Habit Formation and Change
Lucas Carden1 and Wendy Wood1,2
Affiliations: 1Department of Psychology, University of Southern California, 2Marshall School
of Business, University of Southern California.
Abstract
This review highlights emerging findings, topics, and new directions in research on
habitual behavior. We cover how cognitive, attentional mechanisms contribute to habit
formation, how habit is transforming the way we think about self-control, and how focusing on
environmental as well as intrapsychic forces yields more success at habit change. Finally, we
describe studies using big data and new technologies that offer novel methods to study habits
outside the lab by capturing repeated actions in the natural environments in which they occur.
Habit Formation and Change
Ninety-nine hundredths or, possibly, nine hundred and ninety-nine thousandths of our activity is
purely automatic and habitual, from our rising in the morning to our lying down each night. -
William James (1899)
William James never failed to make provocative claims, especially regarding the wide-
reaching influence of habit on human behavior. Over a century later, research has moved beyond
claims of the importance of habit to identifying the psychological mechanisms that drive habit
formation and change.
Habits form as people pursue goals in daily life. When repeatedly performing a behavior
in a particular context, people develop implicit associations in memory between contexts and
responses. Instrumental and Hebbian learning processes are involved (Wood & Rünger, 2016).
As people repeat behavior in a stable context, their intentions and goals to perform the behavior
gradually become less influential, whereas habits increase in influence (Ouellette & Wood, 1998;
Sheeran, Godin, Conner, & Germain, 2017). A number of recent papers have theorized how the
shift from goal-directed behavior to habit learning occurs in detail (Cushman & Morris, 2015;
Gardner, 2015; Wood & Rünger, 2016). Ironically, this shift in control of behavior does not
often occur in people’s lay theories of habit (for review, see Wood, 2017). People commonly use
volitional, goal-directed explanations for why they had performed their habits, even though
intentions and goals are poor predictors of habit (Neal, Wood, Labrecque, & Lally, 2012).
Psychology has in recent years focused on the flexible responses generated by the
nonconscious activation of goals and attitudes (Weingarten et al., 2016). In contrast, habit cuing
involves relatively fixed response patterns. Habits are slow to develop and change in comparison
to other implicit processes, such as Pavlovian fear conditioning and semantic associations
(Amodio & Ratner, 2013). To this end, habits are a challenging construct to measure and
manipulate in the lab. As we show, new technologies provide novel insights into habits in the lab
and everyday contexts (Ram, Wang, Currim, & Currim, 2015).
In this paper, we highlight emerging findings, topics, and new directions in research on
habit. Specifically, cognitive, attentional mechanisms appear central to instrumental learning of
habits, understanding of habit has transformed research on self-control (Galla & Duckworth,
2016), behavior change interventions benefit from changing environments (Marteau, Hollands,
Fletcher, 2012; Rothman et al. 2015), and big data provide novel insight into habits on a large
scale (Larcom, Rauch, & Willems, 2017).
Cognitive Processes of Habit Formation
Attentional mechanisms are important in habit formation, given evidence that
instrumental learning guides attention to context cues (Anderson, 2016; Le Pelley, 2016). That
is, stimuli that have been rewarded in the past acquire attentional priority over non-rewarded
ones (Luque et al., 2017). This phenomenon was demonstrated in experiments in which
participants learned to associate, for example, colored circles on a computer screen with
monetary rewards. When the task was then reconfigured so that the rewarded stimuli were
distractors and participants were to choose new targets, the simple presence of the distractors
impeded performance (Anderson, 2016). Through such basic perception and attention systems,
people preferentially recognize environmental features associated with past rewards.
Evidence that context cues automatically bring habitual responses to mind comes from a
series of studies in which participants practiced a sequential task of making sushi in a computer
game (Labrecque & Wood, 2017). With extensive practice, participants were able to quickly
report the next step in the sequence when primed with the prior step. The strength of habit
associations determined habit persistence. When participants were especially fast in the priming
task, indicating strong habits, their habits persisted even when they wanted to alter the recipe and
add a new ingredient.
Habit resistance to change is understandable given context cues that capture attention
automatically and given habitual responses that are activated automatically on perception of the
cue. Through these basic mechanisms, features of the environment are interwoven into habit
formation and change.
Habits and Effortless Self-Control
William James (1890) claimed that “the more of the details of our daily life we can hand
over to the effortless custody of automatism, the more our higher powers of mind will be set free
for their own proper work.” By implying that the main benefit of forming habits was to reduce
the need for inhibition and self-control, James was prescient about contemporary research on
self-control.
