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Habits in Everyday Life: Thought, Emotion, and Action
Wendy Wood and Jeffrey M. Quinn
Texas A&M University
Deborah A. Kashy
Michigan State University
To illustrate the differing thoughts and emotions involved in guiding habitual and nonhabitual behavior, 2
diary studies were conducted in which participants provided hourly reports of their ongoing experiences.
When participants were engaged in habitual behavior, defined as behavior that had been performed
almost daily in stable contexts, they were likely to think about issues unrelated to their behavior,
presumably because they did not have to consciously guide their actions. When engaged in nonhabitual
behavior, or actions performed less often or in shifting contexts, participants’ thoughts tended to
correspond to their behavior, suggesting that thought was necessary to guide action. Furthermore, the
self-regulatory benefits of habits were apparent in the lesser feelings of stress associated with habitual
than nonhabitual behavior.
In this research we address the relation between ongoing
thought, emotion, and everyday action. In the standard predictive
models in social psychology, behavior is a product of a series of
cognitive and affective events, typically preceded most closely by
conscious intentions to perform the act (Ajzen, 1987; Eagly &
Chaiken, 1993; Gollwitzer, 1999; although see Greve, 2001).
Intentions can be generated through thoughtful deliberation or
relatively superficial processes. Research that has measured peo-
ple’s intentions and then behavior has provided strong support for
these models (see meta-analytic reviews by Armitage & Conner,
2001; Randall & Wolff, 1994; Sheppard, Hartwick, & Warshaw,
1988).
However, not all behaviors are preceded by conscious inten-
tions. Only minimal, sporadic thought is required to initiate, im-
plement, and terminate actions that in the past have been repeated
in stable contexts. Such actions reflect habits, and Ouellette and
Wood (1998) demonstrated that specific intentions to perform
repeated behaviors are not good predictors of such acts. Instead,
habit performance reflects the routine repetition of past acts that is
cued by stable features of the environment. In this view, the
disposition or tendency to perform habitual behaviors is implicit, it
is expressed through the performance itself, and it may not be
reflected in people’s thoughts or reported intentions. Thus, predic-
tive models of behavior indicate that action can emerge from
conscious intentions or from implicit guides developed through
past performance.
Research on the organization of memory systems is consistent
with the idea that behavior can be generated through multiple
processes. For example, neuropsychological studies of memory
have examined patients with brain lesions that yield selective
memory impairment or have used functional neuroimaging tech-
niques to examine activation of brain regions during performance
of behavioral tasks (see reviews by Schacter, 1992, 1995). In this
research, noncognitive habit and skill memory have been linked to
a complex of specific brain systems involving the basal ganglia,
cerebellum, and motor neocortex (Gabrieli, 1998; Squire, Knowl-
ton, & Musen, 1993). These differ from the systems associated
with priming and other forms of nonconscious memory and from
the systems involved in declarative, conscious memory for facts
and events. In addition, a number of studies of memory perfor-
mance have supported a dual-process model in which habitual
patterns and conscious recollection contribute independently to
memory performance (e.g., Caldwell & Masson, 2001; Hay &
Jacoby, 1996; Jacoby, Yonelinas, & Jennings, 1997). In sum,
research on behavior prediction and on memory systems has
distinguished habitual responses from more thoughtful modes of
behavior generation.
Despite the emerging evidence for habitual patterns of respond-
ing, social psychological models of habit are in the early stages of
development. In part, this is because of the often-noted problem of
how to construct appropriate measures of habit (Eagly & Chaiken,
1993; Verplanken & Aarts, 1999). The standard measure is the
frequency with which a behavior has been performed in the past.
Although past performance frequency appears to be an effective
predictor of future behavior, this relation is not necessarily infor-
mative about habits. Ajzen (2002) elaborated on these concerns in
his critique of the behavior prediction research that has demon-
strated the effects of past behavior on future behavior. In his view,
the residual effect of past behavior on future behavior emerges to
the extent that intentions are weakly formed, poorly specified, or
unrealistic. Thus, past behavior effects emerge to the extent that
the true predictors of behavior are not accurately captured in
self-reports of intention. Although it is reasonable to suppose that
stronger intentions are better predictors of behavior than weaker
Wendy Wood and Jeffrey M. Quinn, Department of Psychology, Texas
A&M University; Deborah A. Kashy, Department of Psychology, Michi-
gan State University.
The second study served as Jeffrey M. Quinn’s master’s thesis, under
the direction of Wendy Wood. This research was supported by National
Institute of Mental Health Grant 1R01MH619000-01 awarded to Wendy
Wood. We thank Aysun Bursali for her thoughtful suggestions concerning
the project and Roy Baumeister for his comments on an earlier version of
this article.
Correspondence concerning this article should be addressed to Wendy
Wood, Department of Psychology, Texas A&M University, College Sta-
tion, Texas 77843. E-mail: w-wood@tamu.edu
Journal of Personality and Social Psychology Copyright 2002 by the American Psychological Association, Inc.
2002, Vol. 83, No. 6, 1281–1297 0022-3514/02/$5.00 DOI: 10.1037//0022-3514.83.6.1281
1281
ones, this explanation does not account for the mounting evidence
of the systematic, independent effects of past behavior on future
behavior. That is, past behavior is the primary predictor of future
behavior when habits have developed through past repetition in
stable contexts, whereas intentions are the primarily predictor
when behaviors are relatively novel or performed in unstable
contexts (Albarracin, Kumkale, & Johnson, 2002; Ferguson &
Bibby, 2002; Ouellette & Wood, 1998; Verplanken, Aarts, van
Knippenberg, & Moonen, 1998). This pattern of findings is con-
sistent with the view that behavior can be guided by automatic
processes outside of conscious awareness as well as through more
thoughtful processing modes.
1
Past studies demonstrating the differential impact of habits and
intentions have largely focused on behavior prediction, and they
yield limited evidence of the cognitive processes associated with
behavior performance. To provide a basis for further development
of psychological theorizing and measurement of habits, the present
research offers a descriptive view of the nature and functioning of
repeated behaviors in everyday life. Consistent with Rozin’s
(2001) call for more descriptive research in social psychology, we
evaluate habits as they are naturally “situated in the structure of
social life” (p. 13). We used a diary methodology to assess peo-
ple’s thoughts and emotions while performing habitual behaviors.
Our basic analytic strategy was to compare these with people’s
thoughts and emotions during performance of nonhabits. We could
then evaluate whether behaviors that have been performed fre-
quently in the past, especially frequent behaviors in stable con-
texts, are appropriately defined as habits in the sense that they can
be performed with minimal explicit thought. The present investi-
gation also estimated the incidence of habitual behaviors in every-
day life. Given that conscious self-regulation of judgments and
behavior requires some effort (Baumeister, Bratslavsky, Muraven,
& Tice, 1998; Baumeister, Muraven, & Tice, 2000), we expected
to find that people often rely on habits as an efficient, nontaxing
mode of initiating and controlling daily activities. Finally, we
examined whether the habitual versus nonhabitual mode of behav-
ior performance is linked to emotional experience, especially to
self-regulatory emotions of stress and perceived control.
Automaticity and Conditioning of Repeated Acts
in Stable Contexts
Habitual behaviors typically emerge from repeated actions in
stable contexts. This repetition can reflect people’s attempts to
achieve some goal or people’s unintentional reactions, when they
are unaware of what has been learned (Squire et al., 1993).
Repetition of a behavior in a given setting promotes automaticity
because the cognitive processing that initiates and controls the
response comes to be performed quickly, in parallel with other
activities, and with the allocation of minimal focal attention (e.g.,
Posner & Snyder, 1975). In the present work, we focus on one of
the defining features of automatic, environmentally triggered ac-
tion, that being people’s awareness of action.
2
Stable contexts facilitate this propensity to perform repeated
behaviors with minimal cognitive monitoring. Although no situa-
tion ever completely maps onto earlier experiences, responses
proceed quickly without limiting processing capacity to the extent
that the current environment is similar to the one in which the
behavior was performed in the past. Research on transfer of
learning and stimulus generalization have addressed the question
of what makes features of stimuli and contexts interchangeable for
learning and performance (e.g., Bouton, Nelson, & Rosas, 1999;
Proctor & Dutta, 1993). For our purposes, contexts are stable to the
extent that they present the same contextual cues integral to
performing the response and to the extent that they are similarly
conducive to fulfilling an actor’s goals. As Barker and Schoggen
(1978) noted in their analysis of the genotype of behavior settings,
contexts may vary in superficial attributes but be stable in the
features supporting performance. Unstable contexts are ones in
which shifts in the supporting environment implicate alternate
goals or challenge the smooth initiation, execution, and termina-
tion of practiced responses. Because of the importance of context
stability to automatic responding, we define habits as behaviors
that are repeated in stable contexts.
How do stable environmental events cue behavior? In classic
learning theories, features of the environment directly cue well-
practiced behavior through stimulus–response linkages (e.g., Hull,
1943; Spence, 1956). However, more recent models of cognitive
processing outline how external events mobilize action by auto-
matically triggering behavioral goals or intentions, which then can
be implemented with minimal thought (e.g., Bargh & Ferguson,
2000; Heckhausen & Beckmann, 1990). In the next section, we
consider how thought might be implicated in environmentally
triggered action.
Thought and Habitual Behavior
Popular culture contains a variety of images of the extent to
which people’s thoughts correspond to their ongoing behavior.
1
Although Ajzen (2002) concluded that “empirical tests of the habitu
-
ation hypothesis have so far met with little success,” (p. 107), his analysis
was based on the bivariate correlations between past behavior, intention,
and future behavior. Specifically, he noted that Ouellette and Wood (1998)
found that past behavior and intention were both correlated with future
behavior, and that these correlations emerged in domains and contexts in
which habits were likely to develop as well as ones in which they were not.
Yet, these bivariate correlations are not an appropriate test of the habitu-
ation perspective. Past behavior is often highly correlated with intention,
presumably because people reason from their typical behavior and report
intentions that correspond to what they usually do. A more informative test
is provided by analyses that have examined the independent predictive
power of intention and past behavior. Although Ajzen (2002) neglected to
mention these findings, they reveal the expected pattern—past behavior
tends to emerge as the primary predictor when habits have developed,
whereas intention is the primary predictor when habits are unlikely to have
developed (Ferguson & Bibby, 2002; Ouellette & Wood, 1998; Verplanken
et al., 1998). Thus, models that examined the unique impact of past
behavior and intention provided evidence of the relatively automatic pro-
cesses guiding habits.
