The seven articles in this issue, and the accompanying meta-analysis in Health Psychology Review [McEachan, R.R.C., Conner, M., Taylor, N., & Lawton, R.J. (2011). Prospective prediction of health-related behaviors with the theory of planned behavior: A meta-analysis. Health Psychology Review, 5, 97-144], illustrate the wide application of the theory of planned behaviour [Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179-211] in the health domain. In this editorial, Ajzen reflects on some of the issues raised by the different authors. Among the topics addressed are the nature of intentions and the limits of predictive validity; rationality, affect and emotions; past behaviour and habit; the prototype/willingness model; and the role of such background factors as the big five personality traits and social comparison tendency.

To be able to curb the global pandemic of physical inactivity and the associated 5.3 million deaths per year, we need to understand the basic principles that govern physical activity. However, there is a lack of large-scale measurements of physical activity patterns across free-living populations worldwide. Here we leverage the wide usage of smartphones with built-in accelerometry to measure physical activity at the global scale. We study a dataset consisting of 68 million days of physical activity for 717,527 people, giving us a window into activity in 111 countries across the globe. We find inequality in how activity is distributed within countries and that this inequality is a better predictor of obesity prevalence in the population than average activity volume. Reduced activity in females contributes to a large portion of the observed activity inequality. Aspects of the built environment, such as the walkability of a city, are associated with a smaller gender gap in activity and lower activity inequality. In more walkable cities, activity is greater throughout the day and throughout the week, across age, gender, and body mass index (BMI) groups, with the greatest increases in activity found for females. Our findings have implications for global public health policy and urban planning and highlight the role of activity inequality and the built environment in improving physical activity and health.

The goals of this study were to test whether exercise-related stimuli can elicit automatic evaluative responses and whether automatic evaluations reflect exercise setting preference in highly active exercisers. An adapted version of the Affect Misattribution Procedure was employed. Seventy-two highly active exercisers (26 years +/- 9.03; 43% female) were subliminally primed (7 ms) with pictures depicting typical fitness center scenarios or gray rectangles (control primes). After each prime, participants consciously evaluated the

This multistudy report used an experimental approach to alter automatic evaluations of exercise (AEE). First, we investigated the plasticity of AEE (study 1). A computerized evaluative conditioning task was developed that altered the AEE of participants in two experimental groups (acquisition of positive/negative associations involving exercising) and a control group (eta2 part. = .11). Second, we examined connections between changes in AEE and subsequent exercise behavior (chosen intensity on a bike ergometer; study 2) in individuals that were placed in groups according to their baseline AEE. Group differences in exercise behavior were detected (eta2 part. = .29). The effect was driven by the performance of the group with preexisting negative AEE that acquired more positive associations. This illustrates the effect of altered AEE on subsequent exercise behavior and the potential of AEE as a target for exercise intervention.

Models employed in exercise psychology highlight the role of reflective processes for explaining behavior change. However, as discussed in social cognition literature, information-processing models also consider automatic processes (dual-process models). To examine the relevance of automatic processing in exercise psychology, we used a priming task to assess the automatic evaluations of exercise stimuli in physically active sport and exercise majors (n = 32), physically active nonsport majors (n = 31), and inactive students (n = 31). Results showed that physically active students responded faster to positive words after exercise primes, whereas inactive students responded more rapidly to negative words. Priming task reaction times were successfully used to predict reported amounts of exercise in an ordinal regression model. Findings were obtained only with experiential items reflecting negative and positive consequences of exercise. The results illustrate the potential importance of dual-process models in exercise psychology.