Self-control traditionally is a struggle in which one part of ourselves tries to stop another
part of ourselves from responding (Duckworth, Gendler, & Gross, 2016). This is captured in the
conflicts between one marshmallow now vs. two marshmallows later (Michel, Shoda, Rodriguez,
1989), a farsighted planner and a myopic doer (Thaler & Shefrin, 1981), and a muscle that resists
temptations for a future self (Baumeister, Vohs, & Tice, 2007). In this struggle, habits were
treated as a target of self-control, needing to be inhibited (Quinn, Pascoe, Wood, & Neal, 2010).
More recently, research has recognized James’s claim, highlighting habit as an automatically
activated response that may be consistent with goals (Neal, Wood, & Drolet, 2013).
This shift from habits-as-impediments to habits-as-beneficial is evident in research on
trait self-control (Tangney, Baumeister, & Boone, 2004). We now know that people who score
high on such scales do not engage in much effortful inhibition (Hofmann, Baumeister, Förster, &
Vohs, 2012). In fact, they experience less motivational conflict and report less inhibition of
temptations in daily life compared with people with low self-control (Gillebaart, Schneider, &
De Ridder, 2015; Imhoff, Schmidt, & Gerstenberg, 2014). Instead, people high in self-control
have weak habits for unhealthy activities (e.g., eating junk food, Adriaanse, Kroese, Gillebaart,
& De Ridder, 2014) and strong habits for healthy activities such as sleep, exercise, and work
(Galla & Duckworth, 2015; Gillebaart & Adriaanse, 2017). One longitudinal study showed that
adolescents with high trait self-control had formed meditation habits that better met their goals 3
months after a meditation retreat (Galla & Duckworth, 2015). Furthermore, experimental
research has shown that positive habits actually protect people from conflicting desires and
willpower depletion (Lin, Wood, & Monterosso, 2016).
High trait self-control may thus reflect a kind of situational strategy involving arranging
environmental cues to promote beneficial habit formation (Duckworth, Gendler, & Gross, 2016).
Such people appear to actively avoid situations offering temptations and distractions (Ent,
Baumeister, Tice, 2015). For example, they may reengineer their home or work environments:
make eating healthy snacks easier by placing fruit on their kitchen counters (Sobal & Wansink,
2007; Wansink, Hanks, & Kaipainen, 2015). In education settings, students who used more
situational strategies, such as hiding their cellphone, were more likely to reach their academic
goals (Duckworth et al, 2016).
Effortless self-control might seem an oxymoron given the traditional characterization of
self-control as inhibition. However, we now know that self-control involves a wide range of
responses beyond willpower. To be successful, people high in self-control appear to play offense,
not defense, by anticipating and avoiding self-control struggles. They form beneficial habits that
are activated automatically by the environments in which they live.
Changing Habits
Behavior change interventions have been challenged to successfully alter lifestyle
behaviors like diet, exercise, environmental sustainability, and financial solvency (Rothman et
al., 2015). For example, the national 5-A-Day-For-Better-Health fruits and vegetables campaign
presented people with information about the pros and cons of health behaviors, attempting to
motivate them to change. The campaign successfully increased people’s knowledge about what
they should do to be healthy, but had limited effect on eating habits (Stables et al., 2002).
Another example comes from highly controlled studies designed to change habits using
incentives. These are typically successful in achieving short-term change but fail to maintain
change over time, after the incentives are removed (for review see, Wood & Neal, 2016).
A habit perspective anticipates limited change in behavior when performance contexts
remain stable. Because habits are stored in procedural memory relatively separate from goals and
intentions, encountering the same context activates habitual responses, even when newly adopted
intentions are strong (Walker, Thomas, & Verplanken, 2015). The slow pace of habit learning
was shown with a variety of health habits, such as exercising, that develop only after many
months of repetition in stable contexts (Lally, Van Jaarsveld, Potts, & Wardle, 2010).
New directions in habit change include not only changing beliefs and perceptions but also
changing situational factors. We consider these various strategies below.
Implementation Intentions and Reminders
Popular behavior change interventions involve planning and reminders. For example,
implementation intentions help people to remember and act on intentions to change behavior.
Although earlier reviews indicated the effectiveness of implementation intentions (Adriaanse et
al., 2011), a meta-analysis of over 44 diet studies showed only small behavior change effects
during the interventions and negligible long-term effects (Turton et al., 2016). Especially for
strong antagonistic habits, like eating behavior, implementation intentions have little impact
(Webb, Sheeran, & Luszcynska, 2009). Potentially, implementation intentions could promote
habit formation when used to promote repetition in particular contexts (Wood & Neal, 2016).
Interestingly, the meta-analysis revealed more success with food-specific inhibition and attention
bias modification training, (Turton et al., 2016), both of which may target the cognitive
mechanisms underlying habit.
