive
outcome would be judged more positive than evaluations of inactions paired with a positive
outcome. We predicted that this pattern might hold for intentionality as well. All measures,
manipulations, and exclusions are reported below.
Method
Preregistration
The design, hypotheses, and analysis plan were all preregistered at the Open Science
Framework (https://osf.io/tb6r2/?view_only=52728760eb9d4582a23189f1283c4f94).
ARE ACTIONS BETTER THAN INACTIONS 11
Power Analysis
This experiment employed a 2 (behavior: flipping a switch, not flipping a switch) x 3
(outcome: positive, negative, unspecified) between-subjects design. As our hypotheses were
presented in univariate terms (predicting main effects and interactions for each outcome
separately), this power analysis was based on the univariate results of a pilot study that employed
the same design.2 A df = 0.18 (a small effect, according to Cohen’s 1992 effect size convention)
was chosen because it was the size of the smallest effect across all univariate analyses observed
in the pilot study. An α = 0.01 was chosen to minimize the likelihood of false positives. Thus, to
determine the sample size needed to detect an effect of this size in Experiment 1, a power
analysis was conducted for our factorial design, with α = 0.01, power = 0.80, number of groups =
6, and a df = 0.18. This analysis revealed that the required sample size was N = 458.
Participants
Four hundred and sixty-five participants, recruited from Amazon Mechanical Turk,
participated in exchange for 75 cents. To be eligible for participation, individuals had to be 18
years of age or older and current residents of the United States. To control for data quality, we
included a qualification that prevented the same participants from completing the experiment
more than once. A sensitivity analysis with α = 0.01, power = 0.80, number of groups = 6, and
our actual sample size revealed that we could detect a minimum effect of df = 0.17. The sample
consisted of 202 females (263 males), and ranged in age from 19 to 74 (M = 32.27, SD = 10.65).
Informed consent was obtained from all participants before proceeding with the experiment.
Procedure
2 Based on a multivariate test, with α = 0.01, power = 0.80, number of predictors = 2, number of groups = 6, number
of outcomes = 3, and a f2(V) = 0.03, our required sample size was N = 319.
ARE ACTIONS BETTER THAN INACTIONS 12
Participants were randomly assigned to one of six conditions in a 2 (behavior: flipping a
switch, not flipping a switch) x 3 (outcome: positive, negative, unspecified) between-subjects
design. All participants were told, “Imagine yourself flipping [or not flipping] a switch.
Participants in the positive- and negative-outcome conditions were given additional information.
Participants in the positive-outcome condition read:
Imagine yourself flipping a switch as you leave a room. When you flip the switch, you
turn off the lights in the (now empty) room. You end up conserving some energy.
In contrast, participants in the negative-outcome condition read:
Imagine yourself flipping a switch as you leave a room. When you flip the switch, you
turn on the lights in the (now empty) room. You end up wasting some energy.
Participants in the unspecified-outcome condition were presented with the behavior alone, with
no additional information on the outcome. This three-level manipulation thus served to modify
the outcome information associated with each behavior.
After reading the assigned behavior, participants provided various ratings. Participants
were asked to rate the level of action involved in the behavior on two scales from 1 (complete
inaction/completely passive) to 7 (complete action/completely active), which served as a
manipulation check. Participants were asked to evaluate the behavior on two scales from 1
(completely negative/completely not desirable) to 7 (completely positive/completely desirable).
Participants were also asked to rate the intentionality of the behavior on two scales from 1
(complete absence of a goal/no intention to achieve something) to 7 (full presence of a
goal/strong intention to achieve something). Each set of items correlated highly and were
averaged to form three indices (action: r(462) = .85, evaluation: r(462) = .88, intentionality:
r(462) = .86). Participants were then asked to complete individual difference measures. These
ARE ACTIONS BETTER THAN INACTIONS 13
measures included the Action-Inaction Value Scale, Temporal Action Initiation Scale (Freitas et
al., 2002), Impulsive Decision Making Scale (Hinson et al., 2003), Unethical Decision Making
Scale (Detert et al., 2008), and the State Self-Control Capacity Scale (Twenge et al., 2004). As
data on these scales were collected for exploratory work to be used in future projects, they are
not included in any of the analysis below. Upon the completion of these measures, participants
were debriefed and thanked for their participation.
Results
Manipulation Checks
We first performed checks to determine if each of our manipulations had the intended
effect. Results supported the conclusion that all manipulations worked as expected.
The effect of the behavior manipulation. An independent samples t-test was conducted
to gauge differences in rated action or inaction across the two behavioral conditions. As intended,
the behavior describing an action was perceived as more active (M = 5.69, SD = 1.37) than the
behavior describing an inaction (M = 3.35, SD = 2.09), t(462) = 14.26, p < .001, d = 1.32.
The effect of the outcome manipulation. An analysis of variance was conducted to
detect differences in evaluations across the three outcome conditions. Participants evaluated a
behavior paired with positive outcome information (M = 6.11, SD = 1.21) most positively,
followed by unspecified outcome information (M = 4.87, SD = 1.24), and then by negative
outcome information (M = 3.16, SD = 1.91); F(2, 461) = 151.64, p < .001, d = 1.40.
Experimental Effects
A multivariate analysis of variance was conducted to determine whether there were
differences in evaluations and intentionality as a function of the two-level (action, inaction)
behavior condition and the three-level (positive, negative, unspecified) outcome condition.3 As
shown by the F-ratios, there was a significant interaction between behavior and outcome
3 In Experiments 1-3, our analyses were preregistered as a multivariate analysis of variance because that is how we
obtained the analyses of variance results even though the focus of our experiments was not on the multivariate test.
For completion, however, we report the multivariate interaction for each experiment.
ARE ACTIONS BETTER THAN INACTIONS 14
information for both evaluations and intentions, F(6, 914) = 5.93, p < .001, d = 0.99. We describe
these findings below in relation to the questions guiding this research. Table 1 presents the Ms,
SDs, t-tests for pairwise contrasts, and F-ratios corresponding to these analyses.
ARE ACTIONS BETTER THAN INACTIONS 15
Table 1
Ms, SDs, and F-ratios for Experiment 1.
Conditions and Statistics Statistics
Evaluation
Flipping a Switch
M(SD)
Not Flipping a Switch
M(SD)
t for action
vs inaction contrast
Descriptives
Positive-outcome 6.40(0.81) 5.82(1.46) 3.07[0.50]**
Unspecified-outcome (control) 5.40(1.12) 4.34(1.13) 5.92[0.94]***
Negative-outcome 3.13(2.06) 3.19(1.77) -0.17[0.03]
Contrasts for outcome conditions
t for positive-outcome vs unspecified-outcome contrast 6.39[1.02]*** 7.06[1.14]***
t for unspecified-outcome vs negative-outcome
contrast 8.54[1.38]*** 4.86[0.78]***
t for positive-outcome vs negative-outcome contrast 12.96[2.10]*** 10.03[1.62]***
Direct effects for outcome conditions
F(2, 458) simple main effects for outcome 101.13[1.45]*** 63.57[1.41]***
Main effects and interactions
F(1, 458) main effect: behavior 15.64[0.28]***
F(2, 458) main effect: outcome 159.54[1.62]***
F(2, 458) interaction: behavior x outcome 5.78[0.24]**
Intentionality
Flipping a Switch
M(SD)
Not Flipping a Switch
M(SD)
t for action
vs inaction contrast
ARE ACTIONS BETTER THAN INACTIONS 16
Descriptives
Positive-outcome 5.64(1.45) 5.02(1.77) 2.36[0.38]*
Unspecified-outcome (control) 5.81(1.22) 3.59(1.94) 8.59[1.37]***
Negative-outcome 3.31(2.14) 3.10(1.83) 0.68[0.11]
Contrasts for outcome conditions
t for positive-outcome vs unspecified-outcome contrast -0.81[0.13] 4.77[0.77]***
t for unspecified-outcome vs negative-outcome
contrast 8.93[1.44]*** 1.66[0.26]
t for positive-outcome vs negative-outcome contrast 7.85[1.27]*** 6.63[1.07]***
Direct effects for outcome conditions
F(2, 458) simple main effects for outcome 48.26[0.60]*** 24.98[0.96]***
Main effects and interactions
F(1, 458) main effect: behavior 39.11[0.51]***
F(2, 458) main effect: outcome 59.53[0.95]***
F(2, 458) interaction: behavior x outcome 14.25[0.43]***
Note. The t-statistic is reported for each of the differences based on planned contrasts. The simple effects report the F value for the
simple effect of outcome under action and under inaction. The F-statistic is reported for each of the main effects and interaction.