Research on human and animal behavior has long emphasized its hierarchical structure-the divisibility of ongoing behavior into discrete tasks, which are comprised of subtask sequences, which in turn are built of simple actions. The hierarchical structure of behavior has also been of enduring interest within neuroscience, where it has been widely considered to reflect prefrontal cortical functions. In this paper, we reexamine behavioral hierarchy and its neural substrates from the point of view of recent developments in computational reinforcement learning. Specifically, we consider a set of approaches known collectively as hierarchical reinforcement learning, which extend the reinforcement learning paradigm by allowing the learning agent to aggregate actions into reusable subroutines or skills. A close look at the components of hierarchical reinforcement learning suggests how they might map onto neural structures, in particular regions within the dorsolateral and orbital prefrontal cortex. It also suggests specific ways in which hierarchical reinforcement learning might provide a complement to existing psychological models of hierarchically structured behavior. A particularly important question that hierarchical reinforcement learning brings to the fore is that of how learning identifies new action routines that are likely to provide useful building blocks in solving a wide range of future problems. Here and at many other points, hierarchical reinforcement learning offers an appealing framework for investigating the computational and neural underpinnings of hierarchically structured behavior.

Sometimes our automatic evaluations do not correspond well with those we can reflect on and articulate. We present a novel approach to the assessment of automatic and reflective affective evaluations of exercising. Based on the assumptions of the associative-propositional processes in evaluation model, we measured participants' automatic evaluations of exercise and then shared this information with them, asked them to reflect on it and rate eventual discrepancy between their reflective evaluation and the assessment of their automatic evaluation. We found that mismatch between self-reported ideal exercise frequency and actual exercise frequency over the previous 14 weeks could be regressed on the discrepancy between a relatively negative automatic and a more positive reflective evaluation. This study illustrates the potential of a dual-process approach to the measurement of evaluative responses and suggests that mistrusting one's negative spontaneous reaction to exercise and asserting a very positive reflective evaluation instead leads to the adoption of inflated exercise goals.

The current study explored associations between previous physical activity and both implicit and explicit attitudes, as well as visual attention and activity motivation (intention). Analyses were performed on participants initially unaware of the physical activity focus of the study (N = 98). Higher levels of physical activity were associated with positive implicit attitudes and an attentional bias towards exercise cues. There was a quadratic ('U' shaped) relationship between implicit attitude and attention: the more extreme individuals' implicit attitudes towards exercise (positive or negative) the greater their attentional bias to exercise cues. Furthermore, explicit attitude moderated the relationship between attentional bias and physical activity: attentional bias to exercise cues was associated with higher levels of physical activity only for those who had a strong positive explicit attitude. Findings suggested that implicit cognitions are linked with previous physical activity. Future research should consider strategies for strengthening positive implicit and explicit attitudes and directing attention to cues signalling healthy behaviour.

Obesity can be prevented by the combined adoption of a regular physical activity (PA) and healthy eating behaviors (EB). Researchers mainly focused on socio-cognitive models, such as the Theory of Planned Behavior (TPB), to identify the psychological antecedents of these behaviors. However, few studies were interested in testing the potential contribution of automatic processes in the prediction of PA and EB. Thus, the main objective of this study was to explore the specific role of implicit attitudes in the pattern of prediction of self-reported PA and EB in the TPB framework, among persons with obesity and in adults from the general population. One hundred and fifty-three adults participated to this cross-sectional study among which 59 obese persons (74% women, age: 50.6 +/- 12.3 years, BMI: 36.8 +/- 4.03 kg m(-)(2)) and 94 people from the general population (51% women; age: 34.7 +/- 8.9 years). Implicit attitudes toward PA and EB were estimated through two Implicit Association Tests. TPB variables, PA and EB were assessed by questionnaire. Regarding to the prediction of PA, a significant contribution of implicit attitudes emerged in obese people, beta = .25; 95%[CI: .01, .50]; P = .044, beyond the TPB variables, contrary to participants from the general population. The present study suggests that implicit attitudes play a specific role among persons with obesity regarding PA. Other studies are needed to examine which kind of psychological processes are specifically associated with PA and EB among obese people.

The mesostriatal dopamine system is prominently implicated in model-free reinforcement learning, with fMRI BOLD signals in ventral striatum notably covarying with model-free prediction errors. However, latent learning and devaluation studies show that behavior also shows hallmarks of model-based planning, and the interaction between model-based and model-free values, prediction errors, and preferences is underexplored. We designed a multistep decision task in which model-based and model-free influences on human choice behavior could be distinguished. By showing that choices reflected both influences we could then test the purity of the ventral striatal BOLD signal as a model-free report. Contrary to expectations, the signal reflected both model-free and model-based predictions in proportions matching those that best explained choice behavior. These results challenge the notion of a separate model-free learner and suggest a more integrated computational architecture for high-level human decision-making.