Reminders and symbolic rewards like trophies are common features of web and
smartphone based programs (Stawarz, Cox, & Blandford, 2015). Although reminders may be
effective in the short-term, they can impede habit formation in the long term (Stawarz, Cox, &
Blandford, 2015). Such applications can promote app dependence instead of continued repetition
of a behavior following app use (Renfree et al., 2016). In the future, we predict that such apps
will use context-aware technologies, reminding users to perform behaviors when in specific
environments (see for review, Chen, Ding, Huang, Ye, & Zhang, 2015). In this way, behavior
change apps can facilitate habit formation by connecting specific environmental cues with
desired responses.
Environmental Forces
When environments change, the cues activating habits may change also, with the result of
disrupting habit performance. Without familiar habit cues, people are forced to make decisions
about how to act.
According to the habit discontinuity effect, behavior change interventions are more
effective during life course changes that disrupt habit cues, such as moving house, having a
child, and changing jobs (Verplanken, Walker, Davis, & Jurasek, 2008; Walker, Thomas, &
Verplanken, 2015). The absence of old cues provides a window of opportunity to make decisions
and implement new goals and intentions. In illustration, a recent field experiment with over 800
households, half of which recently relocated, received an informational intervention to promote
25 environmental behaviors (Verplanken & Roy, 2016). The intervention was more effective for
those who had relocated with the last 3 months (see also Bamberg, 2006; Thøgersen, 2012;
Walker, Thomas, & Verplanken, 2015).
A serendipitous example of how environmental disruption changes societal habits
occurred with a two day partial London Tube workers strike in February, 2014. From 200
million points of card swipe data, researchers tracked commuters’ transportation habits before
and after the strike (Larcom, Rauch, & Willems, 2017). The disruption led 5% of commuters to
adopt new, more optimal travel-route habits, and these occurred especially in areas where the
tube map was inaccurately drawn. The disruption of old cues thus enabled commuters to
discover and form more optimal traveling habits.
Although habits can be disrupted by changes in macro environments or during life
transitions, habit performance can also be altered through choice architecture or environmental
reengineering interventions that change the structure of everyday decisions (Thaler, Sunstein, &
Balz, 2012; Rozin, Scott, Dingley, Urbanek, Jiang, & Kaltenbach, 2011). Such interventions
typically target a single conscious decision, such as opting-into a program vs. opting out.
Although altering the decision structure may promote habit formation by making it easier to
perform a desired action, habit formation requires repeated responses in a stable context.
Fortunately, single environmental changes, such as dedicating a prominent place for fruits and
vegetables on the kitchen counter, might guide people into ripcurrents (Frey & Rogers, 2014),
potentially leading to a cascade of psychological changes that maintain new behaviors, including
identity (Wilson, 2011) and physical changes such as weight loss (Carels et al., 2014).
Using Big Data and Smart Phones to Study Habits in Everyday Life
In the past decade, big data and smartphone technologies offer revolutionary new ways to
study habits in daily life. These open up fine-grained analysis of the context cues that trigger
everyday habits. For example, a smoking cessation study combined ecological momentary
assessment of reported cravings with geo-location mapping (via smartphones) of exposure to
point-of-sale tobacco cues (Kirchner et al., 2013). Relapse rates increased with exposure to
smoking cues, even when participants were not experiencing cravings. This study suggests that
environmental cues direct attention and activate a behavioral response in mind, even when
people are not experiencing a desire to act.
Big data analyses also reveal important social consequences to seemingly mundane
habits, such as how often and where students make purchases on campus (Ram, Wang, Currim,
& Currim, 2015). Instead of survey-based methods to assess student retention, researchers
modeled students’ social networks from the frequency and location of ID card transactions (e.g.,
campus restaurant, printer services). Students were less likely to drop out in their freshman year
if they showed more regularity in their transactions, suggesting greater social integration on
campus. An implication is that at-risk students can be identified from such indicators of habitual
social integration, and retention interventions can be designed accordingly.
Conclusion and Future Directions
Habit research has blossomed over the past few years. We are making progress on how
basic cognitive mechanisms like attention relate to habit formation (Anderson, 2016), how
people with high self-control use habits to achieve their goals (Galla & Duckworth, 2016), and
how habits are influenced by environmental disruptions (Verplanken & Roy, 2016). Additional
advances include exciting research on how social interaction habits contribute to intergroup
relations (Hackel, Doll, and Amodio, 2015) and lay beliefs about habit formation and
performance (Carden, Wood, Neal & Pascoe, 2017; for review, see Wood, 2017).
Future habit research can mine new technologies to measure the context cues that drive
habits. In a unique study in addiction research, smokers took pictures of their favorite smoking
environments and brought them into the lab for cue-reactivity tests (Conklin et al., 2010).