Values in brackets represent Cohen’s d effect sizes. Asterisks represent the significance of the contrasts.
*** p < .001 ** p < .01 * p < .05
ARE ACTIONS BETTER THAN INACTIONS 17
Whether an action positivity and an action intentionality bias are present is best answered
by considering judgments of behavior when no additional information is provided. Thus, we first
focused on the conditions with no information about the outcomes of a behavior. As shown in
Table 1, when no outcome information was provided, participants evaluated actions as more
positive than inactions and judged actions as more intentional than inactions. Therefore, in the
absence of outcome information, the answer to our first question is yes, indicating both an action
positivity bias and an action intentionality bias.
The manipulation of outcome information in Experiment 1 was introduced to determine
whether the action positivity bias stems from people spontaneously associating positive or
negative outcomes with a behavior. Thus, the larger difference in evaluations of actions and
inactions should be present when no outcomes are described, which, as shown in Table 1, was
the case. Moreover, the difference in the evaluations of actions and inactions was smaller when
the outcomes were described as positive and nonsignificant when the outcomes were described
as negative. In sum, it appeared that participants spontaneously imputed positive outcomes to
actions and negative outcomes to inactions and that providing specific information about
outcomes thus reduced the action positivity bias.
Moreover, Experiment 1 also examined if the action outcome bias influences the action
intentionality bias. As shown in Table 1, the data supported this notion. Relative to participants in
conditions without outcome information, participants perceived actions as less intentional when
they were described as having a negative outcome and inactions as more intentional when they
were described as having a positive outcome. Moreover, the difference in the intentionality of
actions and inactions was smaller when the outcomes were described as positive and
nonsignificant when the outcomes were described as negative.
ARE ACTIONS BETTER THAN INACTIONS 18
Discussion
The purpose of Experiment 1 was to empirically evaluate the presence of an action
positivity bias, an action outcome bias, and an action intentionality bias, and to begin to
understand the relation between the three. We found that participants evaluated actions as more
positive, and associated actions with more outcome positivity and intentionality, than inactions.
This finding is remarkable because participants could have rationalized that they had good
reasons to not flip a switch. However, they still evaluated not flipping a switch as more negative
than flipping it. As the behavior used in this study was mundane, such a bias supports the notion
of an inherent preference for action and an inherent association between action and outcome
positivity and intentionality. Interestingly, the action positivity bias was smaller when both
actions and inactions had positive outcomes and disappeared when both actions and inactions
had negative outcomes. Moreover, the outcome positivity manipulation also affected perceived
intentionality, suggesting that the action positivity bias can drive the action intentionality bias.
Experiment 2
Experiment 2 was designed to assess the degree to which differences in the perceived
intentionality of action and inaction lead to corresponding evaluative differences between the
two. We hypothesized that (a) when presented with behaviors not described as being intentional
or unintentional, actions would be judged as more positive and more intentional than inactions.
In contrast, (b) when presented with behaviors described as either intentional or unintentional,
intentionality information would lead to differences in evaluation. Specifically, inactions
associated with high intentionality would be perceived as more positive than inactions without
intentionality information. Moreover, actions associated with low intentionality would be
ARE ACTIONS BETTER THAN INACTIONS 19
perceived as more negative than actions without intentionality information. All measures,
manipulations, and exclusions are reported below.
Method
Preregistration
The design, hypotheses, and analysis plan were all preregistered at the Open Science
Framework (https://osf.io/srb7s/?view_only=fdeed2f3cd0c4166ad16603c460cf2ec).
Power Analysis
This experiment employed a 2 (behavior: pressing a button, not pressing a button) x 3
(intentionality: high, low, unspecified) between-subjects design. As our hypotheses were
presented in univariate terms (predicting main effects and interactions for each outcome
separately), this power analysis was based on the univariate results of a pilot study that employed
the same design.4 A df = 0.19 (a small effect, according to Cohen’s 1992 effect size convention)
was chosen because it was the size of the smallest effect across all univariate analyses observed
in the pilot study. An α = 0.01 was chosen to minimize the likelihood of false positives. Thus, to
determine the sample size needed to detect an effect of this size in Experiment 2, a power
analysis was conducted for a factorial design, with α = 0.01, power = 0.80, number of groups =
6, and a df = 0.19. This analysis revealed that the required sample size was N = 377.
Participants
Three hundred and seventy-eight undergraduates, recruited from a Midwestern university
subject pool, participated in exchange for partial course credit. Nine participants had missing
values, resulting in a final sample size of N = 369. A sensitivity analysis with α = 0.01, power =
0.80, number of groups = 6, and our actual sample size revealed that we could detect a minimum
effect of dw = 0.20. The sample included 244 females (125 males), and ranged in age from 18 to
27 years (M = 19.65, SD = 0.07). Informed consent was obtained from all participants before
proceeding with the experiment.
4 Based on a multivariate test, with α = 0.01, power = 0.80, number of predictors = 2, number of groups = 6, number
of outcomes = 3, and a f2(V) = 0.028, our required sample size was N = 342.
ARE ACTIONS BETTER THAN INACTIONS 20
Procedure
Participants were randomly assigned to one of six conditions in a 2 (behavior: pressing a
button, not pressing a button) x 3 (intentionality: high, low, unspecified) between-subjects
design. All participants were told, “Imagine yourself pressing [or not pressing] a button.
Participants in the high- and low-intentionality conditions were given additional information.
Participants in the high-intentionality condition read:
Imagine yourself pressing a button because pressing it is consistent with a particular
purpose you have. Imagine yourself pressing a button in order to achieve a goal or
purpose. Imagine yourself pressing a button intently, with a goal in mind.
In contrast, participants in the low-intentionality condition read:
Imagine yourself pressing a button, even though pressing it is not consistent with any
particular purpose you have. Imagine yourself pressing a button without a goal or
purpose. Imagine yourself pressing a button incidentally, without a goal in mind.
Participants in the unspecified-intentionality condition were presented with the behavior alone,
with no additional information on intentionality. This three-level manipulation was designed to
modify the levels of intentionality associated with each behavior.
After reading the assigned behavior, participants provided various ratings of action,
evaluation, and intentionality (see Experiment 1). Each set of items correlated highly and were
averaged to form three indices (action: r(367) = .64, evaluation: r(367) = .71, intentionality:
r(367) = .81). Participants were then asked to complete individual difference measures (see
Experiment 1). As data on these scales were collected for exploratory work to be used in future
projects, they are not included in any of the analysis below. Upon the completion of these
measures, participants were debriefed and thanked for their participation.
Results
Manipulation Checks
ARE ACTIONS BETTER THAN INACTIONS 21
We first performed checks to determine if each of our manipulations had the intended
effect. Results supported the conclusion that all manipulations worked as expected.
The effect of the behavior manipulation. An independent samples t-test was conducted
to gauge differences in rated action or inaction across the two behavioral conditions. As
predicted, the behavior describing an action was perceived as more active (M = 4.90, SD = 1.64)
than the behavior describing an inaction (M = 3.44, SD = 1.58), t(367) = 8.72, p < .001, d = 0.91.
The effect of the intentionality manipulation. An analysis of variance was conducted to
detect differences in intentionality across the three intentionality conditions. Intentionality was
rated as highest when a behavior was associated with high intentionality (M = 5.29, SD = 1.44),
followed by unspecified intentionality (M = 4.28, SD = 1.80), and then by low intentionality (M
= 2.50, SD = 1.61); F(2, 366) = 93.41, p < .001, d = 1.17.