2
Several additional constructs can be distinguished from habit. Scripts
are cognitive structures representing people’s understanding of stereotyped
sequences of action in well-known situations (Schank & Abelson, 1977).
As Abelson (1981) noted, “the difference between a script and a habit is
that a script is a knowledge structure, not just a response program” (p. 722).
In addition, Langer (1989a, 1989b) cautioned against equating the con-
struct of mindlessness with habits. Although both involve relatively effort-
less, invariant behavior, habits are more closely linked to behavioral
response. In contrast, mindlessness reflects a general mental state of the
organism as a whole (Langer, 1989b).
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WOOD, QUINN, AND KASHY
These range from a Walter Mitty-ish detachment from daily ac-
tivities (Thurber, 1942) to the ideal in some Eastern religions of
thoughtful awareness of all behaviors. Some psychological analy-
ses also imply a close correspondence between thought and action.
For example, James’s (1890) analysis of ideomotor action sug-
gested that cognitive representations of action function as tem-
plates for subsequent overt behavior. In this approach, an action is
generated from thought of the act (see also Bargh & Ferguson,
2000). Similarly, Vallacher and Wegner’s (1987, 1989) action
identification theory links actions to people’s understanding of
what they are doing. Although this perspective is primarily con-
cerned with the conditions under which people understand their
actions in terms of mechanistic performance details versus broader
goals and identities, a central assumption is that “well-learned,
automated acts are performed with a representation of the act in
mind, just as difficult unfamiliar acts are” (Vallacher & Wegner,
1987, p. 9). In addition, the idea that intentional behavior emerges
from thought informs Wegner’s (2002) notion of the empirical
will, in which intentional behavior is demonstrated from causal
relations between people’s conscious psychological states and
their subsequent actions.
Other perspectives allow for greater variability in the extent to
which thought corresponds to action. As Heckhausen and Beck-
mann (1990) noted, the relation between attention and ongoing
activity is likely to vary with the mode being used to guide
behavior. With novel activities or activities in unfamiliar contexts,
the uncertainties associated with performance require that people
continuously attend to and evaluate new information as it is
presented in order to respond appropriately. In contrast, habitual
action does not require continuous attention to behavior or the
circumstances in which it occurs. For frequently performed behav-
iors, specific intentions become implicit as individual behaviors
come to be incorporated into sequences of multiple actions and as
intentions come to be specified at high levels of abstraction (Ouel-
lette & Wood, 1998; Wegner & Bargh, 1998). People are then
freed-up to orient their thoughts toward unrelated concerns. In-
triguing neurological evidence that habits are stored as larger
action sequences rather than discrete acts was provided by Jog,
Kubota, Connolly, Hillegaart, and Graybiel’s (1999) study of the
sensorimotor striatum of rats during learning of a maze. Because
neuronal responses after successful acquisition emphasized the
beginning and the end of the learned procedure, these authors
concluded that an action template was developed for the behav-
ioral unit as a whole (i.e., the full maze), and this was triggered by
specific contexts at the start and the end of the maze.
Variability in the extent to which thought corresponds to action
is also suggested by Bargh’s (1990) auto-motive model of how
goal structures guide behavior. In this view, purposive actions can
be elicited by directly thinking about relevant goals or, when
actions are well-practiced and automatic, by the environment trig-
gering the relevant goal structures and these structures guiding
behavior without conscious awareness. Some support for this
model was provided by the finding that goals experimentally
primed outside of awareness affected behavior independently of
reported intentions (Bargh, Gollwitzer, Lee-Chai, Barndollar, &
Tro¨tschel, 2001, Study 2). These findings contrast with Aarts and
Dijksterhuis’s (2000) demonstration that intentions for habitual
behavior are highly accessible to consciousness. Specifically, peo-
ple who had performed an act relatively frequently in the past, and
thus had established habits, were found to have short response
latencies to rate behavioral intentions (e.g., to ride a bike) when
relevant goals were primed (e.g., go to the store). However, be-
cause Aarts and Dijksterhuis did not measure behavior, it is un-
clear whether their findings are relevant to the cognitive mecha-
nisms that guide action. In sum, past research has demonstrated
that primed goals can affect behavior independently of conscious
intentions, but it remains to be demonstrated that habitual behavior
can be generated without conscious awareness of intentions to
initiate and/or perform the act.
In the present research, we anticipate that people engaged in
habitual actions do not consciously access habit intentions, either
because they do not need to do so in order to repeat well-learned
intentional responses or because the behavior was not intended to
begin with and perhaps became well-learned as a byproduct of
some other action sequence (see, e.g., Lippa & Goldstone’s, 2001,
demonstration of the acquisition of unintended responses through
association). Instead, we anticipate that people often think about
things other than their behavior during habit performance. How-
ever, we also recognize that situational factors can sometimes
focus people on their habitual behavior. For example, people may
self-consciously think about what they are doing when others are
present and they are concerned about these others’ evaluations.
Thus, our estimate of behavior-relevant thought may be affected
by factors in addition to the habitual or nonhabitual mode of
guiding behavior.
The naturally-occurring correspondence between people’s
thought and behavior also may vary across behavioral domain. For
our college student participants, a number of everyday behaviors
inherently require deliberation to generate an appropriate response,
and these include studying, talking with others, taking notes during
lectures, and reading. Some thought about these complex behav-
iors is necessary because each enactment contains considerable
novel information. To achieve behavior-relevant goals, people
must constantly tailor their behavior to events as they unfold (e.g.,
Did they understand the prior paragraph?; Is their interaction
partner responding as desired?). But even with these behaviors, we
anticipate that frequent performance of the act and stability of the
context will reduce the amount of thought necessary for perfor-
mance. This suggests that habits will be associated with less
behavior-relevant thought than nonhabits, regardless of whether
the behavior is one in which some deliberation is necessary for
effective performance.
Emotion and Habitual Behavior
The mode of behavior performance has implications for emo-
tional experience as well as for the contents of consciousness.
Although there is little research evidence on the relation between
mode of performance and emotion, several theoretical perspectives
provide a basis for anticipating that habitual behaviors are associ-
ated with less intense emotions than nonhabitual behaviors. Ac-
cording to one of Frijda’s (1988) laws of emotion, “continued
pleasures wear off; continued hardships lose their poignancy” (p.
353). From this perspective, people are likely to adapt psycholog-
ically and physiologically to the emotion-inducing aspects of re-
peated actions in a way that reduces emotional intensity. In addi-
tion, the anticipation of lesser intensity emotions associated with
habits can be derived from Mandler’s (1975) theory of mind and
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HABITS IN EVERYDAY LIFE
emotion. In this view, emotions arise when the interruption of
one’s plans and organized behavior sequences generates arousal
(i.e., of the autonomic nervous system) and initiates an interpre-
tation of the interruption that implicates particular emotions. Be-
cause infrequently performed behaviors and behaviors in unstable
contexts are plausibly more likely than habitual behaviors to
encounter difficulties and interference, nonhabitual behaviors are
more likely to be associated with emotions. Finally, from the
perspective of Carver and Scheier’s (1998, 1999) cybernetic model
of self-regulation, emotions emerge from discrepancies between
one’s behavior or related outcomes and one’s goals and self-
standards. Specifically, emotions emerge from changes in the rate
at which one’s behavior and outcomes are meeting or failing to
meet self-goals. To extend these ideas to include mode of perfor-
mance, it seems plausible that people will be more attentive to
discrepancies when deliberating about behavior than when acting
habitually. In sum, a variety of theoretical perspectives provide the
basis for anticipating that people are less likely to experience
intense emotions when engaged in habitual than nonhabitual
behavior.
One implication of the limited emotional responses associated with
habitual behavior is that when people do experience emotions during
habit performance, these emotions are likely to be linked to their
thoughts rather than to their behavior. Because habit performance
requires minimal explicit thought, people are able to entertain unre-
lated concerns, and the intruding thoughts may themselves be highly
emotionally charged. Thus, when performing behaviors habitually,
people are likely to report that their emotions are associated with what
they are thinking about—which will often be unrelated to their ac-
tions. This tendency for thoughts and not behaviors to elicit emotions
should be less evident for nonhabitual behaviors.
Habitual performance of behavior also has specific implications
for emotions associated with self-regulation and control. Given
that deliberation about even a single behavior can induce self-
control deficits (Baumeister et al., 1998, 2000; Muraven &
Baumeister, 2000), performance of nonhabits may be associated
with lowered feelings of control compared with habits. Specifi-
cally, the deliberation involved in the initiation and performance of
nonhabits may induce self-regulatory strains evident in partici-
pants’ feelings of stress, loss of control, and helplessness. Habit
performance does not require deliberation and thus is not likely to
elicit the same control deficits. In addition, given that burnout and
work stress have been linked to the pressure of jobs that require
simultaneous performance of multiple tasks (Leiter & Maslach,
2001; Nelson, Quick, & Simmons, 2001), feelings of stress might
emerge when participants perform multiple behaviors simulta-
neously, especially when these are nonhabitual and require con-
scious decision making.
The Present Research
The present investigation consists of two self-report diary
studies that yielded online appraisal of the thoughts and emo-
tions associated with performance of habitual and nonhabitual
behaviors. Participants in this research were signaled with a
watch alarm each hour to report on their behaviors, thoughts,
and emotions.
We anticipated that the habitual or nonhabitual mode of
behavior performance would be reflected in how closely peo-
ple’s thoughts corresponded to their actions. That is, less cor-
respondence should emerge when behaviors have been per-
formed frequently in the past in stable contexts, and thus habits
have developed, than when behaviors have been performed less
often or in unstable contexts, and thus habits have not devel-
oped. We also anticipated that participants would report lower
intensity emotions during performance of habits than nonhabits,
given that habituation occurs to repeated behaviors (Frijda,
1988), that habits are likely to be associated with few arousal-
inducing interruptions (Mandler, 1975), and that people may
not be aware of emotion-inducing discrepancies between habit-
ual behaviors and personal goals. Because habits require little
behavior-relevant thought, people are likely to report that any
emotions experienced during habit performance emerged from
their thoughts rather than from their behaviors. In addition, the
self-regulatory advantages of habits should be apparent in lower
levels of stress and burnout when performing behaviors habit-
ually rather than with awareness.