It is now widely accepted that instrumental actions can be either goal-directed or habitual; whereas the former are rapidly acquired and regulated by their outcome, the latter are reflexive, elicited by antecedent stimuli rather than their consequences. Model-based reinforcement learning (RL) provides an elegant description of goal-directed action. Through exposure to states, actions and rewards, the agent rapidly constructs a model of the world and can choose an appropriate action based on quite abstract changes in environmental and evaluative demands. This model is powerful but has a problem explaining the development of habitual actions. To account for habits, theorists have argued that another action controller is required, called model-free RL, that does not form a model of the world but rather caches action values within states allowing a state to select an action based on its reward history rather than its consequences. Nevertheless, there are persistent problems with important predictions from the model; most notably the failure of model-free RL correctly to predict the insensitivity of habitual actions to changes in the actionreward contingency. Here, we suggest that introducing model-free RL in instrumental conditioning is unnecessary, and demonstrate that reconceptualizing habits as action sequences allows model-based RL to be applied to both goal-directed and habitual actions in a manner consistent with what real animals do. This approach has significant implications for the way habits are currently investigated and generates new experimental predictions.

Behavioral evidence suggests that instrumental conditioning is governed by two forms of action control: a goal-directed and a habit learning process. Model-based reinforcement learning (RL) has been argued to underlie the goal-directed process; however, the way in which it interacts with habits and the structure of the habitual process has remained unclear. According to a flat architecture, the habitual process corresponds to model-free RL, and its interaction with the goal-directed process is coordinated by an external arbitration mechanism. Alternatively, the interaction between these systems has recently been argued to be hierarchical, such that the formation of action sequences underlies habit learning and a goal-directed process selects between goal-directed actions and habitual sequences of actions to reach the goal. Here we used a two-stage decision-making task to test predictions from these accounts. The hierarchical account predicts that, because they are tied to each other as an action sequence, selecting a habitual action in the first stage will be followed by a habitual action in the second stage, whereas the flat account predicts that the statuses of the first and second stage actions are independent of each other. We found, based on subjects' choices and reaction times, that human subjects combined single actions to build action sequences and that the formation of such action sequences was sufficient to explain habitual actions. Furthermore, based on Bayesian model comparison, a family of hierarchical RL models, assuming a hierarchical interaction between habit and goal-directed processes, provided a better fit of the subjects' behavior than a family of flat models. Although these findings do not rule out all possible model-free accounts of instrumental conditioning, they do show such accounts are not necessary to explain habitual actions and provide a new basis for understanding how goal-directed and habitual action control interact.

Exercise psychology has yet to produce intervention methods capable of increasing exercise and physical activity behavior in a sustainable manner. This situation is forcing a critical reevaluation of current conceptual models, especially the assumption that behavioral decisions are driven solely by the rational evaluation of information. Like other behavioral sciences, exercise psychology is transitioning to dual-process models that acknowledge the importance of non-reflective processes. Emerging evidence suggests that the pleasure or displeasure experienced during exercise may influence subsequent physical activity. These data raise the possibility of inactivity resulting from a conflict between positively evaluated information on health benefits and unpleasant affective experiences. Thus, researchers must devise methods to make exercise and physical activity more pleasant and enjoyable across the lifespan.

Dual-process and dual-system theories in both cognitive and social psychology have been subjected to a number of recently published criticisms. However, they have been attacked as a category, incorrectly assuming there is a generic version that applies to all. We identify and respond to 5 main lines of argument made by such critics. We agree that some of these arguments have force against some of the theories in the literature but believe them to be overstated. We argue that the dual-processing distinction is supported by much recent evidence in cognitive science. Our preferred theoretical approach is one in which rapid autonomous processes (Type 1) are assumed to yield default responses unless intervened on by distinctive higher order reasoning processes (Type 2). What defines the difference is that Type 2 processing supports hypothetical thinking and load heavily on working memory.