Experimental Effects
A multivariate analysis of variance was conducted to determine whether there were
differences in intentionality and evaluation as a function of the two-level (action, inaction)
behavior condition, and the three-level (high, low, unspecified) intentionality condition. As
shown by the F-ratios, there was a significant interaction between behavior and intentionality
information for both evaluations and intentions, F(6, 724) = 6.28, p < .001, d = 1.00. We describe
these findings below in relation to the questions guiding this research. Table 2 presents the Ms,
SDs, t-tests for pairwise contrasts, and F-rations corresponding to this analysis.
ARE ACTIONS BETTER THAN INACTIONS 22
Table 2
Ms, SDs, and F-ratios for Experiment 2.
Conditions and Statistics Statistics
Evaluation
Pressing a Button
M(SD)
Not Pressing a Button
M(SD)
t for action
vs inaction contrast
Descriptives
High-intentionality 5.55(1.10) 4.46(1.47) 4.71[0.84]***
Unspecified-intentionality (control) 4.71(1.01) 3.82(0.94) 5.04[0.91]***
Low-intentionality 3.54(1.32) 3.57(1.21) -0.12[0.02]
Contrasts for intentionality conditions
t for high-intentionality vs unspecified-intentionality
contrast 4.43[0.79]*** 2.84[0.52]**
t for unspecified-intentionality vs low-intentionality contrast 5.60[1.00]*** 1.28[0.23]
t for high-intentionality vs low-intentionality contrast 9.29[1.66]*** 3.64[0.66]***
Direct effects for intentionality conditions
F(2, 363) simple main effects for intentionality 45.31[1.20]*** 9.07[0.64]***
Main effects and interactions
F(1, 363) main effect: behavior 27.58[0.48]***
F(2, 363) main effect: intentionality 45.85[0.95]***
F(2, 363) interaction: behavior x intentionality 7.70[0.36]**
Intentionality
Pressing a Button
M(SD)
Not Pressing a Button
M(SD)
t for action
vs inaction contrast
ARE ACTIONS BETTER THAN INACTIONS 23
Descriptives
High-intentionality 5.67(1.15) 4.90(1.60) 3.13[0.56]**
Unspecified-intentionality (control) 4.90(1.83) 3.62(1.53) 4.20[0.76]***
Low-intentionality 2.32(1.57) 2.69(1.65) -1.27[0.23]
Contrasts for intentionality conditions
t for high-intentionality vs unspecified-intentionality
contrast 2.83[0.50]** 4.48[0.82]***
t for unspecified-intentionality vs low-intentionality contrast 8.53[1.52]*** 3.18[0.59]**
t for high-intentionality vs low-intentionality contrast 13.71[2.44]*** 7.47[1.36]***
Direct effects for intentionality conditions
F(2, 363) simple main effects for intentionality 79.31[1.32]*** 30.34[1.11]***
Main effects and interactions
F(1, 363) main effect: behavior 12.00[0.29]**
F(2, 363) main effect: intentionality 99.30[1.42]***
F(2, 363) interaction: behavior x intentionality 8.93[0.35]***
Note. The t-statistic is reported for each of the differences based on planned contrasts. The simple effects report the F value for the
simple effect of intentionality under action and under inaction. The F-statistic is reported for each of the main effects and interaction.
Values in brackets represent Cohen’s d effect sizes. Asterisks represent the significance of the contrasts.
*** p < .001 ** p < .01
ARE ACTIONS BETTER THAN INACTIONS 24
As in Experiment 1, we first tested for the action positivity bias and action intentionality
bias by examining evaluation and intentionality when no additional information was provided.
As shown in Table 2, when no information about intentionality was provided, participants
evaluated actions as more positive than inactions and judged actions as more intentional than
inactions. Therefore, in the absence of information about intentionality, the answer to our first
question is yes, supporting both an action positivity bias and an action intentionality bias.
Experiment 2 also examined if the action intentionality bias affects the action positivity
bias. As shown in Table 2, our results supported this hypothesis. Participants evaluated actions as
more negative when they were described as having low intentionality than when intentionality
was not described. Participants also evaluated inactions as more positive when they were
described as having high intentionality than when intentionality was not described. For this
reason, the difference in the evaluations of actions and inactions was smaller when intentionality
was described as high than when it was not described. Moreover, the difference in the
evaluations of actions and inactions was smaller when intentionality was described as low than
when it was not described.
Discussion
The purpose of Experiment 2 was to assess if perceived intentionality differences
contribute to evaluative differences between actions and inactions. The results replicated the
action positivity and action intentionality biases found in Experiment 1. Moreover, we found that
actions were perceived as more positive than inactions because actions are perceived as more
intentional than inactions. Therefore, when actions are associated with lower intentionality and
inactions are associated with higher intentionality, the action positivity bias disappears.
Experiment 3
ARE ACTIONS BETTER THAN INACTIONS 25
Experiments 1 and 2 assessed the relative roles of outcome and intentionality information
on the action positivity bias. However, neither experiment obtained evidence about the relative
weight of outcome and intentionality information because neither manipulated both factors
within the same experiment. Thus, Experiment 1 showed that spontaneous thoughts about
outcomes led actions to appear more positive than inactions, but it is possible that intentionality
was equally or more important. Likewise, Experiment 2 showed that spontaneous thoughts about
intentionality led actions to appear more positive than inactions, but it is possible that outcomes
were equally or more important. Hence, Experiment 3 filled this gap. In this experiment, we
retested the hypotheses of the prior experiments, but also sought to determine the weight of
outcome and intentionality information in influencing evaluations of behavior. In particular, we
sought to determine if (a-b) the effect of the outcome manipulation on evaluations was greater
than the direct effect of the intentionality manipulation on evaluations. We also examined
whether (c) intention and outcome information interact (possibly in an additive manner) to affect
evaluations of action and inaction. All measures, manipulations, and exclusions are reported
below.
Method
Preregistration
The design, hypotheses, and analysis plan were all preregistered at the Open Science
Framework (https://osf.io/gpvue/?view_only=235f2e8ef35d42018de4ce7a4554ed72).
Power Analysis
This experiment employed a 2 (behavior: flipping a switch, not flipping a switch) x 3
(outcome: positive, negative, unspecified) x 3 (intentionality: high, low, unspecified) between-
subjects design. As our hypotheses were presented in univariate terms (predicting main effects
and interactions for each outcome separately), this power analysis was based on the univariate
results of a pilot study that employed the same design.5 A df = 0.15 (a small effect, according to
5 Based on a multivariate test, with α = 0.01, power = 0.80, number of predictors = 3, number of groups = 18,
number of outcomes = 3, and a f2(V) = 0.014, our required sample size was N = 514.
ARE ACTIONS BETTER THAN INACTIONS 26
Cohen’s 1992 effect size convention) was chosen because it was the size of the smallest effect
across all univariate analyses observed in the pilot study. An α = 0.01 was chosen to minimize
the likelihood of false positives. Thus, to determine the sample size needed to detect an effect of
this size in Experiment 3, a power analysis was conducted for a factorial design, with α = 0.01,
power = 0.80, number of groups = 18, and a df = 0.15. This analysis revealed that the required
sample size was N = 752. However, as this experiment was part of a larger project, and data were
collected through a third-party platform, it was not possible to control for the exact number of
participants recruited, and we ended up with more participants than necessary.
Participants
Nine hundred and ninety participants, recruited from a nationally representative sample
on Dynata, participated in exchange for $1.22. To be eligible for participation, individuals had to
be 18 years of age or older and current residents of the United States. A sensitivity analysis with
α = 0.01, power = 0.80, number of groups = 18, and our actual sample size revealed that we
could detect a minimum effect of df = 0.13. The sample consisted of 452 females, 381 males, and
157 people who preferred not to answer. The sample ranged in age from 18 to 88 (M = 46.38, SD
= 17.14). Informed consent was obtained from all participants before proceeding with the
experiment.
Procedure
Participants were randomly assigned to one of eighteen conditions in a 2 (behavior:
flipping a switch, not flipping a switch) x 3 (outcome: positive, negative, unspecified) x 3
(intentionality: high, low, unspecified) between-subjects design. All participants were told,
Imagine yourself flipping [or not flipping] a switch.” Participants in the positive- and negative-
outcome conditions and participants in the high- and low-intentionality conditions were given
additional information (as described in Experiments 1 and 2, respectively). Participants in the
positive outcome x high-intentionality condition read:
ARE ACTIONS BETTER THAN INACTIONS 27
Imagine yourself flipping the light switch as you leave a room, because flipping it is
consistent with a particular purpose you have. You flip the switch intently, with a goal in
mind. When you flip the switch, you turn off the lights in the (now empty) room. You end
up conserving some energy.