As a secondary focus of the research, we examined participants’
interpretations of habitual and nonhabitual behavior. On the one
hand, given the low intensity emotions and the minimal cognitive
monitoring associated with habit performance, these behaviors
may not strongly implicate the self and instead may be explained
in terms of external factors such as situational constraints. Alter-
natively, reasoning from Bargh et al.’s (2001) assumption that the
goals and associated behaviors that become automated through
frequent selection are likely to reflect individuals’ guiding values,
habitual behaviors may be especially self-defining and participants
may report that they reflect personal attributes, desires, and
preferences.
Also, given our correlational design, a number of alternate
interpretations exist for any findings. First, we considered the
possibility that the predicted relations might be obtained spu-
riously as a result of some third factor rather than the mode of
behavior performance. To address this issue, we selected sev-
eral behaviors that, according to participants’ diary responses,
were sometimes performed habitually and sometimes nonhab-
itually. We then tested our hypotheses within these behavioral
domains, and in this way were able to hold constant many of the
extraneous factors that might vary with domain. A second
concern is whether the diary method can illuminate the causal
ordering between thought, emotion, and behavior. We discuss
the benefits and limitations of the diary method in the General
Discussion.
The two studies in this article overlap considerably in their
designs and results, and for this reason we present them jointly. In
the first study, participants reported on a single behavior at each
hourly diary assessment. The second study included more partic-
ipants, a longer recording period, and allowed reports of multiple
simultaneous thoughts and behaviors. Because people are likely to
be most aware of actions that require attention and control, reports
limited to a single behavior at each assessment might underesti-
mate the incidence of habits.
Method
Participants
Study 1. A total of 70 undergraduate students (35 women
and 35 men) at Texas A&M University participated in partial fulfill-
1284
WOOD, QUINN, AND KASHY
ment of a requirement in their introductory psychology course. The data
from an additional 19 participants were excluded from the analyses
either because they failed to complete the form correctly or they
completed it retrospectively.
Study 2. A total of 209 undergraduate students (131 women and 78
men) at Texas A&M University participated in partial fulfillment of a
requirement in their introductory psychology course. The data from an
additional 16 participants were excluded from the analyses because they
either failed to complete the form correctly or they completed it
retrospectively.
Procedure
The studies were conducted in three phases: an introductory session, a
recording period of 1 day (Study 1) or 2 days (Study 2), and a follow-up
session in which participants provided additional information about the
behaviors they listed in the diary.
Phase 1: Introductory session. In groups of approximately 40, partic-
ipants attended an introductory meeting for a study investigating the
behaviors people perform in their daily lives. Participants were told that
they would be tracking their behaviors, thoughts, and emotions. Observa-
tions were to be recorded once per hour while participants were awake.
Participants received a wristwatch programmed to chime on the hour to cue
them to complete the diary.
Participants received copies of the diary forms and examples of correct
entries. To ensure accuracy in completing the diary, participants were
instructed to make their hourly entries while the events occurred. To
encourage completion of the diary, participants rated their implementation
intentions on a questionnaire, identified the best day(s) to complete the
diary, and described how they would remember to complete the form each
hour (Gollwitzer, 1999). In addition, participants signed a “contract” indi-
cating their commitment to complete the data collection. Participants then
scheduled a follow-up session and were excused.
3
Phase 2: Diary records. Participants carried the diary forms with
them, recording their behaviors, thoughts, and emotions once per hour (see
diary measures below).
Phase 3: Follow-up session. Participants attended a follow-up session
in small groups to provide additional information about their diary entries
(see follow-up questionnaire below). Upon completion, participants re-
ceived their experimental credit and indicated whether they had reported
the events listed in the diary as they occurred or retrospectively. Partici-
pants were then debriefed and excused.
Measures
Diary behavior reports. Participants recorded the single behavior
(Study 1) or all of the behaviors (Study 2) in which they were engaged at
the moment of the watch chime. For each behavior, participants rated: (a)
the frequency with which they had performed the behavior in the past
month, with response options 1 (monthly or less often),2(at least once a
week),or3(just about every day); (b) the extent to which they performed
the behavior in the same physical location each time, from 1 (rarely)to3
(usually); and (c) the involvement of other people in the behavior (others
involved vs. others not involved). In Study 2, participants also rated: (a) the
amount of attention normally required for successful performance, from 1
(almost no attention)to4(constant attention); (b) the degree of difficulty
of the behavior, from 1 (very easy) to5(very difficult); and (c) the
importance of the behavior for achieving personal goals, from 1 (unimpor-
tant)to5(highly important).
In the analyses, habits were defined as behaviors participants reported
performing “just about every day” and “usually in the same location.”
Nonhabits were behaviors performed less often (i.e., once a week or
monthly) and in less stable contexts (i.e., rarely or sometimes in the same
location).
Diary thoughts. Participants reported their thoughts by answering the
open-ended question, “What were you thinking about during this activity?”
Space was provided for participants to write a short description of a single
thought (Study 1) or multiple thoughts (Study 2).
Correspondence between diary thoughts and behaviors. Two indepen-
dent raters coded the diaries for whether participants were thinking
about the behavior in which they were engaged at each recording
period. Thoughts were classified as corresponding with behavior
when they involved the specific actions being performed (e.g., when
eating, “about how good the bread was”) or implicated abstract goals
and outcomes that related in some way to the actions being performed
(e.g., “how I need to start eating more healthy so I can get back in the
shape I was during summer”). Thus, we judged that participants’
thoughts corresponded to their behavior when the thoughts reflected
either specific, relatively low-level instrumental intentions or more
abstract, higher level intentions. This was done to capture the more
abstract thoughts reflecting high levels of intention and goal specifica-
tion that may direct habitual performance (Heckhausen & Beckmann,
1990; Vallacher & Wegner, 1987). Thoughts and behaviors were clas-
sified as not corresponding when they were clearly about unrelated
issues (e.g., thinking about an upcoming math test while driving home).
Because participants in Study 2 were allowed to report multiple simul-
taneous behaviors and multiple concurrent thoughts, correspondence
was coded at the level of the individual behavior, rather than at the level
of the hourly entry. Raters agreed on 84% of behaviors in Study 1 and
89% of behaviors in Study 2. Disagreements were resolved through
discussion.
Behavior complexity. Two independent raters categorized all behav-
iors as (a) complex, meaning those that required responses tailored to
new information as it emerged during performance; or (b) less complex,
meaning those that could be performed effectively with minimal mod-
ification to new information. The most common examples of complex
or difficult activities appearing in participants’ diaries included behav-
iors related to academic achievement (e.g., studying, listening to lec-
tures), extended interactions with other people (i.e., conversations, as
opposed to brief, routinized greetings), creative endeavors (e.g., com-
posing a letter or essay), and challenging games or competitions (e.g.,
sports, cards). Examples of less complex behaviors included driving,
cooking, and paying bills. Raters agreed on 88% of judgments in
Study 1 and 93% in Study 2. Disagreements were resolved through
discussion.
Diary emotion measures. Participants rated whether their emotions
varied from the day’s baseline level of emotion on a 5-point scale ranging
from much more negative to much more positive, with the midpoint
representing no change. Analyses on this raw valence scale yielded no
effects and we do not discuss them further. To form a 3-point scale
reflecting changes in emotional intensity regardless of valence, responses
were treated as deviations from the scale midpoint. The resulting change in
emotional intensity scale ranged from 1 (no change from baseline)to3
(much more positive/negative than baseline).
3
Participants also completed several individual difference measures
during the introductory session. These included the Need for Cognition
Scale (Cacioppo, Petty, & Kao, 1984), the Need to Evaluate Scale (Jarvis
& Petty, 1996), the Affect Intensity Measure (Larsen, Diener, & Emmons,
1986), the Personal Need for Structure (Neuberg & Newsom, 1993), and
additionally in Study 2, the Emotional Intensity Scale (Bachorowski &
Braaten, 1994) and Goldberg’s (1992) 100 Adjective Markers for the
Big 5. In Study 1, individual’s tendency to perform habits, reflected in the
proportion of habitual behaviors they reported, was marginally related to
their scores on need for cognition, r(62) ⫽⫺0.24, p ⫽ .06, and need to
evaluate, r(61) ⫽⫺0.22, p ⫽ .08. However, no relations between habitual
behavior and personality emerged in Study 2.
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HABITS IN EVERYDAY LIFE
Participants reported on the source of their emotions by checking the
appropriate box to indicate whether the cause was their thoughts, their
actions, or both. In Study 2, participants also reported on specific emotions
associated with self-regulatory challenges. They rated on 5-point scales,
ranging from 1 (very little or not at all)to5(extremely), the extent to which
they felt stressed, fatigued, overwhelmed, tired, burned out, helpless, out of
control, weak, and bored.
Follow-up questionnaire to assess participants’ implicit theories. In
both studies, participants indicated with a response of yes or no whether
they considered each behavior to be a habit. They also indicated whether
the behavior reflects the kind of person they are (in Study 1 they answered
with yes or no and in Study 2 they answered on a 5-point response scale
ranging from not at all to a lot).
In Study 1, participants indicated whether performing each behavior
caused them to experience feelings of pride or shame (with a response of
yes or no). We further explored self-related emotions in Study 2 by
instructing participants to rate their feelings about performing each behav-
ior using a 5-point scale, from 1 (very bad about myself)to5(very good
about myself).
In Study 2, participants also rated a number of possible causes for
their behavior. On 5-point scales ranging from 1 (not at all)to5(a lot),
they indicated the extent to which the behavior was performed because
of (a) dispositional factors (“because of something about you,” i.e.,
because “you like to do it”), (b) another person, (c) temporal factors
(“because it was the right time to do it”), and (d) situational factors
(“because of the situation you were in”). Also on the 5-point scale,
participants indicated how much they had thought about the behavior
before performing it.
Results and Discussion
As can be seen in Tables 1 and 2, between a third and a half of
all behaviors listed were classified as habits, given that they were
performed just about every day and usually in the same location.