Personalized smoking environments led to stronger cravings than generic environments. This
method holds strong promise for studying many kinds of habits in the lab.
Habits in general stretch researchers’ capabilities because they are interactions between
persons and environments. They reflect the past implicit learning as activated by current context
cues. On the one hand, studying habit involves understanding implicit perceptual, attentional,
and memory processes. On the other hand, habit research involves identifying the visual,
visceral, and social cues, and their combinations, that activate habits in mind. Recent research
discoveries provide a strong foundation to understand both person and situation in these ways.
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**Wood, W., & Rünger, D. (2016). Psychology of habit. Psychology, 67. 289-314.
http://dx.doi.org/10.1146/annurev-psych-122414-033417
This paper provides a broad review of the cognitive, motivational, and neurobiological bases of
habit. A model is presented of the many ways in which habit interfaces with goal-directed
processes.
Wood, W. (2017). Habit in personality and social psychology. Personality and Social
Psychology Review, 1-15. https://doi.org/10.1177/1088868317720362
Wood, W., & Neal, D. T. (2016). Healthy through habit: Interventions for initiating and
maintaining health behavior change. Behavioral Science & Policy, 2, 71-83.
... This is important since lifestyle advices often revolve around habits that need to be (re)learned. Because changing or learning habits is a slow process [30], the patient-tailored PROfeel lifestyle advices often represent a step in the participant's process of (re)learning a habit. The advices incorporate factors known to strengthen habit learning, such as anticipating and avoiding self-control struggles, planning, and/or environment changes [30]. ...
... Because changing or learning habits is a slow process [30], the patient-tailored PROfeel lifestyle advices often represent a step in the participant's process of (re)learning a habit. The advices incorporate factors known to strengthen habit learning, such as anticipating and avoiding self-control struggles, planning, and/or environment changes [30]. For example, when a participant struggles with rumination (i.e., worrying) throughout the day, an advice could be to reduce rumination time by practicing distracting attention. ...
... The surveys function as both monitoring and reminder tools for the participant. Reminders are often used as a tool in behavior change interventions [30], and a meta-analysis has shown that reminders can make a small contribution to behavior change in dietary interventions [34]. ...
Article
Full-text available
Background Chronic fatigue with a debilitating effect on daily life is a frequently reported symptom among adolescents and young adults with a history of Q-fever infection (QFS). Persisting fatigue after infection may have a biological origin with psychological and social factors contributing to the disease phenotype. This is consistent with the biopsychosocial framework, which considers fatigue to be the result of a complex interaction between biological, psychological, and social factors. In line, similar manifestations of chronic fatigue are observed in chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) and juvenile idiopathic arthritis (JIA). Cognitive behavioral therapy is often recommended as treatment for chronic fatigue, considering its effectiveness on the group level. However, not everybody benefits on the individual level. More treatment success at the individual level might be achieved with patient-tailored treatments that incorporate the biopsychosocial framework. Methods In addition to biological assessments of blood, stool, saliva, and hair, the QFS-study consists of a randomized controlled trial (RCT) in which a single-subject experimental case series (N=1) design will be implemented using Experience Sampling Methodology in fatigued adolescents and young adults with QFS, CFS/ME, and JIA (aged 12–29). With the RCT design, the effectiveness of patient-tailored PROfeel lifestyle advices will be compared against generic dietary advices in reducing fatigue severity at the group level. Pre-post analyses will be conducted to determine relevance of intervention order. By means of the N=1 design, effectiveness of both advices will be measured at the individual level. Discussion The QFS-study is a comprehensive study exploring disrupted biological factors and patient-tailored lifestyle advices as intervention in adolescent and young adults with QFS and similar manifestations of chronic fatigue. Practical or operational issues are expected during the study, but can be overcome through innovative study design, statistical approaches, and recruitment strategies. Ultimately, the study aims to contribute to biological research and (personalized) treatment in QFS and similar manifestations of chronic fatigue. Trial registration Trial NL8789. Registered July 21, 2020.
... www.nature.com/scientificreports/ habit change in everyday settings has also implicated the role of effortful inhibition and self-control in overcoming unwanted behaviors 18,20 . Importantly, however, how inhibitory control-the ability to suppress prepotent but unwanted actions, thoughts, or emotions 21,22 -affects habit change when complex associations need to be modified has not yet been directly probed in a controlled experimental setting in healthy humans. ...
... Furthermore, Mary may, consciously or unconsciously, suppress some aspects of her habitual behavior of not dividing waste, which could exacerbate the above-described behavioral pattern. Since old and new behaviors coexist, and a continuous inhibition of the old behavior may be unsustainable over longer periods, our findings highlight that interventions using other approaches for habit change must be tested (for further discussion see 18,36,37 ). One might argue that our results are driven by an incomplete acquisition of the new knowledge as suggested by data from the Rewiring phase (see also the "How does acquisition of new knowledge compare with the initial learning process?" section in the Supplementary Information). ...