Participants in the positive outcome x low-intentionality condition read:
Imagine yourself flipping the light switch as you leave a room, even though flipping it is
not consistent with any particular purpose you have. You flip the switch incidentally,
without a goal in mind. When you flip the switch, you turn off the lights in the (now
empty) room. You end up conserving some energy.
Participants in the negative outcome x high-intentionality condition read:
Imagine yourself flipping the light switch as you leave a room, because flipping it is
consistent with a particular purpose you have. You flip the switch intently, with a goal in
mind. When you flip the switch, you turn on the lights in the (now empty) room. You end
up wasting some energy.
Participants in the negative outcome x low-intentionality condition read:
Imagine yourself flipping the light switch as you leave a room, even though flipping it is
not consistent with any particular purpose you have. You flip the switch incidentally,
without a goal in mind. When you flip the switch, you turn on the lights in the (now
empty) room. You end up wasting some energy.
Participants in the unspecified conditions were presented with the behavior alone, with no
additional information on the outcome or intentionality. This manipulation thus served to modify
both the outcome information and the levels of intentionality associated with each behavior.
ARE ACTIONS BETTER THAN INACTIONS 28
After reading the assigned behavior, participants provided various ratings of action,
evaluation, and intentionality (see Experiment 1). Each set of items correlated highly and were
averaged to form three indices (action: r(975) = .63, evaluation: r(949) = .72, intentionality:
r(953) = .72). Upon the completion of this task, participants were debriefed and thanked for their
participation.
Results
Manipulation Checks on Ratings of Action/Inaction and Evaluations
We first performed checks to determine if each of our manipulations had the intended
effect. Results supported the conclusion that all manipulations worked as expected.
The effect of the behavior manipulation. An independent samples t-test was conducted
to gauge differences in rated action or inaction across the two behavioral conditions. As
expected, the behavior describing an action was perceived as more active (M = 5.01, SD = 1.85)
than the behavior describing an inaction (M = 4.08, SD = 1.96), t(988) = 7.70, p < .001, d = 0.49.
The effect of the outcome manipulation. An analysis of variance was conducted to
detect differences in evaluations across the three outcome conditions. Evaluations were most
positive when a behavior was paired with positive outcome information (M = 5.38, SD = 1.76),
followed by unspecified outcome information (M = 4.65, SD = 1.83), and then by negative
outcome information (M = 3.43, SD = 1.96); F(2, 971) = 92.44, p < .001, d = 0.73.
The effect of the intentionality manipulation. An analysis of variance was conducted to
detect differences in intentionality across the three intentionality conditions. Intentionality was
rated highest when a behavior was associated with high intentionality (M = 4.69, SD = 1.90),
followed by unspecified intentionality (M = 4.34, SD = 2.10), and then by low intentionality (M
= 4.09, SD = 2.10); F(2, 970) = 7.14, p = .001, d = 0.23.
Experimental Effects
A multivariate analysis of variance was conducted to determine whether there were
differences in intentionality and evaluation as a function of the two-level (action, inaction)
behavior condition, the three-level (positive, negative, unspecified) outcome condition, and the
ARE ACTIONS BETTER THAN INACTIONS 29
three-level (high, low, unspecified) intentionality condition. As shown by the F-ratios, there was
a significant interaction between behavior, intentionality, and outcome, F(12, 2850) = 1.81, p = .
04, d = 0.97. We describe these findings below in relation to the questions guiding this research.
ARE ACTIONS BETTER THAN INACTIONS 30
Table 3
Ms, SDs, and F-ratios for Experiment 3.
Conditions and Statistics Statistics
Evaluation
Flipping a Switch
M(SD)
Not Flipping a Switch
M(SD)
t for action
vs inaction contrast
Descriptives
Unspecified intentionality
Positive-outcome 5.89(1.31) 5.26(1.65) 2.23[0.43]*
Unspecified-outcome (control) 5.31(1.84) 3.68(1.87) 4.43[0.84]***
Negative-outcome 3.15(2.04) 3.25(1.78) -0.38[0.07]
High intentionality
Positive-outcome 5.51(1.71) 5.34(1.61) 0.45[0.09]
Unspecified-outcome (control) 5.56(1.24) 4.62(1.88) 2.61[0.52]*
Negative-outcome 3.35(1.95) 3.44(1.88) -0.25[0.05]
Low intentionality
Positive-outcome 5.22(2.17) 5.07(1.96) 0.36[0.07]
Unspecified-outcome (control) 4.75(1.73) 3.96(1.55) 2.60[0.50]*
Negative-outcome 3.75(1.99) 3.64(2.10) 0.30[0.06]
Contrasts for outcome conditions
Within unspecified intentionality
t for positive-outcome vs unspecified-outcome contrast 1.94[0.38] 4.45[0.86]***
t for unspecified-outcome vs negative-outcome contrast 5.98[1.15]*** 1.40[0.27]
t for positive-outcome vs negative-outcome contrast 8.55[1.66]*** 6.03[1.17]***
ARE ACTIONS BETTER THAN INACTIONS 31
Conditions and Statistics Statistics
Within high intentionality
t for positive-outcome vs unspecified-outcome contrast 0.04[0.01] 2.07[0.40]*
t for unspecified-outcome vs negative-outcome contrast 6.44[1.23]*** 3.19[0.61]**
t for positive-outcome vs negative-outcome contrast 6.07[1.16]*** 5.56[1.07]***
Within low intentionality
t for positive-outcome vs unspecified-outcome contrast 1.13[0.22] 3.28[0.63]**
t for unspecified-outcome vs negative-outcome contrast 2.88[0.55]** 0.92[0.18]
t for positive-outcome vs negative-outcome contrast 3.65[0.70]*** 3.73[0.72]***
Direct effects for outcome conditions
F(2, 950) simple main effects for outcome 64.50[0.52]*** 39.92[0.41]***
Contrasts for intentionality conditions
Within unspecified outcome
t for high-intentionality vs unspecified-intentionality contrast 0.52[0.10] 2.37[0.46]*
t for unspecified-intentionality vs low-intentionality contrast 1.53[0.30] -0.66[0.13]
t for high-intentionality vs low-intentionality contrast 2.24[0.43]* 1.95[0.37]
Within positive outcome
t for high-intentionality vs unspecified-intentionality contrast -1.42[0.27] 0.12[0.02]
t for unspecified-intentionality vs low-intentionality contrast 1.98[0.38]* 0.54[0.10]
t for high-intentionality vs low-intentionality contrast 0.71[0.14] 0.76[0.15]
Within negative outcome
t for high-intentionality vs unspecified-intentionality contrast 0.63[0.12] 0.55[0.11]
t for unspecified-intentionality vs low-intentionality contrast -1.68[0.32] -1.04[0.20]
t for high-intentionality vs low-intentionality contrast -1.07[0.21] -0.52[0.10]
ARE ACTIONS BETTER THAN INACTIONS 32
Conditions and Statistics Statistics
Direct effects for intentionality conditions
F(2, 950) simple main effects for intentionality 35.59[0.38]*** 19.74[0.29]***
Main effects and interactions
F(1, 950) main effect: behavior 16.19[0.26]***
F(2, 950) main effect: outcome 95.97[0.63]***
F(2, 950) main effect: intentionality 1.67[0.08]
F(2, 950) interaction: behavior x outcome 8.40[0.19]***
F(2, 950) interaction: behavior x intentionality 1.17[0.07]
F(4, 950) interaction: outcome x intentionality 3.48[0.12]**
F(4, 950) interaction: behavior x outcome x intentionality 0.62[0.05]
Intentionality
Flipping a Switch
M(SD)
Not Flipping a Switch
M(SD)
t for action
vs inaction contrast
Descriptives
Unspecified intentionality
Positive-outcome 5.61(1.56) 5.10(1.70) 1.62[0.31]
Unspecified-outcome (control) 5.36(1.78) 3.43(1.86) 5.55[1.07]***
Negative-outcome 3.14(2.16) 3.22(1.85) -0.21[0.04]
High intentionality
Positive-outcome 5.31(1.80) 5.15(1.61) 0.51[0.10]
Unspecified-outcome (control) 5.19(1.63) 4.74(1.93) 1.26[0.25]
Negative-outcome 3.83(1.93) 3.93(1.98) 0.01[0.00]
Low intentionality
Positive-outcome 4.47(2.16) 4.66(1.84) -0.50[0.10]
ARE ACTIONS BETTER THAN INACTIONS 33
Conditions and Statistics Statistics
Unspecified-outcome (control) 4.25(2.56) 3.71(1.83) 1.34[0.26]
Negative-outcome 3.50(2.11) 3.97(2.23) -1.15[0.22]