This estimate was greater in Study 2 (43%) than in Study 1 (35%),
2
(1, N ⫽ 6,830) ⫽ 14.34, p ⬍ .01, consistent with our expectation
that the procedure of listing multiple behaviors in Study 2 would
encourage participants to include more activities performed habit-
ually. To illustrate the kinds of activities participants listed, we
classified diary entries in Study 2 into broad behavioral domains.
As shown in Table 3, the most commonly reported domains in
students’ lives included studying and other school-related behav-
iors, activities related to entertainment and news- and information-
gathering, social interaction, and eating and drinking. Of the do-
mains we identified, attending to hygiene and appearance and
sleeping and waking activities were most likely to be classified as
habitual in the sense that they were performed frequently in stable
contexts.
Correspondence Between Thoughts and Behaviors
Examples of thoughts that did or did not correspond to actions
for some common behaviors listed in participants’ diaries are
presented in Table 4. For the analyses, the diary design yielded a
hierarchically nested data structure with participants’ hourly re-
ports nested within individual participants. Because the hourly
reports from an individual were not independent, we treated par-
ticipants as the units of analysis. To do this, data were aggregated
across each participant’s hourly reports, and analyses were con-
ducted on participant-level data. Thus, to evaluate the relationship
between mode of behavior performance (habitual vs. nonhabitual)
and correspondence between thoughts and behaviors (correspond
vs. do not correspond), the hourly reports for each participant were
aggregated by tallying the frequency of habitual behaviors for
which thought corresponded with the behavior and the frequency
of habitual behaviors for which thought and behavior did not
correspond. To generate participant-level percentage estimates,
these frequencies were divided by the total number of habitual
behaviors reported by the participant. The same procedure was
followed for nonhabitual behaviors. The four percentages yielded
by each participant (correspondence/habit, noncorrespondence/
habit, correspondence/nonhabit, noncorrespondence/nonhabit)
were analyzed in a Mode of Performance (habit vs. nonhabit) ⫻
Thought/Behavior Correspondence (thoughts did vs. did not cor-
Table 1
Means and Standard Deviations of Variables Assessed in Study 1
Variable MSD
Number of hourly diary entries per participant 9.58 3.12
On the basis of the experimenter’s rating, the proportion of behaviors classified as:
habitual (performed almost daily, usually in same location) .35 .19
corresponding with thoughts .61 .19
On the basis of participants’ ratings of each behavior, the proportion of behaviors in which:
other people were involved .49 .18
any emotions were caused by actions .43 .24
any emotions were caused by thoughts .35 .21
pride was experienced .20 .22
shame was experienced .03 .06
Participants’ ratings of:
frequency of past performance 2.23 0.36
stability of context 2.55 0.34
intensity of emotions 1.86 0.42
Note. Proportions were computed for each participant and the mean value that is reported in the table was
calculated across participants in the sample. Ratings of frequency of past performance and stability of context
were obtained on scales ranging from 1 to 3, with higher numbers indicating greater frequency or stability.
Emotional intensity ratings are reported on a scale ranging from 1 (no change in emotion from baseline for the
day)to3(much more negative or positive than baseline).
1286
WOOD, QUINN, AND KASHY
respond to behavior) repeated measures analysis of variance
(ANOVA) design.
4
Study 1. The analyses yielded a significant interaction between
mode of performance and correspondence, F(1, 63) ⫽ 48.63, MSE ⫽
.12, p ⬍ .001. Analyses of simple main effects indicated that, for
behaviors classified as habits, thoughts were more likely not to cor-
respond with behaviors (M ⫽ 60%) than to correspond (M ⫽ 40%),
t(62) ⫽⫺2.27, p ⬍ .05, whereas for nonhabitual behaviors, thoughts
were more likely to correspond with behaviors (M ⫽ 70%) than not
to correspond (M ⫽ 29%), t(62) ⫽ 7.40, p ⬍ .001.
Study 2. The analyses yielded a significant main effect for
correspondence, F(1, 208) ⫽ 16.73, MSE ⫽ .09, p ⬍ .001 (Ms ⫽
56% and 44% for correspondence and noncorrespondence, respec-
tively), and a significant interaction between mode of performance
and correspondence, F(1, 208) ⫽ 90.99, MSE ⫽ .06, p ⬍ .001.
Consistent with Study 1, simple effects comparisons revealed that
during performance of habits, participants’ thoughts were more
likely not to correspond with their behavior (51%) than to corre-
spond (44%), t(207) ⫽⫺2.52, p ⫽ .01, whereas for nonhabits,
thoughts and behaviors were more likely to correspond (60%) than
not to correspond (36%), t(207) ⫽ 9.99, p ⬍ .001. It should be
noted that these percentages sum to less than 100% because we
were unable to code some of the listed behaviors and thoughts.
The thought–behavior correspondence findings across both
studies are consistent with our prediction that performance of
habitual behaviors allowed people to focus their attention away
from their current behavior. The lower levels of behavior-relevant
thought associated with habits than nonhabits is an indicator of
greater automaticity in the guidance of habitual acts. The finding
that participants thought about habits about 40% of the time is
consistent with the idea that this mode of behavior regulation is
best characterized by minimal or sporadic cognitive monitoring
and not by the complete absence of thought (see Pashler, 1994).
Additional evidence of the extent to which behavior was guided by
explicit thought emerged in Study 2 from participants’ ratings of the
attention and thought required to perform each behavior and the
difficulty of performance. Because these ratings represent continuous
dependent measures, they were analyzed with a multilevel regression
approach (Kenny, Kashy, & Bolger, 1998). In essence, a regression
equation was estimated for each participant to represent the relation-
ship between a predictor (e.g., mode of performance) and an outcome
measure (e.g., rated attention to behavior). In the analyses, the inter-
4
Because participants listed more nonhabitual than habitual behaviors,
we calculated the percentage correspondence for habits and nonhabits
separately. Thus, our results reflect the percentage of each type of behavior
that corresponds to thought, not the percentage of the total number of
behaviors listed.
Table 2
Means and Standard Deviations of Variables Assessed in Study 2
Variable MSD
Number of hourly diary entries per participant 20.74 5.47
Number of behaviors total reported per participant 30.12 10.84
Number of behaviors per hourly entry 1.46 0.37
Number of thoughts per hourly entry 1.08 0.23
On the basis of the experimenter’s rating, the proportion of behaviors classified as:
corresponding with thoughts .53 .16
habitual (performed almost daily, usually in same location) .43 .19
On the basis of participants’ ratings of each behavior, the proportion of behaviors in which:
other people were involved .44 .16
any emotions were caused by actions .56 .21
any emotions were caused by thoughts .33 .19
Participants’ ratings of:
frequency of past behavior performance 2.49 0.22
stability of context 2.57 0.23
attention required 2.27 0.38
behavior difficulty 1.94 0.38
importance of behavior for personal goals 2.47 0.63
intensity of emotions 1.82 0.31
loss of control 1.56 0.52
fatigue/lack of interest 2.03 0.63
amount of thought required before performance 2.19 0.56
attribution of behavior to internal causes 2.61 0.60
attribution of behavior to another person 2.08 0.65
attribution of behavior to the situation 3.17 0.87
attribution of behavior to time 3.25 0.77
Note. Proportions were computed for each participant and the mean value that is reported in the table was
calculated across participants in the sample. Ratings of frequency of past performance and stability of context
were obtained on scales ranging from 1 to 3, with higher numbers indicating greater frequency or stability.
Ratings of attention, difficulty, importance, and prior thought were obtained on scales ranging from 1 to 5, with
higher numbers reflecting greater amounts of the attribute. Emotional intensity ratings are reported on a scale
ranging from 1 (no change in emotion from baseline for the day)to3(much more negative or positive than
baseline). Loss of control and lack of interest are reported on scales ranging from 1 to 5, with higher numbers
indicating greater experience of each emotion. Attribution ratings were obtained on scales ranging from 1 to 5,
with higher numbers reflecting stronger attributions.
1287
HABITS IN EVERYDAY LIFE
cepts and slopes from these equations were then aggregated to yield
a mean intercept and slope across participants.
5
The multilevel analyses conducted with mode of performance
(habit vs. nonhabit) as a dichotomous predictor at the behavior
level revealed that participants rated habits as less demanding of
attention than nonhabits, unstandardized regression coefficient,
B ⫽⫺0.44, SE ⫽ .04, t(193) ⫽⫺11.85, p ⬍ .001. Participants
also reported that they thought less about their actions before
performing habits in comparison with nonhabits, B ⫽⫺0.61, SE ⫽
.04, t(199) ⫽⫺14.00, p ⬍ .001. In addition, habits were rated as
being less difficult to perform than were nonhabits, B ⫽⫺0.47, SE
⫽ .04, t(188) ⫽⫺11.40, p ⬍ .001. These findings are consistent
with the thought–behavior correspondence results in indicating the
greater conscious processing required to guide nonhabit than habit
performance. With frequent performance of behaviors in stable
contexts, aspects of the cognitive processes controlling perfor-
mance appear to become automatic and relatively easy to execute.
6
Controlling for Plausible Alternate Explanations
Given the correlational design of this research, additional analyses
were necessary to verify that the obtained relations do not reflect some
artifactual difference between the behaviors classified as habits and
those classified as nonhabits (e.g., habitual behaviors being inherently
easier to perform). To address this concern, we examined whether
participants’ thoughts corresponded with behavior more for nonhabits
than for habits when analyses were conducted within behavioral
domain (see similar analysis in Ouellette & Wood, 1998).
5
It should be noted that in Study 2, the full three-level hierarchical
design consisted of individual behaviors that were nested within hourly
diary entries and entries that were nested within persons. However, we
treated the data as having two levels because of the relatively low number
of observations at the behavior level, with a mean of only 1.46 (SD ⫽ 0.37)
behaviors reported within each hourly diary entry. Thus, in most cases, the
behavior level did not differ from the hour level.
6
To provide some evidence of the validity of our measure of thought–
behavior correspondence, we examined whether it increased with factors that
should focus participants’ attention on what they are doing, especially whether
other people were present and involved in the behavior. In the presence of
others, people may have a self-conscious concern with appearing appropriately
and with generating responses to ongoing interaction (Baumeister, 1984;
Carver & Scheier, 1978). Indeed, the pattern of means indicated that the
presence of others increased the correspondence between thought and action,
although the effect emerged as significant only in Study 2. That is, in Study 2,
analysis on thought–behavior correspondence yielded a two-way interaction
between performance mode and correspondence, F(1, 208) ⫽ 90.42, MSE ⫽
.02, p ⬍ .001, which revealed the expected pattern of greater behavior-relevant
thought when others were present.