... Throughout the experiment, participants were informed that they would participate in an experiment assessing reaction times and response accuracy changes over extended practice; thus, both learning and rewiring occurred incidentally 57 . This was chosen because in everyday situations many habits are developed incidentally 18,44 ; note the current study aimed to test the role of inhibitory control on (un)learning processes and not the effect of incidental vs. intentional processes on rewiring, for that see 23 . For the detailed description of the ASRT task and the structural changes introduced in the Rewiring phase, see the "Supplementary methods" section in the Supplementary Information. ...
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Our habits constantly influence the environment, often in negative ways that amplify global environmental and health risks. Hence, change is urgent. To facilitate habit change, inhibiting unwanted behaviors appears to be a natural human reaction. Here, we use a novel experimental design to test how inhibitory control affects two key components of changing (rewiring) habit-like behaviors in healthy humans: the acquisition of new habit-like behavior and the simultaneous unlearning of an old one. We found that, while the new behavior was acquired, the old behavior persisted and coexisted with the new. Critically, inhibition hindered both overcoming the old behavior and establishing the new one. Our findings highlight that suppressing unwanted behaviors is not only ineffective but may even further strengthen them. Meanwhile, actively engaging in a preferred behavior appears indispensable for its successful acquisition. Our design could be used to uncover how new approaches affect the cognitive basis of changing habit-like behaviors.
... While habit research has traditionally used animal learning paradigms or lab-based tasks in humans (Smith & Graybiel, 2016), theoretical and methodological advances have inspired growth in studies of human habit formation for everyday actions in common contexts (Lally et al., 2010;Shiffman et al., 2008;Verplanken & Orbell, 2003). Research in controlled settings, such as experimental lab-based studies, offers important elucidation of core principles and mechanisms underlying habit formation (see, Carden & Wood, 2018). Studying habit formation "in the wild", however, can identify impediments that have minimal influence in controlled settings, such as forgetting to act (Lally et al., 2010), pursuing competing goals (Hamilton et al., 2019), or temporary removal from target contexts (Lally et al., 2011). ...
... Our criteria may seem overly restrictive. For example, by cautioning against using "frequency in context" measures to capture the relationship between repetition and habit formation, we discount the use of "big data" technologies to model habit development based on emergence of observable, context-consistent behavioural patterns (see, Carden & Wood, 2018). However, such studies track only predictable behaviour, not habit per se, and it is problematic to treat behaviour as both a precursor of habit and an index of the habit so generated. ...
Article
Full-text available
Advances in understanding how habit forms can help people change their behaviour in ways that make them happier and healthier. Making behaviour habitual, such that people automatically act in associated contexts due to learned context-response associations, offers a mechanism for maintaining new, desirable behaviours even when conscious motivation wanes. This has prompted interest in understanding how habit forms in the real world. To reliably inform intervention design, habit formation studies must be conceptually and methodologically sound. This paper proposes methodological criteria for studies tracking real-world habit formation, or potential moderators of the effect of repetition on formation. A narrative review of habit theory was undertaken to extract essential and desirable criteria for modelling how habit forms in naturalistic settings, and factors that influence the relationship between repetition and formation. Next, a methodological review identified exemplary real-world habit formation studies according to these criteria. Fourteen methodological criteria, capturing study design (four criteria), measurement (six criteria), and analysis and interpretation (four criteria), were derived from the narrative review. Five extant studies were found to meet our criteria. Adherence to these criteria should increase the likelihood that studies will offer revealing conclusions about how habits develop in real-world settings.
... Preferences are further linked to health behaviour through habits or repeated behaviour. The existence of habits in consumption behaviour and the persistence of habits over time have been extensively studied (Carden and Wood 2018;Loewenstein et al. 2016;Pollak 1976). For habitual behaviour, there is a positive relationship between past and current consumption (Becker 1992). ...
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This paper argues that the role of the socioeconomic environment in individual health decision-making in small developing countries particularly the island nations of the Caribbean, is typically not adequately accounted for in models. This creates a sizeable gap in the ability to adequately understand individual health behaviour particularly when it comes to modifiable non-communicable (NCD) behavioural risk factors and to craft and implement effective interventions. The paper’s objective is to outline a theoretical model, which explicitly includes the role of the socioeconomic environment in health decision-making as a first step to understanding how the socioeconomic environment influences health behaviour.
... The eventual adoption of a new habit may imply some automated behavior, overruling any conflicting motivations, even once the stimulus for the new habit is not there anymore (Marien et al., 2019). However, the maintaining of a habit for a long time is not guaranteed, as contexts may change (Gardner and Rebar, 2019;Carden and Wood, 2018). Furthermore, the continuation of a new behavior requires maintenance (Judah et al., 2013). ...