Contrasts for outcome conditions
Within unspecified intentionality
t for positive-outcome vs unspecified-outcome contrast 0.78[0.15] 4.85[0.93]***
t for unspecified-outcome vs negative-outcome contrast 5.92[1.14]*** 0.59[0.11]
t for positive-outcome vs negative-outcome contrast 6.89[1.34]*** 5.46[1.96]***
Within high intentionality
t for positive-outcome vs unspecified-outcome contrast 0.37[0.07] 1.15[0.23]
t for unspecified-outcome vs negative-outcome contrast 3.62[0.72]*** 2.12[0.41]*
t for positive-outcome vs negative-outcome contrast 3.92[0.74]*** 3.48[0.66]**
Within low intentionality
t for positive-outcome vs unspecified-outcome contrast 0.53[0.10] 2.70[0.52]**
t for unspecified-outcome vs negative-outcome contrast 1.77[0.34] -0.67[0.13]
t for positive-outcome vs negative-outcome contrast 2.36[0.45]* 1.78[0.34]
Direct effects for outcome conditions
F(2, 950) simple main effects for outcome 0.82[0.06] 1.98[0.09]
Contrasts for intentionality conditions
Within unspecified outcome
t for high-intentionality vs unspecified-intentionality contrast -0.53[0.10] 3.50[0.67]**
t for unspecified-intentionality vs low-intentionality contrast 2.88[0.56]** -0.78[0.15]
t for high-intentionality vs low-intentionality contrast 2.43[0.47]* 2.79[0.54]**
Within positive outcome
t for high-intentionality vs unspecified-intentionality contrast -0.94[0.18] 0.24[0.05]
ARE ACTIONS BETTER THAN INACTIONS 34
Conditions and Statistics Statistics
t for unspecified-intentionality vs low-intentionality contrast 3.15[0.61]** 1.29[0.25]
t for high-intentionality vs low-intentionality contrast 2.20[0.42]* 1.44[0.28]
Within negative outcome
t for high-intentionality vs unspecified-intentionality contrast 2.03[0.39]* 1.89[0.36]
t for unspecified-intentionality vs low-intentionality contrast -0.88[0.17] -1.91[0.37]
t for high-intentionality vs low-intentionality contrast 1.12[0.22] -0.12[0.02]
Direct effects for intentionality conditions
F(2, 950) simple main effects for intentionality 6.61[0.17]** 5.38[0.15]**
Main effects and interactions
F(1, 950) main effect: behavior 6.08[0.16]*
F(2, 950) main effect: outcome 47.28[0.44]***
F(2, 950) main effect: intentionality 8.00[0.18]***
F(2, 950) interaction: behavior x outcome 8.02[0.18]***
F(2, 950) interaction: behavior x intentionality 4.09[0.13]*
F(4, 950) interaction: outcome x intentionality 4.23[0.13]**
F(4, 950) interaction: behavior x outcome x intentionality 1.12[0.07]
Note. The t-statistic is reported for each of the differences based on planned contrasts. The simple effects report the F value for the
simple effect of outcome and intentionality under action and under inaction. The F-statistic is reported for each of the main effects and
interaction. Values in brackets represent Cohen’s d effect sizes. Asterisks represent the significance of the contrasts.
*** p < .001 ** p < .01 * p < .05
WHY PEOPLE LIKE PRESSING BUTTONS 35
Experiments 1 and 2 showed that actions are judged to be more positive and more
intentional than inactions in the absence of outcome and intention information, respectively. As
shown in Table 3, this finding replicated in Experiment 3. Specifically, when no outcome or
intentionality information was provided, participants evaluated actions as more positive and
more intentional than inactions. Therefore, in the absence of additional information, the answer
to our first question is again yes, indicating both an action positivity bias and an action
intentionality bias.
Experiment 3 further replicated the findings that providing outcome and intentionality
information alters spontaneous evaluations. These results supported the notion that participants
spontaneously assumed that actions led to more positive outcomes and were more intentional,
whereas inactions led to less positive outcomes and were less intentional. The data comparable to
Experiment 1 (see Table 3) showed that, relative to conditions without outcome information,
participants judged actions as less positive and less intentional when they were described as
having a negative outcome, and inactions as more positive and more intentional when they were
described as having a positive outcome. Similarly, the data comparable to Experiment 2 (see
Table 3) showed that relative to conditions without intentionality information, participants
judged actions as less positive and less intentional when they were described as being low in
intentionality, and inactions as more positive and more intentional when they were described as
being high in intentionality.
What is the relative weight of the action outcome and action intentionality biases?
Experiments 1 and 2 provided good evidence that both the action outcome bias and action
intentionality bias have an influence on each other but could not answer the question of whether
outcome positivity or intentionality plays a stronger role in determining the action positivity bias.
Experiment 3 was designed to answer this question. By comparing the direct effect of
WHY PEOPLE LIKE PRESSING BUTTONS 36
manipulating outcome information with the direct effect of manipulating intentionality
information on evaluations, we see that the direct effects of outcome information were stronger.
Discussion
The purpose of Experiment 3 was to assess the role of both outcome and intentionality in
evaluations of action and inaction. The findings showed that participants evaluated actions as
more positive and more intentional than inactions, replicating prior experiments. However, when
outcome and intentionality information both varied, the effect of outcome information on
evaluations was stronger, implying that the action outcome bias dominates over the action
intentionality bias.
Experiment 4
Experiment 4 was designed to test the implications of the action positivity bias on
behavior, by examining whether evaluations favoring action would similarly translate into
preferences for active behaviors. We hypothesized that, (a) when given the opportunity to engage
in an action or an inaction, actions would be preferred, suggesting that biases for action extend to
behaviors as well. Consistent with prior experiments, we further hypothesized that, (b) when
asked to evaluate the behavior engaged in, actions might be evaluated more favorably than
inactions. All measures, manipulations, and exclusions are reported below.
Method
Preregistration
The design, hypotheses, and analysis plan were all preregistered at the Open Science
Framework (https://osf.io/7pu5z/?view_only=9a8e4a5df403488d8bd90cd7eca8cb75).
Power Analysis
This experiment employed a 2 (behavior: pressing a button, not pressing a button)
between-subjects design. The power analysis was based on results of a pilot study that employed
the same design. A df = 0.29 (a small-to-medium effect, according to Cohen’s 1992 effect size
convention) was chosen because it was the size of the effect observed in the pilot study. An α =
WHY PEOPLE LIKE PRESSING BUTTONS 37
0.01 was chosen to minimize the likelihood of false positives. Thus, to determine the sample size
needed to detect an effect of this size in Experiment 4, a power analysis was conducted, with α =
0.01, power = 0.80, df = 1, and a dw = 0.29. This analysis indicated that the required sample size
should be N = 139.
Participants
One hundred and forty-one participants, recruited from Amazon Mechanical Turk,
participated in exchange for $0.75. To be eligible for participation, individuals had to be 18 years
of age or older and current residents of the United States. To control for data quality, we included
a qualification that prevented the same participants from completing the experiment more than
once. A sensitivity analysis with α = 0.01, power = 0.80, df = 1, and our actual sample size
revealed that we could detect a minimum effect of dw = 0.29. The sample consisted of 68 females
(73 males), and ranged in age from 18 to 65 (M = 37.86, SD = 11.57). Informed consent was
obtained from all participants before proceeding with the experiment.