Table 3
Behaviors Frequently Listed in Participants’ Diaries: Study 2
Type of behavior Examples of common entries
%ofall
diary
entries
% entries
categorized
as habits
School work Attending classes, studying, reading, doing homework and
assignments, taking notes, going to the library
32 32
Entertainment, news,
& information
Watching TV, listening to music, using the Internet, reading
the newspaper, going to movies/entertainment, playing
games
14 54
Social interaction Talking to friends, family, and others; face-to-face, on the
phone, via computer; reading/writing e-mail
10 47
Eating, drinking Eating, drinking (except alcohol), cooking or preparing food 7 46
Hygiene &
appearance
Showering, washing hands, brushing teeth, dressing, putting
on make-up
488
Transportation &
travel
Driving, riding a bike, taking a bus, walking to or from
some location
458
Going to
sleep/waking
Waking up, lying in bed, getting ready for bed, taking a nap 3 81
Exercise Lifting weights, running, swimming, playing sports 1 44
Working Part-time or full-time jobs 1 55
Cleaning Doing laundry, washing dishes, cleaning dorm/apartment 1 21
Relaxing Resting, relaxing, sitting on the couch 1 48
Other Various behaviors appearing infrequently in participants’
diaries
21 32
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WOOD, QUINN, AND KASHY
We selected two behaviors from participants’ diaries in Study 2:
watching TV and driving. These behaviors were approximately
equally often classified as habits (52%) and as nonhabits (48%)
and were noted with sufficient frequency in the diaries to allow
analyses to be performed solely on these domains (i.e., TV watch-
ing: n ⫽ 614; driving: n ⫽ 83). Because the variables were
categorical and independent at the level of individual participants,
we conducted Mode of Performance (habit vs. nonhabit) ⫻ Cor-
respondence (thoughts do vs. do not correspond with behavior)
repeated measures ANOVAs on the aggregated percentage esti-
mates for each individual (percentage of habits/correspond, habits/
not correspond, nonhabits/correspond, nonhabits/not correspond).
A marginal main effect for correspondence, F(1, 79) ⫽ 3.27,
MSE ⫽ .27, p ⬍ .10 (Ms ⫽ 41% and 59% for correspond and do
not correspond, respectively) was qualified by the predicted inter-
action between performance mode and correspondence, F(1,
79) ⫽ 18.33, MSE ⫽ .26, p ⬍ .001. Simple effects tests showed
that participants’ thoughts were more likely not to correspond
(64%) than to correspond to their behaviors (28%) when they
performed the behaviors habitually, t(127) ⫽⫺5.85, p ⬍ .001.
When performing behavior in a nonhabitual manner, however,
only a slight trend emerged for thoughts to correspond (49%)
rather than not correspond (46%, t ⬍ 1). These findings indicate
that the overall relation between mode of performance and the
extent to which participants think about the activity when perform-
ing it is not due to some confounding feature of the behaviors we
classified as habitual or nonhabitual.
Additional evidence that our results do not reflect some artifact
of the behaviors classified as habits and nonhabits comes from the
ratings of attention, thought, and performance difficulty for the
domains of watching TV and driving. Because these continuous
ratings were performed for each behavior, we conducted multilevel
regression analyses on behavior ratings within participants, with
mode of behavior performance as the predictor. Consistent with
the findings from the full sample of behaviors, participants re-
ported less attention and less thought when they were watching TV
and driving in a habitual manner than in a nonhabitual one, B ⫽
⫺0.18, SE ⫽ .07, t(102) ⫽⫺2.50, p ⬍ .05; B ⫽⫺0.32, SE ⫽ .08,
t(124) ⫽⫺4.09, p ⬍ .001, for attention and thought, respectively.
Analyses on rated performance difficulty, although yielding a
trend in the same direction, did not reach significance (t ⬍ 1.5).
These findings provide additional reassurance that the minimal
cognitive monitoring associated with frequently performed behav-
iors in stable contexts reflects mode of performance and not other
factors.
Habits and Emotion
Study 1. Two strategies were used to evaluate whether habits
were associated with lesser intensity emotional experiences. The
first approach evaluated this relation at the level of participants’
mean ratings. We computed correlations between the aggregated
percentage of each participant’s behaviors that were habitual and
each participant’s overall mean ratings of emotional intensity. As
Table 4
Examples of Corresponding and Noncorresponding Thoughts and Behaviors
Behavior Example of corresponding thought Example of noncorresponding thought
Attending classes “I was getting the answer to my test
back and was worried.”
“I’m really tired.”
Studying “Thinking about passing my test.”“That I would really like to go home.”
Watching television “I’m watching game shows so I am
thinking about the answers.”
“I’m hungry.”
Eating “This dinner stinks.”“Thinking about my test again.”
Talking on the
telephone
“I made an A on my test, and I was
telling him how relieved I was. I
was happy just to talk to him.”
“About sleeping.”
Working on the
computer
“Who e-mailed me?”“Excited about my friend coming to
visit.”
Reading “I was concentrating on the events
in the book.”
“Getting ready to exercise.”
Listening to music “ . . . 80s music was so great.”“I was thinking I had to find a parking
place and I would be late for class.”
Driving “Not running over pedestrians that
keep walking in front of me.”
“I was thinking about all I had to
accomplish today.”
Exercising “That I’m dead tired, and I’ve only
got a few more exercises left to
do.”
“Where I’d like to go for spring break.”
Cooking “I am so hungry. This smells good.”“‘Friends’ comes on in 30 minutes.”
1289
HABITS IN EVERYDAY LIFE
we anticipated, the greater the percentage of participants’ behav-
iors that were habitual, the less likely they were to report increases
in the intensity of their emotions, r(64) ⫽⫺.25, p ⬍ .05. The
second approach evaluated the relationship between mode of per-
formance and ratings of emotional intensity at the level of the diary
entry by calculating correlations across entries for each participant.
The mean correlation aggregated across participants yielded no
effect (t ⬍ 1), which is perhaps understandable given that partic-
ipants provided on average less than 10 diary entries.
Study 2. Because each hourly diary entry included one or more
behaviors but only one rating of the intensity of change in partic-
ipants’ emotional state, we could not identify which behavior was
associated with the rated emotion. Thus, analyses of the habit-
emotion relationship were conducted at the level of the diary entry
rather than at the level of the individual behavior. For these
purposes, each diary entry was classified as involving the perfor-
mance of (a) a single habitual behavior (26% of behavior entries),
(b) a single nonhabitual behavior (37% of entries), (c) multiple
behaviors, all of which were habits (10% of entries), (d) multiple
behaviors, all of which were nonhabits (15% of entries), (e) only
one nonhabit and one or more habits (11% of entries), and (f) two
or more nonhabits and one or more habits (2% of entries).
We examined whether nonhabits were associated with more
intense changes in emotion than habits by computing a multilevel
regression model. A dichotomous predictor was formed to repre-
sent diary entries in which participants reported only habits versus
diary entries with at least one nonhabit (i.e., from the entry
classifications given in the prior paragraph, this represents a com-
parison between a ⫹ c vs. b ⫹ d ⫹ e ⫹ f). Consistent with
expectations, less intense emotion change emerged in the habit-
only entries than in entries that included at least one nonhabit, B ⫽
⫺0.09, SE ⫽ .02, t(206) ⫽⫺3.65, p ⬍ .001. The intercept of this
regression model revealed that the mean emotional intensity while
performing at least one nonhabitual behavior was 1.85 on the
3-point intensity scale ranging from no change to much more
positive/negative than baseline feelings. The regression coefficient
indicates that entries composed only of habits were rated .09 scale
points lower in intensity than entries that included at least one
nonhabit.
We conducted additional multilevel analyses to address a num-
ber of more specific questions concerning the relation between
emotion change and performance of habitual and nonhabitual
behaviors. First, the intensity of emotional change was unrelated to
a continuous numerical representation of the total number of
behaviors being performed simultaneously (t ⬍ 1).
7
Thus, our
findings concerning mode of behavior performance and emotional
intensity do not depend on the number of behaviors participants
reported performing at a diary entry.
In addition, the performance of multiple nonhabits simulta-
neously did not yield a greater increase in emotional intensity than
performance of a single nonhabit. Specifically, emotional intensity
did not increase with the performance of multiple rather than
single nonhabits (t ⬍ 1), or with the performance of combinations
of habits and nonhabits that involved only one nonhabit rather than
multiple nonhabits (t ⬍ 1). Thus, even though performing a single
nonhabitual behavior was associated with changes in emotional
intensity, emotions did not shift further with the performance of
additional nonhabits.
As anticipated, analyses revealed that performance of habits
simultaneously with other behaviors was not associated with in-
creased emotional intensity. That is, emotional intensity did not
increase with the performance of multiple rather than single habits
(t ⬍ 1), or with the performance of combinations of habits and
nonhabits rather than only nonhabits (t ⬍ 1.5).
In summary, behaviors performed habitually were associated
with lesser change in emotional intensity than behaviors performed
nonhabitually. This effect might arise from habituation processes,
given that emotional intensity generally decreases with increasing
exposure to a stimulus (Frijda, 1988). It also might emerge because
performance of habits is associated with few arousal-inducing
interruptions that form the basis for emotional experiences
(Mandler, 1975). And this effect also is consistent with our rea-
soning from Carver and Scheier’s (1999) theory of self-control, in
that people may not be aware of emotion-inducing discrepancies
between behaviors and personal goals when they perform behav-
iors habitually.
Source of Emotion
Study 1. To evaluate whether the source of participants’ emo-
tions varied with the mode of behavior performance, we aggre-
gated these dichotomous variables to yield percentage data that
were independent at the level of the individual, following the
strategy used to analyze thought–behavior correspondence. A
Source of Emotions (actions vs. thoughts) ⫻ Mode of Performance
(habit vs. nonhabit) repeated measures ANOVA revealed a mar-
ginally significant interaction, F(1, 57) ⫽ 3.62, MSE ⫽ .16, p ⫽
.06. Simple main effect analyses revealed that when performing
nonhabits, participants were more likely to identify the source of
their emotions to be their actions (43%) rather than their thoughts
(30%), t(56) ⫽ 2.16, p ⬍ .05. However, when performing habits,
this pattern did not emerge, and instead participants showed a
nonsignificant tendency to identify the origin of their emotions as
their thoughts (43%) more than their actions (36%, t ⬍ 1). These
percentages do not sum to 100 because some participants indicated
that their emotions stemmed from both thoughts and behaviors.