... Habit is formed as a consequence of repeating an action (Carden and Wood, 2018) and the more frequently customers use a service, the more likely it is that they will do so in the future (Wang et al., 2016). Chou et al. (2013) defined habit as the extent to which individuals automatically perform an activity. ...
Online retailers seek to motivate their customers to switch from web-based stores to retail apps as using the latter as a shopping channel has many benefits for retailers compared to web-based stores. This study aims to examine the drivers of customers' intention to switch from web-based stores to retail apps by applying the Stimulus-Organism-Response (S-OR) model. The moderating role of habit was investigated. Data were collected from 389 Malaysian individuals through an online survey and analysed using the partial least squares technique. The results indicated that performance expectancy has a significant influence on switching intention and is driven by mobile characteristics. Furthermore, effort expectancy has a positive effect on both performance expectancy and switching intention and is triggered by visual complexity and aesthetic quality. The moderating effects of habit on the associations between performance expectancy, effort expectancy and switching intention were not supported. The findings extend the literature on retail apps by exploring the switching intention drivers and testing the moderating effect of culture, which have received less attention. The results enable retail apps' developers and marketers to strategise retail apps development and marketing activities in a way that encourages current web-based stores' customers to use retail apps as a better alternative.
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Developing and enhancing societal capacity to understand, debate elements of, and take actionable steps toward a sustainable future at a scale beyond the individual are critical when addressing sustainability challenges such as climate change, resource scarcity, biodiversity loss, and zoonotic disease. Although mounting evidence exists for how to facilitate individual action to address sustainability challenges, there is less understanding of how to foster collective action in this realm. To support research and practice promoting collective action to address sustainability issues, we define the term “collective environmental literacy” by delineating four key potent aspects: scale, dynamic processes, shared resources, and synergy. Building on existing collective constructs and thought, we highlight areas where researchers, practitioners, and policymakers can support individuals and communities as they come together to identify, develop, and implement solutions to wicked problems. We close by discussing limitations of this work and future directions in studying collective environmental literacy.
Preprint
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Background Chronic fatigue with a debilitating effect on daily life is a frequently reported symptom among adolescents and young adults with a history of Q-fever infection (QFS). Persisting fatigue after infection may have a biological origin with psychological and social factors contributing to the disease phenotype. This is consistent with the biopsychosocial framework, which considers fatigue to be the result of a complex interaction between biological, psychological and social factors. In line, similar manifestations of chronic fatigue are observed in Chronic Fatigue Syndrome/Myalgic Encephalomyelitis (CFS/ME) and Juvenile Idiopathic Arthritis (JIA). Cognitive behavioural therapy is often recommended as treatment for chronic fatigue, considering its’ effectiveness on the group-level. However, not everybody benefits on the individual level. More treatment success at the individual level might be achieved with patient-tailored treatments that incorporate the biopsychosocial framework. Methods In addition to biological assessments of blood, stool, saliva, and hair, the QFS-study consists of a randomized controlled trial (RCT) in which a single-subject experimental case series ( N = 1 ) design will be implemented using Experience Sampling Methodology in fatigued adolescents and young adults with QFS, CFS/ME and JIA (aged 12–29). With the RCT design, the effectiveness of patient-tailored PROfeel lifestyle advices will be compared against generic dietary advices in reducing fatigue severity at the group-level. Pre-post analyses will be conducted to determine relevance of intervention order. By means of the N = 1 design, effectiveness of both advices will be measured at the individual level. Discussion The QFS-study is a comprehensive study exploring disrupted biological factors and patient-tailored lifestyle advices as intervention in adolescent and young adults with QFS and similar manifestations of chronic fatigue. Practical or operational issues are expected during the study, but can be overcome through innovative study design, statistical approaches, and recruitment strategies. Ultimately, the study aims to contribute to biological research and (personalized) treatment in QFS and similar manifestations of chronic fatigue. Trial registration: Trial NL8789 (www.trialregister.nl). Registered July 21, 2020.
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Philosophers, psychologists, and economists have reached the consensus that one can use two different kinds of regulation to achieve self-control. Synchronic regulation uses willpower to resist current temptation. Diachronic regulation implements a plan to avoid future temptation. Yet this consensus may rest on contaminated intuitions. Specifically, agents typically use willpower (synchronic regulation) to achieve their plans to avoid temptation (diachronic regulation). So even if cases of diachronic regulation seem to involve self-control, this may be because they are contaminated by synchronic regulation. We therefore developed a novel multifactorial method to disentangle synchronic and diachronic regulation. Using this method, we find that ordinary usage assumes that only synchronic––not diachronic––regulation counts as self-control. We find this pattern across four experiments involving different kinds of temptation, as well as a paradigmatic case of diachronic regulation based on the classic story of Odysseus and the Sirens. Our final experiment finds that self-control in a diachronic case depends on whether the agent uses synchronic regulation at two moments: when she (1) initiates and (2) follows-through on a plan to resist temptation. Taken together, our results strongly suggest that synchronic regulation is the sole difference maker in the folk concept of self-control.