Procedure
Participants were informed that they would be participating in a decision-making task and
would be assigned to either express their responses by selecting a specific button (action
condition) or checking whether a specific button was already selected based on given prompts
(inaction condition) (see Figure 1). Participants were told, however, that they could indicate their
preference for which task they would like to do. This indication was taken as an assessment of
whether participants preferred action or inaction. Following their indication, participants were
asked to complete the decision-making task, which always corresponded to participants’
indicated preference. Participants were then asked to respond to a few questions about the task.
These questions involved rating how active and effortful the task was on a scale from 1 (not at
WHY PEOPLE LIKE PRESSING BUTTONS 38
all) to 5 (extremely), which served as the manipulation checks, and rating how much participants
enjoyed the task on a scale from 1 (not at all) to 5 (extremely). Participants were then asked to
complete an individual difference measure. This measure included the Health Lifestyle and
Personal Control Questionnaire (Darviri et al., 2014). As data on this scale was collected for
exploratory work to be used in future projects, it is not included in any of the analysis below.
Upon the completion of this measure, participants were debriefed and thanked for their
participation.
Figure 1
WHY PEOPLE LIKE PRESSING BUTTONS 39
The behavior conditions in Experiment 4. Participants were randomly assigned to either express
their responses by selecting a specific button (action condition) or checking whether a specific
button was already selected based on given prompts (inaction condition).
Results
Manipulation Checks on Ratings of Action/Inaction and Effort
An independent samples t-test was conducted to examine differences in rated action or
inaction across the two experimental conditions. As intended, the task involving an action was
perceived as more active (M = 2.95, SD = 1.04) than the task involving an inaction (M = 1.50,
SD = 0.93), t(139) = 6.32, p < .001, d = 1.47. The same held true for effort; for action: M = 2.19,
SD = 1.17; for inaction: M = 1.58, SD = 1.02; t(139) = 2.35, p = .02, d = 0.56.
Behavior Preference
A chi-square goodness of fit test was conducted to determine if there were differences in
the number of participants selecting the active versus the inactive tasks. Results revealed a
statistically significant difference in the percentage of participants who selected each option,
χ2(1) = 61.34, p < .001, d = 0.97, with just over 75% of the participants selecting the active task.
Behavior Evaluation
An independent samples t-test was also conducted to test whether participants found the
active task more enjoyable than the inactive task. Results revealed no significant difference in
how favorably participants evaluated the active task (M = 2.99, SD = 1.24), relative to the
inactive task (M = 2.79, SD = 1.59), t(139) = 0.69, p = .49, d = 0.15. Interestingly then, people
expect actions to be more positive (Experiment 1-3) and chose them more frequently
(Experiment 4). However, people do not always enjoy action more than inaction.
WHY PEOPLE LIKE PRESSING BUTTONS 40
Discussion
The purpose Experiment 4 was to investigate whether conclusions about the action
positivity bias had implications for behavioral preferences. Experiment 4 extended the results
from previous experiments, showing that people choose action over inaction. Once people had
engaged in the task, however, the actual experience with the task (i.e., pressing or not pressing a
button) was evaluatively neutral. As actual experience with a behavior should shape evaluations
based on the nature of that experience (Fazio et al., 1978), our results suggest that the behavior of
pressing buttons is neutral in valence.
General Discussion
The goals of this paper were to test whether neutral actions and inactions differ in
evaluation, whether neutral actions and inactions differ in intentionality, and whether
assumptions in outcome evaluations matter more than assumptions about intentionality. In our
experiments, we found that (a) people evaluate actions as more positive (an action positivity
bias) and more intentional (an action intentionality bias) than inactions, (b) people assume
positive outcomes for actions and more negative outcomes for inaction (an action outcome bias),
and that (c) assumed outcome positivity is most influential than assumed intentionality. We also
found that (d) these differences are reflected in behavioral preferences as well.
Our findings complement the literature examining biases associated with actions and
inactions. This literature has almost solely focused on variations of the action effect, which
shows that people feel more regret for actions over inactions (Kahneman & Tversky, 1982), and
the associated omission bias, which occurs when people show a preference for omissions over
commissions when faced with a decision that may lead to a negative consequence (Baron &
Ritov, 1994; Ritov & Baron, 1990; Spranca et al., 1991). The prolific decision-making literature
WHY PEOPLE LIKE PRESSING BUTTONS 41
exploring these, and associated, effects has demonstrated that actions produce more blame and
more regret than inactions. In this context, our research contributes in two ways. First,
considering that, in everyday life, many behaviors one encounters are trivial in nature,
knowledge about such mundane judgments are important. Second, we propose an interrelated set
of biases in which actions may be evaluated more positively because of differences in expected
outcomes or differences in expected intentionality. Although both outcomes and intentionality
play a role, an action bias in outcome evaluations seems to play a larger role in the action
positivity bias than an action bias in intentionality.
Past research has shown that action is easier than inaction in behavioral change contexts.
For example, people have an easier time forming action than inaction goals (Albarracín, Wang, et
al., 2018), behavioral skills programs already tend to emphasize what new behaviors to introduce
(Albarracín et al., 2005), and telling people what not to do elicits psychological reactance
(Brehm, 1966; Rains, 2013; Rosenberg & Siegel, 2018). However, an action focus may not
always be ideal. For example, people experience greater difficulty in response to multiple action
demands than in response to multiple inaction demands (Albarracín, Wang, et al., 2018).
Supporting this possibility, a series of experiments using a multiple Go/No-go task showed that
both misses and false alarms were more frequent when participants had to press a key in
response to three targets than when they had to not press a key in response to three targets. This
pattern is attributable to the greater cognitive load posed by the multiple action goals and by
people's natural focus on action. Corroborating this finding, when participants were encouraged
to focus on inaction, the difference in errors decreased. Thus, even though people have an easier
time forming action than inaction goals, requesting inactions appears necessary for better self-
WHY PEOPLE LIKE PRESSING BUTTONS 42
regulation and performance. Under these conditions, increasing the perceived positivity and
intentionality of inaction might prove beneficial.
Another way to change preferences for action or inaction is through how choices are
framed. Information about a behavior can either emphasize the benefits of taking action (i.e., a
gain-framed appeal) or the costs of failing to take action (i.e., a loss-framed appeal). Recent work
shows that this type of framing can be used to explain the prevalence of an action bias.
Gavaruzzi and colleagues (2011) presented participants with a scenario describing a cancer
diagnosis and asked them to choose between one of two treatment options: Watchful waiting or
surgery (similar to the design used in Fagerlin et al., 2005). What was manipulated was the
presentation of the inactive option. In one condition, participants were informed that if they
chose to wait, the cancer could possibly metastasize, making surgery impossible. Therefore, the
presentation of the inactive choice emphasized the possible loss associated with this option. In
the other condition, participants were informed that watchful waiting did not preclude future
surgery, thereby emphasizing the gains associated with this inactive option. Their results found
that surgery was preferred over watchful waiting only when the inactive choice was framed as a
loss, but the preference for watchful waiting was stronger when action remained an option for the
future. This suggests that how the outcomes of an inactive choice are framed can affect
preferences and, in most situations, framing inaction as a deferred decision, with room for future
action, leads to the attenuation of the action bias. It would thus be interesting to replicate these
results and identity how similar gain-frame approaches can interact with positivity and
intentionality to attenuate the action bias in other areas of health.
It is important to consider the limitations of this research. First, the conclusions from this
study are constrained by our methodological choices. For example, in three out of the four
WHY PEOPLE LIKE PRESSING BUTTONS 43
experiments described here, participants were asked to imagine doing or not doing something.