Consistent with predictions, then, when compared with nonhabits,
participants’ emotions during habit performance were slightly
more likely to be associated with their thoughts than with the
behavior itself.
Study 2. We first conducted analyses on diary entries that
reported only single behaviors. A Mode of Performance (habit vs.
nonhabit) ⫻ Source of Emotions (actions vs. thoughts) repeated
measures ANOVA resulted in a significant main effect for source
of emotions, F(1, 174) ⫽ 63.09, MSE ⫽ .26, p ⬍ .001 (Ms ⫽ 66%
and 34% for actions and thoughts, respectively), and a significant
interaction, F(1, 174) ⫽ 11.42, MSE ⫽ .12, p ⬍ .001. Although,
in general, participants were more likely to attribute their emotions
to their actions, this tendency was more pronounced for nonhabits
(Ms ⫽ 65% and 29% for actions and thoughts, respectively),
t(202) ⫽ 9.50, p ⬍ .001, than for habits (Ms ⫽ 59% and 37% for
7
In the multilevel regression analysis, the number of behaviors per
-
formed was centered on the grand mean prior to inclusion in the model.
Because other predictors in these analyses were categorical, they were not
centered because the value zero is meaningful and corresponds to one of
the levels of the categorical predictor.
1290
WOOD, QUINN, AND KASHY
actions and thoughts, respectively), t(175) ⫽ 4.09, p ⬍ .001.
Comparable results emerged in analyses on diary entries composed
of multiple behaviors in which all of the behaviors listed were
either habits or nonhabits.
In summary, across both studies the most frequently mentioned
source of emotion was participants’ behavior. This pattern did not
hold as strongly for habits as nonhabits, and, as we had anticipated,
participants mentioned their thoughts as an important source of
emotional experience when engaged in habitual behavior. These
findings are consistent with our expectation that habitual behaviors
themselves do not generate strong emotional responses, and emo-
tions during habit performance are likely to emerge from the
thoughts that intrude during action, including reflections of past
and future experiences and the recognition of ongoing events
unrelated to behavior.
It is worth noting that the correlational nature of the present
design generates some ambiguity in how best to interpret these
findings. Although we prefer to conclude that the source of emo-
tion when performing habitual behaviors differs from that with
nonhabitual behaviors, it could also be that the ratings of source of
emotion reflect the different thought processes that guide habits
and nonhabits. For example, it could be that people identified as
the source of their emotion whatever they were attending to at that
moment—when performing habits, this happened to be their
thoughts, but when performing nonhabits, this happened to be their
behavior. Regardless of the most appropriate account, participants’
identification of somewhat different sources of emotion for habits
and nonhabits contributes to our thesis that the cognitive and
emotional processes that guide habit performance differ from those
that guide nonhabits.
Habits and Self-Regulatory Emotions: Study 2
A maximum likelihood factor analysis was performed on par-
ticipants’ hourly ratings of emotional experiences indicative of
self-regulatory challenges, and two factors emerged with eigen-
values greater than one. The first factor reflected stress, and the
items that loaded primarily on this factor were helpless (loading of
.86), out of control (.80), overwhelmed (.64), stressed (.53), and
weak (.49). The second factor concerned fatigue, and the items that
loaded primarily on this factor were fatigued (loading of .85), tired
(.81), burned out (.59), and bored (.33). Mean ratings were calcu-
lated across the items that loaded heavily on each factor.
To evaluate the relations between stress, fatigue, and change in
emotional intensity, we computed bivariate correlations within
participants and then aggregated these across participants to yield
mean correlations. As would be expected, feelings of stress and
fatigue were related to greater changes in emotional intensity,
r(199) ⫽ .26 and .21, ps ⬍ .001, for stress and fatigue, respec-
tively. No additional effects were obtained in any analysis on the
fatigue factor, and it will not be discussed further.
We conducted multilevel regression analyses to evaluate
whether the conscious direction of nonhabitual behavior was as-
sociated with greater feelings of stress. Because mode of perfor-
mance refers to the level of individual behavior listings and stress
was rated at the level of diary entries, the analyses were similar to
those outlined above for emotional intensity. Participant ratings of
stress were predicted from a dichotomous variable that compared
habit-only diary entries (M ⫽ 1.51 on the 5-point stress scale) with
entries that included at least one nonhabit (M ⫽ 1.59). Lesser
feelings of stress emerged with entries consisting only of habits,
B ⫽⫺0.08, SE ⫽ .02, t(198) ⫽⫺3.69, p ⬍ .001. These findings
suggest that the habitual mode of performance plays a role in the
self-regulation of behavior in that habits are associated with lesser
experience of helplessness and stress than nonhabits.
We then examined whether the reduced stress emerged with the
performance of a single nonhabitual behavior or whether this
feeling characterized multiple nonhabits. That is, these analyses
tested whether lowered stress emerged with the cognitive process-
ing requirements of consciously guiding a single behavior or
whether it emerged with the additional processing demands of
multitasking several consciously guided behaviors. A multilevel
regression model predicting stress from the raw number of non-
habitual behaviors performed concurrently revealed that, as antic-
ipated, the larger the number of nonhabitual behaviors, the more
participants felt stressed, B ⫽ 0.04, SE ⫽ .01, t(187) ⫽ 3.12, p ⬍
.01. However, examination of the mean ratings revealed that,
compared with performance of habits (M ⫽ 1.51), performance of
any nonhabitual behaviors reduced stress (Ms ⫽ 1.60 and 1.58 for
diary entries with one nonhabit and entries with two or more
nonhabits, respectively). The comparison between single and mul-
tiple nonhabits was not significant (t ⬍ 1). Thus, the decrement in
stress emerged with the cognitive processing required to engage in
a single nonhabitual behavior and did not reflect the deleterious
effects of multitasking nonhabits.
In summary, the analyses on self-regulatory emotions revealed
an important advantage to the lesser emotional intensity associated
with habits than nonhabits. That is, the habitual performance mode
was associated not only with lower intensity emotions overall, but
these effects were specifically reflected in lesser feelings of stress,
overload, and lack of control and did not extend to experiences of
fatigue and lack of interest. Furthermore, although we had antic-
ipated that multitasking of several nonhabits might pose a partic-
ular threat to self-regulation and thus be associated with the
greatest increase in stress, instead it appeared that the performance
of any single nonhabitual behavior increased stress.
Explanations for Behavior and Self-Related Emotions
Study 1. To evaluate the extent to which participants’ postdi-
ary questionnaire ratings of pride and shame varied with the mode
of performance, we aggregated data for these dichotomous vari-
ables to yield percentage estimates that were independent at the
level of the individual (see comparable analysis on thought–
behavior correspondence). A Pride (pride reported vs. not re-
ported) ⫻ Mode of Performance (habit vs. nonhabit) repeated
measures ANOVA yielded a significant main effect, F(1, 62) ⫽
133.98, MSE ⫽ .17, p ⬍ .001, reflecting the overall low frequency
of the experience of pride (20% of diary entries), and a significant
interaction, F(1, 62) ⫽ 5.87, MSE ⫽ .10, p ⬍ .05. Simple effects
tests revealed that pride was unlikely when performing habits
(Ms ⫽ 13% and 84% for pride and neutral feelings, respectively),
t(61) ⫽ 11.64, p ⬍ .001, but more likely with nonhabits (Ms ⫽
23% and 74% for pride and neutral feelings, respectively),
t(61) ⫽ 7.17, p ⬍ .001. Analyses on participants’ feelings of
shame revealed only a main effect indicating that participants
rarely experienced this emotion (3% of diary entries), F(1, 62) ⫽
2,351.88, MSE ⫽ .02, p ⬍ .001. The lesser pride participants
1291
HABITS IN EVERYDAY LIFE
reported concerning habitual than nonhabitual behaviors suggests
that habits are relatively unimportant aspects of participants’ ideal
self-concepts.
Study 2. The relationship between mode of behavior perfor-
mance and self-related emotions as reported in the postdiary ques-
tionnaire was evaluated with multilevel regression equations.
Mode of performance (habit vs. nonhabit) was represented as a
dichotomous predictor at the behavior level, and emotion ratings
were continuous variables obtained for each behavior reported.
Overall, habits were not judged to contribute positively to the
self-concept. Compared with nonhabits, performance of habits was
more likely to lead to negative self-evaluations, B ⫽⫺0.14, SE ⫽
.03, t(195) ⫽⫺4.31, p ⬍ .001. Habits also were less likely than
nonhabits to be considered important to attaining personal goals,
B ⫽⫺0.72, SE ⫽ .06, t(190) ⫽⫺12.86, p ⬍ .001, and were
judged less informative to others about the self, B ⫽⫺0.43, SE ⫽
.04, t(194) ⫽⫺9.94, p ⬍ .001. These findings echo the effects for
pride in Study 1 in suggesting that frequently performed acts in
stable contexts are not strong components of participants’ favor-
able self-views.
To evaluate the relation between mode of performance and
participants’ causal attributions for each behavior as reported in
the postdiary questionnaire, we conducted multilevel regression
equations with performance mode (habit vs. nonhabit) as a dichot-
omous predictor at the behavior level. Overall, participants ex-
pressed less certainty about the causal factors responsible for
habitual than nonhabitual behavior. That is, habits were judged
(marginally) less likely than nonhabits to be performed for internal
reasons, such as because the participant liked to do it, B ⫽⫺0.09,
SE ⫽ .05, t(191) ⫽⫺1.76, p ⬍ .10. Habits also were judged less
likely than nonhabits to be performed because of such external
reasons as the influence of other individuals, B ⫽⫺0.73, SE ⫽
.05, t(193) ⫽⫺14.61, p ⬍ .001, and the influence of situational
factors, B ⫽⫺0.42, SE ⫽ .05, t(194) ⫽⫺8.26, p ⬍ .001. The
relatively low attribution ratings for habitual behavior are consis-
tent with the notion that participants are not thinking about what
they are doing during habit performance and thus are unaware of
the factors responsible for their habits.