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This article synthesizes recent findings on antecedents of healthy eating. We discuss consumer-related and environment-related forces that influence consumers’ healthy food choices and emphasize the duality of these forces so that they can facilitate but also prevent healthy eating. Specifically, our review documents how consumer lay beliefs, goals, and habits shape eating patterns. We further document the impact of environment-related forces on healthy consumption—focusing on intervention strategies and environmental changes (i.e., the trend towards online retail channels). Finally, we discuss three salient tensions (i.e., an innate craving for unhealthy food, a focus on single decisions, and a selective focus on self-control dilemmas) that emerge when taking a holistic view on existing research.
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Incentives have dual effects in consumer settings: The benefits on deliberate consumer purchase and performance are well known. But the detrimental effects on habit performance are less recognized. In the present research, we traced these dual outcomes to consumers’ lay theories about action control when incentivized. An initial study demonstrated that, when given incentives, consumers believed thoughtful and effortful action control strategies were better than relying on habit, despite strong evidence that relying on habit would have been successful. We then tested the effects of incentives on habits in an experimental task. When the task was learned using conscious rules, incentives had the well-known effect of benefitting performance. However, when the task was learned habitually, incentives impeded performance. Participants ended up overriding habits that had been successful in the past. We discuss these dual effects of incentives for managing repeated patronage and its implications for attitudinal versus habitual loyalty.
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Recent research has shown that perceptual processing of stimuli previously associated with high-value rewards is automatically prioritized, even when rewards are no longer available. It has been hypothesized that such reward-related modulation of stimulus salience is conceptually similar to an 'attentional habit'. Recording event-related potentials in humans during a reinforcement learning task, we show strong evidence in favor of this hypothesis. Resistance to outcome devaluation (the defining feature of a habit) was shown by the stimulus-locked P1 component, reflecting activity in the extrastriate visual cortex. Analysis at longer latencies revealed a positive component (corresponding to the P3b, from 550 to 700ms) sensitive to outcome devaluation. Thus, distinct spatio-temporal patterns of brain activity were observed corresponding to habitual and goal-directed processes. These results demonstrate that reinforcement learning engages both attentional habits and goal-directed processes in parallel. Consequences for brain and computational models of reinforcement learning are discussed.Significance statementThe human attentional network adapts in order to detect stimuli that predict important rewards. A recent hypothesis suggests that the visual cortex automatically prioritizes reward-related stimuli, driven by cached representations of reward value -i.e., Stimulus-Response habits. Alternatively the neural system may track the current value of the predicted outcome. Our results demonstrate for the first time that visual cortex activity is increased for reward-related stimuli even when the rewarding event is temporarily devalued. In contrast, longer latency brain activity was specifically sensitive to transient changes in reward value. Therefore, we show that both habit-like attention and goal directed processes occur in the same learning episode at different latencies. This result has important consequences for computational models of reinforcement learning.
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A recent study suggests that habits play a mediating role in the association between trait self-control and eating behavior, supporting a notion of effortless processes in trait self-control (Adriaanse et al., 2014). We conceptually replicated this research in the area of exercise behavior, hypothesizing that these associations would generalize to other self-control related behaviors. Sufficient exercise is essential for several health and well-being outcomes, and therefore many people intend to exercise. However, the majority of the population does not actually exercise to a sufficient or intended extent, due to competing temptations and short-term goals. This conflict makes exercise a typical example of a self-control dilemma. A within-subjects survey study was conducted to test associations between trait self-control, habit strength, and exercise behavior. Participants were recruited at a local gym. Results demonstrated that trait self-control predicted exercise behavior. Mediation analysis revealed that the association between self-control and exercise was mediated by stronger exercise habits, replicating findings by Adriaanse et al. (2014). These results highlight the relevance of self-control in the domain of exercise. In addition, they add to a growing body of knowledge on the underlying mechanisms of trait self-control on behavior that point to habit—rather than effortful impulse inhibition—as a potential key to self-control success.