Yet, is imagining a behavior similar to engaging in it? Maybe. Recent work suggests that, when
considering threatening stimuli, our imagination can affect the neural pathways in our brains
much like actual behavior (Reddan et al., 2018; see also Benoit et al., 2019). If the same holds
true for neutral behaviors, it is likely that imagining a behavior is similar to enacting it. And, in
fact, the choices in Experiment 4 suggest that the judgments people report are consistent with
overt choice for action. Nonetheless, it will be important to replicate these results with actual
behavior. Second, work by Zell and colleagues (2013) has found that cultural differences exist in
attitudes towards action and inaction. Specifically, people from nations that score higher in
dialecticism (generally, East Asian societies) report more positive attitudes toward action than
people from nations that score lower in dialecticism. The research reported in this paper is
exclusively based on participants from the United States. Therefore, although we find evidence
for an action positivity bias, an action outcome bias, and an action intentionality bias, we cannot
confirm whether these biases are present in other societies as well. Investigating these judgments
with international samples is thus necessary. Finally, although we find statistically significant
results, we also need to consider the practical significance of these findings (Cohen, 1990). The
importance and meaning of an effect size depends on multiple factors, such as the context of the
study and the importance of the outcomes (Henson, 2006). In our study, we consistently find
medium-to-large effects, suggesting that, when presented with two choices (to act or not), people
are likely to choose and enjoy action. Although this bias to press buttons might seem trivial, an
overall preference for action could become detrimental to health. For example, excessive action
is conducive to stress, diminished health, and poor psychological well-being (e.g., in situations
involving smoking or excessive alcohol consumption, Albarracín et al., 2009; or situations
WHY PEOPLE LIKE PRESSING BUTTONS 44
involving chronic stress, Lupien et al., 2009). Understanding the magnitude of this bias in
everyday life is thus vital.
Concluding Remarks
In this paper, we found that people not only evaluate actions more favorably than
inactions (Experiment 1-3), but also prefer to engage in actions more than inaction (Experiment
4). Importantly, these preferences for action over inaction are driven by interrelated biases in
outcomes and intentionality, with the action outcome bias predominating over the action
intentionality bias (Experiments 3). This work thus offers a possible way of balancing action and
inaction by countering these biases. In particular, whereas the positive outcomes of actions need
not be emphasized, we recommend that practitioners belabor the positive outcomes of inactions.
Likewise, whereas the negative outcomes of inactions need not be emphasized, we recommend
that practitioners belabor the negative outcomes of actions. As more research accumulates, these
ideas could directly be applied to the development of successful programs to increase behaviors
with positive outcomes, and decrease behaviors with negative outcomes, for individuals and
society.
WHY PEOPLE LIKE PRESSING BUTTONS 45
Open Practices
All experiments in this paper were preregistered. For the preregistration plans, see:
https://osf.io/tb6r2/?view_only=52728760eb9d4582a23189f1283c4f94 (Experiment 1),
https://osf.io/srb7s/?view_only=fdeed2f3cd0c4166ad16603c460cf2ec (Experiment 2),
https://osf.io/gpvue/?view_only=235f2e8ef35d42018de4ce7a4554ed72 (Experiment 3), and
https://osf.io/7pu5z/?view_only=9a8e4a5df403488d8bd90cd7eca8cb75 (Experiment 4).
WHY PEOPLE LIKE PRESSING BUTTONS 46
WHY PEOPLE LIKE PRESSING BUTTONS 47
References
Ajzen, I., Fishbein, M., & Heilbroner, R. L. (1980). Understanding attitudes and predicting
social behavior. Englewood Cliffs, NJ: Prentice-hall.
Ajzen, I., Fishbein, M., Lohmann, S., & Albarracín, D. (2019). The influence of attitudes on
behavior. In D. Albarracín, & B. T. Johnson (Eds.), The Handbook of Attitudes (2nd Ed.,
pp. 197-255). New York, NY: Routledge. doi:10.4324/9781315178103
Albarracín, D. (2020). Action and inaction in a social world: Prediction and change of attitudes
and behavior. Cambridge University Press.
Albarracín, D., Gillette, J. C., Earl, A. N., Glasman, L. R., Durantini, M. R., & Ho, M. H. (2005).
A test of major assumptions about behavior change: a comprehensive look at the effects
of passive and active HIV-prevention interventions since the beginning of the epidemic.
Psychological Bulletin, 131(6), 856. doi:10.1037/0033-2909.131.6.856
Albarracín, D., Sunderrajan, A., & Dai, W. (2018). Action, inaction, and actionability:
Definitions and implications for communications and interventions to change behaviors.
In D. A. Hope & R. A. Bevins (Eds), Change and Maintaining Change (pp. 75-99).
Springer.
Albarracín, D., Sunderrajan, A., Dai, W., & White, B. X. (2019). The social creation of action
and inaction: From concepts to goals to behaviors. Advances in Experimental Social
Psychology, 60, 223-271. doi:10.1016/bs.aesp.2019.04.001
Albarracín, D., Wang, W., & Leeper, J. (2009). Immediate increase in food intake following
exercise messages. Obesity, 17(7), 1451-1452. doi:10.1038/oby.2009.16
WHY PEOPLE LIKE PRESSING BUTTONS 48
Albarracín, D., Wang, W., & McCulloch, K. C. (2018). Action dominance: The performance
effects of multiple action demands and the benefits of an inaction focus. Personality and
Social Psychology Bulletin, 44(7), 996-1007. doi:10.1177/0146167218756031
Aronson, E., & Mills, J. (1959). The effect of severity of initiation on liking for a group. The
Journal of Abnormal and Social Psychology, 59(2), 177.
Bar-Eli, M., Azar, O. H., Ritov, I., Keidar-Levin, Y., & Schein, G. (2007). Action bias among
elite soccer goalkeepers: The case of penalty kicks. Journal of Economic Psychology,
28(5), 606-621. doi:10.1016/j.joep.2006.12.001
Baron, J., & Hershey, J. C. (1988). Outcome bias in decision evaluation. Journal of Personality
and Social Psychology, 54(4), 569.
Baron, J., & Ritov, I. (2004). Omission bias, individual differences, and normality.
Organizational Behavior and Human Decision Processes, 94(2), 74-85.
doi:10.1016/j.obhdp.2004.03.003
Benoit, R. G., Paulus, P. C., & Schacter, D. L. (2019). Forming attitudes via neural activity
supporting affective episodic simulations. Nature Communications, 10(1), 2215.
doi:10.1038/s41467-019-09961-w
Brehm, J. W. (1966). A theory of psychological reactance. Academic Press.
Brehm, J. W., Wright, R. A., Solomon, S., Silka, L., & Greenberg, J. (1983). Perceived difficulty,
energization, and the magnitude of goal valence. Journal of Experimental Social
Psychology, 19(1), 21-48.
Brown, R., & Fish, D. (1983). The psychological causality implicit in language. Cognition,
14(3), 233-274. doi:10.1016/0010-0277(83)90006-9
Cohen, J. (1990). Things I have learned so far. American Psychologist, 45, 1304 –1312.
WHY PEOPLE LIKE PRESSING BUTTONS 49
Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155-159. doi:10.1037/0033-
2909.112.1.155
Darviri, C., Alexopoulos, E. C., Artemiadis, A. K., Tigani, X., Kraniotou, C., Darvyri, P., &
Chrousos, G. P. (2014). The Healthy Lifestyle and Personal Control Questionnaire
(HLPCQ): A novel tool for assessing self-empowerment through a constellation of daily
activities. BMC Public Health, 14(1), 995. doi:10.1186/1471-2458-14-995
Detert, J. R., Treviño, L. K., & Sweitzer, V. L. (2008). Moral disengagement in ethical decision
making: A study of antecedents and outcomes. Journal of Applied Psychology, 93(2),
374-391. doi:10.1037/0021-9010.93.2.374
Fagerlin, A., Zikmund-Fisher, B. J., & Ubel, P. A. (2005). Cure me even if it kills me:
preferences for invasive cancer treatment. Medical decision making, 25(6), 614-619.
doi:10.1177/0272989X05282639
Fazio, R. H., Zanna, M. P., & Cooper, J. (1978). Direct experience and attitude-behavior
consistency: An information processing analysis. Personality and Social Psychology
Bulletin, 4(1), 48-51.
Feldman, G., & Albarracín, D. (2017). Norm theory and the action-effect: The role of social
norms in regret following action and inaction. Journal of Experimental Social
Psychology, 69, 111-120. doi:10.1016/j.jesp.2016.07.009
Fillon, A., Kutscher, L., & Feldman, G. (2020). Impact of past behavior normality: Meta-analysis
of exceptionality effect. Cognition and Emotion.
Fishbein, M., & Ajzen, I. (2011). Predicting and changing behavior: The reasoned action
approach. Taylor & Francis.