In summary, habitual behaviors proved to be, at best, unrelated
to participants’ self-concepts and, at worst, associated with nega-
tive aspects of the self. Participants’ relatively unfavorable slant on
habitual behaviors emerged in the low levels of pride they ex-
pressed concerning such acts, the association between such behav-
iors and negative self-evaluations, and the relative unimportance of
these behaviors for attaining personal goals. One interpretation of
participants’ negative spin on habitual behavior is that it reflects
the dissociation between the implicit intentions that guide habits
and participants’ explicit intentions and goals. Indeed, habits were
judged to be relatively uninformative about the self and were given
uniformly low attribution ratings, suggesting that participants were
uncertain about the causes for such behaviors. As Ouellette and
Wood (1998) speculated, the intentions that initially directed hab-
its can become implicit as behavior becomes more automatic, and
performance of such acts often continues even when they conflict
with conscious desires. The dissociation between habitual behavior
and explicit cognitive judgments is illustrated in Trafimow’s
(2000) finding that intentions for habitual behavior tend not to be
well-integrated with other aspects of conscious reasoning (e.g.,
attitudes, subjective norms). Presumably, these judgments about
habitual behavior lack coherence because people do not rely on
them to guide behavior and thus rarely think about them. The
possibility that behavior can be determined by implicit as well as
explicit intentions has parallels to Wilson, Lindsey, and Schooler’s
(2000) analysis of dual attitudes, in which people access and rely
on implicit attitudes except when motivated and able to override
these with their explicit judgments.
Habits and Complex Behaviors
To evaluate the generality of the present framework across
behavioral domains, we conducted analyses to examine whether
the correspondence between behavior and thought varied with
behavioral complexity. Highly complex behaviors, such as study-
ing and taking lecture notes, likely require more thoughtful guid-
ance to tailor responses to novel feedback from the environment
than less complex behaviors, such as cooking and exercising. For
the analyses, we aggregated the ratings to yield percentage data
that were independent at the level of the individual. These per-
centages were analyzed in a Mode of Performance (habit vs.
nonhabit) ⫻ Correspondence (thoughts and behavior did vs. did
not correspond) ⫻ Behavior Complexity (high vs. low) repeated
measures ANOVA.
Study 1. The three-way interaction between mode of perfor-
mance, correspondence, and complexity was not significant (F ⬍
2), indicating that behavior complexity did not modify the relation
between performance mode and thought–behavior correspon-
dence. However, all two-way interactions and main effects were
significant. The Mode of Performance ⫻ Correspondence interac-
tion, F(1, 63) ⫽ 47.10, MSE ⫽ .06, p ⬍ .001, revealed the standard
pattern obtained in the overall analysis. The Complexity ⫻ Cor-
respondence interaction, F(1, 63) ⫽ 53.61, MSE ⫽ .08, p ⬍ .001,
yielded a pattern consistent with our prediction that people are
more likely to think about their behavior when performing com-
plex actions. Specifically, for complex behaviors, participants’
thoughts were more likely to correspond to their behavior (M ⫽
83%) than to not correspond (M ⫽ 16%), t(61) ⫽ 11.65, p ⬍ .001,
whereas for simpler behaviors, thoughts were more likely not to
correspond (M ⫽ 55%) than to correspond (M ⫽ 44%), t(62) ⫽
⫺1.68, p ⬍ .10. The significant Mode of Performance ⫻ Com-
plexity interaction, F(1, 63) ⫽ 59.99, MSE ⫽ .06, p ⬍ .001,
revealed that habits were more likely to be low in complexity
(M ⫽ 79%) than high in complexity (M ⫽ 21%), t(62) ⫽⫺8.57,
p ⬍ .001, whereas nonhabits did not demonstrate an effect (Ms ⫽
54% and 46% for high and low complexity, respectively, t ⬍ 1.5).
Study 2. The results of the analyses above were highly similar
to those from Study 1. A nonsignificant three-way interaction (F ⬍
1) emerged in conjunction with significant two-way interactions
between mode of performance and correspondence, F(1,
208) ⫽ 84.80, MSE ⫽ .03, p ⬍ .001, complexity and correspon-
dence, F(1, 208) ⫽ 207.51, MSE ⫽ .03, p ⬍ .001, and mode of
performance and complexity, F(1, 208) ⫽ 121.50, MSE ⫽ .03, p ⬍
.001. Because the patterns of means comprising the interactions
were essentially identical to the first study, they will not be
presented in detail.
As we had anticipated, both studies revealed that the complexity
of behavior affected the extent to which participants thought about
their actions while performing them. Greater thought was devoted
to complex behaviors (e.g., studying, conversing with others),
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WOOD, QUINN, AND KASHY
presumably because these required tailoring responses to ongoing
input, than to less complex behaviors (e.g., exercising, cooking).
Although we had suspected that behavioral complexity might
interact with mode of performance to predict thought about be-
havior, such an effect did not emerge in either study. Instead, it
seems that greater thought is required to perform less complex
behaviors as well as more complex ones when the behaviors are
performed infrequently or in unstable contexts than when per-
formed frequently in stable ones. Thus, it appears that the effects
of behavior complexity on behavior-relevant thought were rela-
tively independent from mode of performance, and that the rela-
tions between mode of behavior performance and extent of
behavior-relevant thought were robust across behavioral domains.
Implicit Theories of Habit: Study 2
As a secondary focus of this research, we examined participants’
implicit theories about habitual behavior. After completing the
diary measures, participants identified whether they considered
each behavior they listed to be a habit. The analysis examined the
extent to which the participants’ categorizations of their own
behaviors as habits could be accounted for by other features of the
behavior (e.g., frequency of performance). Specifically, we used
hierarchical linear modeling to evaluate the extent to which vari-
ability in habit judgments could be predicted by these other at-
tributes of behavior. This analysis was conducted using the statis-
tical package HLM Version 5 (Raudenbush, Bryk, Cheong, &
Congdon, 2001), which includes an option for modeling categor-
ical outcome measures.
The analysis revealed that participants were more likely to label
behaviors as habits to the extent that the behaviors had been
performed frequently in the past, t(204) ⫽ 19.82, p ⬍ .001, had
been performed in stable contexts, t(204) ⫽ 3.71, p ⬍ .001, did not
require much thought to perform, t(204) ⫽ 14.33, p ⬍ .001, were
low in complexity, t(204) ⫽⫺2.23, p ⬍ .001, and were explained
in terms of external causes rather than internal ones, t(204) ⫽ 6.46,
p ⬍ .001. In this model, the following proved not to be significant
when entered simultaneously with the above predictors: changes in
the intensity of emotional experience, whether thoughts corre-
sponded to behavior, the presence of other people, ratings of
attention required by a behavior, and the difficulty in performing
the behavior. It appears, then, that our participants’ definitions of
habits relied on our frequency and stability criteria, but in addition
took into account the complexity of the behavior, the thought
required for performance, and the perceived causes of the act.
Given our reliance on self-reports, readers may wonder whether
participants’ theories about habitual behavior affected their re-
sponses in the diaries. To minimize this possibility, we did not
inform participants until the debriefing that the study concerned
habits. Also, participants rated whether behaviors were habitual
only at the end of the study, after they had completed all diary
measures. Thus, we envision that diary responses were generated
through a relatively simple process in which participants were
actively engaged in their everyday activities and they reported on
their immediate experiences when cued by the watch.
General Discussion
Our findings demonstrate the differing thought processes and
emotional experiences associated with habitual and nonhabitual
performance of behavior. Participants were less likely to think
about their behavior when performing habits, defined as repeated
acts in stable contexts, than when performing nonhabits, defined as
relatively novel acts and acts in variable contexts. Specifically,
participants’ thoughts wandered from their behavior during habit
performance about 50%–60% of the time. We assume that thought
about behavior-irrelevant factors is an indicator of the limited,
sporadic conscious processing required by habit performance. Of
course, behavior-relevant thought is only one indicator of auto-
matic behavior, and we did not evaluate other indicators such as
the ability to multitask with minimal performance interference or
the ability to perform tasks efficiently. However, we see no reason
to assume that these other indicators of automaticity would have
yielded highly divergent findings from the ones we report.
In addition, we were able to estimate the percentage of all of
participants’ actions that were performed with seemingly minimal
conscious guidance. Our participants performed almost 50% of
their behaviors without thinking about them. This estimate is
considerably lower than prior speculations, which placed the per-
centage of behaviors in daily life that are performed in a nonde-
liberative, relatively thoughtless manner at around 95% (see Bargh
& Chartrand, 1999). It could be argued that our estimate is a lower
bound to the incidence of nonthoughtful behavior, given that our
sample was comprised of college students who may spend greater
portions of their day in thought, study, and novel activities than
other individuals. However, even the present estimate renders a
picture of people as relatively detached from their ongoing activ-
ity—at least half of the time.
Thought about behavior was varied and addressed specific in-
strumental intentions about how to do something (e.g., “what
lipstick color am I going to wear?”), subjective reactions to the
performance (e.g., “how boring and pointless what we are doing
is”), outcomes of the behavior (e.g., “we better win”), aspects of
the context that might facilitate or hinder performance (e.g., “I was
thinking that I need to write faster to keep up with the notes”),
self-related thoughts (e.g., “that I am mathematically inept”), and
simple descriptions (“driving”). Our estimate of behavior-relevant
thought thus included specific, lower level concrete details as well
as higher level, more abstract goals (Vallacher & Wegner, 1987).
We coded all of these types of thoughts as corresponding to
behavior because we assumed that they all can contribute to
conscious guidance of action.
Although we have emphasized the occasions on which partici-
pants did not think about their behavior, the finding that partici-
pants thought about habits about 40% of the time suggests that the
habitual mode of behavior regulation is best characterized by
minimal or sporadic cognitive monitoring and not by the complete
absence of thought. Yet, it is also likely that the present study
provides a somewhat inflated estimate of the thoughtful processing
guiding behavior. One reason is that the measure of thought was
sufficiently global that it may not have captured automaticity when
it emerged in only the initiation, execution, or termination of a
response. Participants may have indicated thinking about their
behavior when any of these aspects of performance required de-
liberation. In addition, it is worth noting that thoughts were as-
sessed immediately following actions in the diaries. Thus, consis-
tency and saliency pressures may have encouraged participants to
focus on their actions when reporting their thoughts. Finally, in
natural settings a number of factors in addition to the mode of
1293
HABITS IN EVERYDAY LIFE
processing guiding behavior are likely to increase thought–
behavior correspondence. As we explained in Footnote 6, the
presence of others increased correspondence, presumably as a
result of increased self-consciousness. Thus, it may be that the
present study overestimates the extent to which thought was in-
volved in guiding behavior, especially habitual behavior.