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We present evidence that a significant fraction of commuters on the London underground do not travel on their optimal route. We show that a strike on the underground, which forced many commuters to experiment with new routes, brought lasting changes in behavior. This effect is stronger for commuters who live in areas where the underground map is more distorted, which points to the importance of informational imperfections. Information resulting from the strike improved network efficiency. Search costs alone are unlikely to explain the suboptimal behavior. JEL-classification: D83, L91, R41 Key words: Experimentation, Learning, Habit, Optimization, Search
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Self-control is typically viewed as a key ingredient responsible for effective self-regulation and personal goal attainment. This study used experience sampling, daily diary, and prospective data collection to investigate the immediate and semester-long consequences of effortful self-control and temptations on depletion and goal attainment. Results showed that goal attainment was influenced by experiences of temptations rather than by actively resisting or controlling those temptations. This study also found that simply experiencing temptations led people to feel depleted. Depletion in turn mediated the link between temptations and goal attainment, such that people who experienced increased temptations felt more depleted and thus less likely to achieve their goals. Critically, results of Bayesian analyses strongly indicate that effortful self-control was consistently unrelated to goal attainment throughout all analyses.
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This article presents a comprehensive survey of research concerning interactions between associative learning and attention in humans. Four main findings are described. First, attention is biased toward stimuli that predict their consequences reliably (). This finding is consistent with the approach taken by Mackintosh (1975) in his attentional model of associative learning in nonhuman animals. Second, the strength of this attentional bias is modulated by the value of the outcome (). That is, predictors of high-value outcomes receive especially high levels of attention. Third, the related but opposing idea that may result in increased attention to stimuli (Pearce & Hall, 1980), receives less support. This suggests that hybrid models of associative learning, incorporating the mechanisms of both the Mackintosh and Pearce-Hall theories, may not be required to explain data from human participants. Rather, a simpler model, in which attention to stimuli is determined by how strongly they are associated with significant outcomes, goes a long way to account for the data on human attentional learning. The last main finding, and an exciting area for future research and theorizing, is that and modulate both deliberate attentional focus, and more automatic attentional capture. The automatic influence of learning on attention does not appear to fit the traditional view of attention as being either or . Rather, it suggests a new kind of "derived" attention. (PsycINFO Database Record
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Habit formation apps are intended to help instigate and maintain new behaviors. Prior research has established that these apps mostly do not support the theoretical ‘habit’ construct defined in psychology, yet are generally popular and well reviewed in app stores. This apparent mismatch between theory and ‘in-the-wild’ usage has not been investigated to date. Through an in-depth qualitative study of a popular application Lift, this research establishes that common techniques such as reminders and streaks are effective at supporting repetition of new behaviors, but at the same time create a dependency: on-going app use is often required to achieve lasting change. This dependency introduces fragility in users’ attempts to change their behavior, as they often abandon the app and subsequently disengage with their new behaviors.
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A growing body of research indicates that self-control is critical to academic success. Surprisingly little is known, however, about the diverse strategies students use to implement self-control or how well these strategies work. To address these issues, we conducted a naturalistic investigation of self-control strategies (Study 1) and two field experiments (Studies 2 and 3). In Study 1, high school students described the strategies they use to manage interpersonal conflicts, get academic work done, eat healthfully, and manage other everyday self-control challenges. The majority of strategies in these self-nominated incidents as well as in three hypothetical academic scenarios (e.g., studying instead of texting friends) were reliably classified using the process model of self-control. As predicted by the process model, students rated strategies deployed early in the impulse-generation process (situation selection, situation modification) as being dramatically more effective than strategies deployed later (attentional deployment, cognitive change, response modulation). In Study 2, high school students randomly assigned to implement situation modification were more likely to meet their academic goals during the following week than students assigned either to implement response modulation or no strategy at all. In Study 3, college students randomly assigned to implement situation modification were also more successful in meeting their academic goals, and this effect was partially mediated by decreased feelings of temptation throughout the week. Collectively, these findings suggest that students might benefit from learning to initiate self-control when their impulses are still nascent.
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Exercising self-control is often difficult, whether declining a drink in order to drive home safely, passing on the chocolate cake to stay on a diet, or ignoring text messages to finish reading an important paper. But enacting self-control is not always difficult, particularly when it takes the form of proactively choosing or changing situations in ways that weaken undesirable impulses or potentiate desirable ones. Examples of situational self-control include the partygoer who chooses a seat far from where drinks are being poured, the dieter who asks the waiter not to bring around the dessert cart, and the student who goes to the library without a cell phone. Using the process model of self-control, we argue that the full range of self-control strategies can be organized by considering the timeline of the developing tempting impulse. Because impulses tend to grow stronger over time, situational self-control strategies—which can nip a tempting impulse in the bud—may be especially effective in preventing undesirable action. Ironically, we may underappreciate situational self-control for the same reason it is so effective—namely, that by manipulating our circumstances to advantage, we are often able to minimize the in-the-moment experience of intrapsychic struggle typically associated with exercising self-control.