WHY PEOPLE LIKE PRESSING BUTTONS 50
Freitas, A. L., Liberman, N., Salovey, P., & Higgins, E. T. (2002). When to begin? Regulatory
focus and initiating goal pursuit. Personality and Social Psychology Bulletin, 28(1), 121-
130. doi:10.1177/0146167202281011
Gavaruzzi, T., Lotto, L., Rumiati, R., & Fagerlin, A. (2011). What makes a tumor diagnosis a call
to action? On the preference for action versus inaction. Medical Decision Making, 31(2),
237-244. doi:10.1177/0272989X10377116
Hastie, R., & Dawes, R. M. (2009). Rational choice in an uncertain world: The psychology of
judgment and decision making. Sage Publications.
Henson, R. K. (2006). Effect size measures and meta-analytic thinking in counseling psychology
research. The Counseling Psychologist, 34, 601–629. doi:10.1177/0011000005283558
Hern, A. (2015, June 8). Reddit's mysterious button experiment is over. The Guardian. Retrieved
from https://www.theguardian.com/technology/2015/jun/08/reddits-mysterious-button-
experiment-is-over
Hinson, J. M., Jameson, T. L., & Whitney, P. (2003). Impulsive decision making and working
memory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29(2),
298-306. doi:10.1037/0278-7393.29.2.298
Ireland, M. E., Hepler, J., Li, H., & Albarracín, D. (2015). Neuroticism and attitudes toward
action in 19 countries. Journal of Personality, 83(3), 243-250. doi:10.1111/jopy.12099
Kahneman, D., & Tversky, A. (1979). Prospect Theory: An analysis of decision under risk.
Econometrlca, 47, 263-291.
Kahneman, D., & Tversky, A. (1982). The psychology of preferences. Scientific American,
246(1), 160-173. doi:10.1038/scientificamerican0182-160
WHY PEOPLE LIKE PRESSING BUTTONS 51
Karsh, N., Eitam, B., Mark, I., & Higgins, E. T. (2016). Bootstrapping agency: How control-
relevant information affects motivation. Journal of Experimental Psychology: General,
145(10), 1333-1350. doi:10.1037/xge0000212.
Landman, J. (1987). Regret and elation following action and inaction: Affective responses to
positive versus negative outcomes. Personality and Social Psychology Bulletin, 13(4),
524-536.
Lupien, S. J., McEwen, B. S., Gunnar, M. R., & Heim, C. (2009). Effects of stress throughout the
lifespan on the brain, behaviour and cognition. Nature Reviews Neuroscience, 10(6), 434-
445. doi:10.1038/nrn2639
Malle, B. F., & Knobe, J. (1997). Which behaviors do people explain? A basic actor–observer
asymmetry. Journal of Personality and Social Psychology, 72(2), 288.
McCulloch, K. C., Li, H., Hong, S., & Albarracín, D. (2012). Naïve definitions of action and
inaction: The continuum, spread, and valence of behaviors. European Journal of Social
Psychology, 42(2), 227-234. doi:10.1002/ejsp.860
Miller, M. J., Woehr, D. J., & Hudspeth, N. (2002). The meaning and measurement of work
ethic: Construction and initial validation of a multidimensional inventory. Journal of
Vocational Behavior, 60(3), 451-489. doi:10.1006/jvbe.2001.1838
Norton, M. I., Mochon, D., & Ariely, D. (2012). The IKEA effect: When labor leads to love.
Journal of Consumer Psychology, 22(3), 453-460. doi:10.1016/j.jcps.2011.08.002
Osgood, C. E. (1962). Studies on the generality of affective meaning systems. American
Psychologist, 17(1), 10.
WHY PEOPLE LIKE PRESSING BUTTONS 52
Pfattheicher. S., & Lazarevic, L., & Westgate, E., & Schindler, S. (2020). On the relation of
boredom and sadistic aggression. Journal of Personality and Social Psychology.
doi:10.31234/osf.io/r67xg
Rains, S. A. (2013). The nature of psychological reactance revisited: A meta-analytic review.
Human Communication Research, 39(1), 47-73. doi:10.1111/j.1468-2958.2012.01443.x
Reddan, M. C., Wager, T. D., & Schiller, D. (2018). Attenuating neural threat expression with
imagination. Neuron, 100(4), 994-1005. doi:10.1016/j.neuron.2018.10.047
Ritov, I., & Baron, J. (1990). Reluctance to vaccinate: Omission bias and ambiguity. Journal of
Behavioral Decision Making, 3(4), 263-277. doi:10.1002/bdm.3960030404
Rosen, F. (2003). Classical utilitarianism from Hume to Mill. New York, NY: Routledge.
Rosenberg, B. D., & Siegel, J. T. (2018). A 50-year review of psychological reactance theory: Do
not read this article. Motivation Science, 4(4), 281. doi:10.1037/mot0000091
Rosset, E. (2008). It’s no accident: Our bias for intentional explanations. Cognition, 108(3), 771-
780. doi:10.1016/j.cognition.2008.07.001
Spranca, M., Minsk, E., & Baron, J. (1991). Omission and commission in judgment and choice.
Journal of Experimental Social Psychology, 27(1), 76-105. doi:10.1016/0022-
1031(91)90011-T
Sunderrajan, A., & Albarracín, D. (2020). Naïve definitions of action and inaction: Judges’
ratings of free associations and preselected words using natural language processing and
top-down coding. [Unpublished manuscript]. Department of Psychology, University of
Illinois, Champaign, United States.
Tversky, A., & Kahneman, D. (1991). Loss aversion in riskless choice: A reference-dependent
model. The Quarterly Journal of Economics, 106(4), 1039-1061. doi:10.2307/2937956
WHY PEOPLE LIKE PRESSING BUTTONS 53
Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of
uncertainty. Journal of Risk and Uncertainty, 5(4), 297-323. doi:10.1007/BF00122574
Twenge, J. M., Muraven, M., & Tice, D. M. (2004). Measuring state self-control: Reliability,
validity, and correlations with physical and psychological stress. Unpublished
manuscript, San Diego State University, San Diego, CA.
Wilson, T. D., Reinhard, D. A., Westgate, E. C., Gilbert, D. T., Ellerbeck, N., Hahn, C., ... &
Shaked, A. (2014). Just think: The challenges of the disengaged mind. Science,
345(6192), 75-77. doi:10.1126/science.1250830
Wyer Jr, R. S. (2019). Cognitive organization and change: An information-processing approach.
New York, NY: Psychology Press.
Wyer, R. S., & Albarracín, D. (2005). Belief formation, organization, and change: Cognitive and
motivational influences. In D. Albarracín, B. T. Johnson, & M. P. Zanna (Eds.), The
Handbook of Attitudes (pp. 273-322). New York, NY: Psychology Press.
Zell, E., Su, R., Li, H., Ho, M. R., Hong, S., Kumkale, T., . . . Albarracín, D. (2013). Cultural
differences in attitudes toward action and inaction: The role of dialecticism. Social
Psychological and Personality Science, 4(5), 521-528. doi:10.1177/1948550612468774
... One possible direction would be to assess the attitude toward socially undesirable inactions instead of actions. Compared to actions, inactions are often seen as less intentional (Rosset, 2008;Sunderrajan & Albarracín, 2021), appear less consequential (Baron & Ritov, 2004), receive less attention (Kahneman & Miller, 1986), elicit weaker emotional reactions (Landman, 1987;Zhou et al., 2010), and are judged as less negative even in the presence of undesirable outcomes (Sunderrajan & Albarracín, 2021). Therefore, people are likely to be more concerned with controlling their disposition toward socially undesirable actions than inactions. ...
... One possible direction would be to assess the attitude toward socially undesirable inactions instead of actions. Compared to actions, inactions are often seen as less intentional (Rosset, 2008;Sunderrajan & Albarracín, 2021), appear less consequential (Baron & Ritov, 2004), receive less attention (Kahneman & Miller, 1986), elicit weaker emotional reactions (Landman, 1987;Zhou et al., 2010), and are judged as less negative even in the presence of undesirable outcomes (Sunderrajan & Albarracín, 2021). Therefore, people are likely to be more concerned with controlling their disposition toward socially undesirable actions than inactions. ...
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