Multiple Processes Guiding Behavior
The present results contribute to the developing evidence that
action emerges from multiple systems that guide behavior. As we
noted in the introduction to this article, predictive models of
behavior, neuropsychological evidence of brain activation, and
cognitive analyses of memory performance all converge in sug-
gesting that behavior can be guided by habitual processes in the
case of well-learned behaviors or by more explicit processes in the
case of novel behaviors or ones performed in difficult, shifting
contexts.
The present results illuminate several aspects of this multiple
system model. First, they are relevant to the question of how to
measure habits. The differing content of thoughts during perfor-
mance of well-practiced behavior in stable contexts versus less
practiced behavior or behavior in unstable contexts provides some
validation for the traditional definition of habits in terms of be-
havior frequency (e.g., Triandis, 1977). Although in some accounts
the predictive effects of frequent past behavior emerge in part
because past behavior reflects intentions, perceived control, and
other factors (Ajzen, 2002), our findings suggest that people are
not necessarily thinking about intentions or these other predictors
of behavior during habit performance. Thus, the limited thought
about habitual behavior is consistent with the idea that frequently
performed acts in stable contexts are habitual in the sense that they
are guided by relatively automatic processes that involve minimal
thought.
In addition, the limited thought associated with habit provides
insight into the psychological mechanisms through which habitual
tendencies guide behavior. Specifically, our findings imply a mi-
nor role for conscious intentions. Our findings cast doubt on the
idea that habitual behavior is guided by conscious intentions that
are automatically activated when behavior-relevant goals are sa-
lient (Aarts & Dijksterhuis, 2000; Ajzen, 2002). Although people
may be able to report on their intentions when directly requested in
experimental contexts, in daily life habitual behavior is apparently
guided by implicit processes that operate outside of conscious
awareness. These implicit processes may include intentions that
are incorporated into broad sequences of action that are cued by
stable environmental conditions. Such intentions are not easily
accessed by standard self-report procedures but instead are ex-
pressed in behavior. Given that the focus of the present study was
not on behavior prediction, we did not measure intention or other
components of planned behavior. Yet, the limited evidence for
thought about habitual behavior suggests that these components as
assessed explicitly in the standard behavior prediction study may
not provide much insight into the factors guiding habits in every-
day contexts.
Our findings also provided reassuring evidence of the generality
of a dual-mode model of guiding behavior. That is, the distinction
between explicit and habitual guides to behavior held across be-
havioral domains. Even complex acts that required online moni-
toring for effective performance (e.g., studying, conversing with
others) were performed with less behavior-relevant thought, and
presumably greater automaticity, given frequent practice in stable
contexts. Thus, the habitual performance mode is not only relevant
to simple actions such as typing, driving, and cooking, but is also
useful for understanding the guidance of complex behaviors that
are tailored to ongoing input. It may be that, with practice, people
form expectations about the general form and content of this input
and develop standard patterns of response that reduce the amount
of thought required for actions. This is perhaps illustrated in the
stereotypic interaction between long-married couples at breakfast,
in which a conversation can be maintained despite the inattention
of one partner who has learned to respond appropriately to pauses
while reading the newspaper or being otherwise engaged.
Habitual Behavior and Emotional Response
The mode of behavior performance proved to have implications
for emotional experiences. Overall, habitual behavior was associ-
ated with lesser intensity emotions than nonhabits. Also, partici-
pants were especially likely to identify their thoughts rather than
their behavior as the source of emotions when engaged in habits.
This general pattern in which habit-related emotions are low in
intensity and elicited by thoughts could have implications for
broader lifestyle patterns. We speculate that people whose lives are
characterized by large proportions of habitual behavior can find
that their emotional experiences become dull and subdued over
time. Much like Thurber’s (1942) character, Walter Mitty, they
may find that their own ruminations and fantasies are the primary
source of their emotions rather than their immediate behavioral
experiences.
It is also worth noting that the mode of behavior performance
did not affect all emotions in the same manner. Although we had
anticipated that habituation would increase ratings of fatigue and
boredom, these experiences did not vary with behavior mode. Yet,
self-related emotions did vary with the mode of behavior perfor-
mance. Specifically, participants experienced lesser pride and
worse feelings about the self associated with habitual than nonha-
bitual behavior. This effect seems to be part of a broader pattern in
which habits were not judged to be especially self-relevant. Habits
were considered relatively uninformative about the self, relatively
unimportant to attaining personal goals, and the causal mecha-
nisms responsible for them were not readily apparent to partici-
pants. These effects may reflect the overall tendency for habitual
behaviors to be viewed as imposed and not freely chosen (see
Wegner & Wenzlaff, 1996). In daily life, the disconnection be-
tween habitual behavior and the self has a number of implications.
For example, if people do not see themselves as especially respon-
sible for their habits, they may not believe that they have sufficient
efficacy to change such acts. Also, goals achieved through routin-
ized activity may not be a strong source of pride. Thus, healthy
lifestyle decisions that become routinized as part of one’s daily
behavior may not yield a sense of personal accomplishment be-
cause the behavior does not appear to be volitional.
Another noteworthy aspect of the emotion findings is the lesser
stress, burnout, and feeling of being out of control that participants
experienced when engaged in habitual than nonhabitual behaviors.
Their feelings of stress increased with the deliberation involved in
a single nonhabitual behavior, but did not increase further when
1294
WOOD, QUINN, AND KASHY
participants were performing multiple nonhabits simultaneously.
This finding echoes Baumeister et al.’s (1998) laboratory research
indicating that the act of decision making about a single behavior
can deplete self-control mechanisms and impair subsequent acts of
self-regulation such as decision making and performance. From
this perspective, the stress-reducing benefits of the muted emo-
tional experiences associated with habit performance emerged
because habits do not drain self-control resources to the same
extent as nonhabits. In general, the lesser stress associated with
habits than nonhabits provides an initial framework to develop a
social psychological perspective on the role of habit in the every-
day self-regulation of behavior.
Diary Data Collection Methods in Social Cognition
We used a signal-contingent diary method to provide a new
perspective on the much researched question of the processes and
consequences associated with habitual versus explicit guides to
behavior (and related distinctions between conscious vs. noncon-
scious, automatic vs. controlled processes). Although diary meth-
ods do not appear to have been mined extensively by researchers
in attitudes and social cognition (see Rozin’s, 2001, analysis of the
articles appearing in the Attitudes and Social Cognition section of
the Journal of Personality and Social Psychology), they have been
used effectively to track the naturally occurring prevalence of a
variety of social and personality phenomena and their fluctuations
with natural events (see Reis & Gable, 2000; Stone, Shiffman, &
DeVries, 1999). These data collection techniques also have been
used in ecological studies of memory in everyday life (Neisser &
Libby, 2000). Contemporaneous reports are especially useful to
study habitual behavior because they can minimize the biases
associated with retrospection that emerge when people have not
attended to the behavior of interest (Reis & Gable, 2000; Stone,
Shiffman, & DeVries, 1999).
Our diary studies of everyday experience are inherently corre-
lational and need to be combined with other methodologies to
illuminate the causal ordering between thought, emotion, and
behavior. We have argued that thought content and emotional
intensity are products of the mode through which behavior is
performed. This sequence of events is consistent with experimental
research in cognitive and social psychology demonstrating the
various consequences of automatically versus consciously guided
action (e.g., Bargh & Ferguson, 2000; Frijda, 1988; Jacoby et al.,
1997; Baumeister et al., 2000). However, the relations that
emerged in our research between thoughts, emotions, and behav-
iors also might suggest that the mode of initiating and guiding
behavior depends on emotions or on thoughts. Experimental in-
vestigation would be needed to evaluate the plausibility of these
alternate scenarios.
Conclusion
Overall, our findings provided a highly textured picture of the
consequences associated with the mode of behavior performance.
Habits appear to be associated with a variety of benefits as well as
costs. Probably the most striking benefit is the one that is best-
known—the cognitive economy and performance efficiency of
habits. This emerged in the lesser awareness of habitual than
nonhabitual behavior. Habits potentially free people to engage in
other kinds of important thoughtful activities such as rumination of
past events and planning for future activities. Another important
advantage of habits is their association with reduced stress and
greater feelings of control. In daily life, habit performance is not
likely to deplete self-regulatory resources in the same way as
deliberative behavior and this may allow people to conserve reg-
ulatory strength for important decisions.
Yet, these potential benefits of habitual acts co-occur with clear
disadvantages of automating behavior. Other research has already
begun to suggest some of the disadvantages of automaticity. For
example, when judgments become automatic, people may react on
the basis of past experience and be less responsive to small
changes in the relevant stimuli (e.g., Fazio, Ledbetter, & Towles-
Schwen, 2000). Repetition of behavior may continue even when
the behavior is no longer the most appropriate, effective response.
A vivid example of this possibility was provided by Ferguson and
Bibby’s (2002) habitual blood donors, who were apparently unde-
terred in their future willingness to donate when other donors
fainted in their presence. In contrast, occasional donors contributed
less in the future when fellow donors suffered in this manner. In
the present study, another potential disadvantage of habits emerged
in the subdued emotions and lesser pride people experienced when
performing behaviors habitually. Habit performance seems to have
an insulating quality that reduces the immediacy of emotional
experience. Other disadvantages are apparent in the findings that
people viewed habits to be relatively uninformative about the self,
unimportant in attaining personal goals, and associated with rela-
tively negative self-evaluations. It may be that, when people do not
think about their behavior, their acts reflect implicit intentions that
do not necessarily represent their current goals and plans. In
general, these varying benefits and costs of automating behavior
highlight the importance of strategically using habits in daily life
to accomplish tasks efficiently with minimal stress and yet still
maintain a sense of personal involvement and emotional engage-
ment in ongoing activities.
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Received December 3, 2001
Revision received May 7, 2002
Accepted May 8, 2002 䡲
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