OBJECTIVES: 'Habit' is a process whereby situational cues generate behaviour automatically, via activation of learned cue-behaviour associations. This article presents a conceptual and empirical rationale for distinguishing between two manifestations of habit in health behaviour, triggering selection and initiation of an action ('habitual instigation'), or automating progression through subactions required to complete action ('habitual execution'). We propose that habitual instigation accounts for habit-action relationships, and is the manifestation captured by the Self-Report Habit Index (SRHI), the dominant measure in health psychology. DESIGN: Conceptual analysis and prospective survey. METHODS: Student participants (N = 229) completed measures of intentions, the original, non-specific SRHI, an instigation-specific SRHI variant, an execution-specific variant, and, 1 week later, behaviour, in three health domains (flossing, snacking, and breakfast consumption). Effects of habitual instigation and execution on behaviour were modelled using regression analyses, with simple slope analysis to test habit-intention interactions. Relationships between instigation, execution, and non-specific SRHI variants were assessed via correlations and factor analyses. RESULTS: The instigation-SRHI was uniformly more predictive of behaviour frequency than the execution-SRHI and corresponded more closely with the original SRHI in correlation and factor analyses. CONCLUSIONS: Further, experimental work is needed to separate the impact of the two habit manifestations more rigorously. Nonetheless, findings qualify calls for habit-based interventions by suggesting that behaviour maintenance may be better served by habitual instigation and that disrupting habitual behaviour may depend on overriding habits of instigation. Greater precision of measurement may help to minimize confusion between habitual instigation and execution. Statement of contribution What is already known on this subject? Habit is often used to understand, explain, and change health behaviour. Making behaviour habitual has been proposed as a means of maintaining behaviour change. Concerns have been raised about the extent to which health behaviour can be habitual. What does this study add? A conceptual and empirical rationale for discerning habitually instigated and habitually executed behaviour. Results show habit-behaviour effects are mostly attributable to habitual instigation, not execution. The most common habit measure, the Self-Report Habit Index, measures habitual instigation, not execution.

Scientists in many different fields have been attracted to the study of habits because of the power habits have over behavior and because they invoke a dichotomy between the conscious, voluntary control over behavior, considered the essence of higher-order deliberative behavioral control, and lower-order behavioral control that is scarcely available to consciousness. A broad spectrum of behavioral routines and rituals can become habitual and stereotyped through learning. Others have a strong innate basis. Repetitive behaviors can also appear as cardinal symptoms in a broad range of neurological and neuropsychiatric illness and in addictive states. This review suggests that many of these behaviors could emerge as a result of experience-dependent plasticity in basal ganglia-based circuits that can influence not only overt behaviors but also cognitive activity. Culturally based rituals may reflect privileged interactions between the basal ganglia and cortically based circuits that influence social, emotional, and action functions of the brain.

OBJECTIVES: The primary purpose of this study was to investigate how habit strength in a preparatory and performance phase predicts exercise while accounting for intention. The secondary purpose was to determine the strength of potential habit antecedents (affective judgement, perceived behavioural control, consistency, and cues) in both exercise phases. DESIGN: This was a prospective study with measures collected at baseline and week 6. METHODS: Participants (n = 181) were a sample of adults (18-65) recruited across nine gyms and recreation centres who completed baseline and follow-up questionnaires after 6 weeks. RESULTS: Intention (beta = .28, p = .00) and habit preparation (beta = .20, p = .03), predicted exercise, and change of exercise with coefficients of beta = .25, (p = .00) and beta = .18, (p = .04), respectively, across 6 weeks but not habit performance (p>.05). CONCLUSIONS: This study highlighted the distinction between the two phases of exercise and the importance of preparatory habit in predicting behaviour. Focusing on a consistent preparatory routine could be helpful in establishing an exercise habit. Statement of contribution What is already known on this subject? A recent meta-analysis found habit to correlate r = .43 with behaviour (Gardner, de Bruijn, & Lally, ). Verplanken and Melkevik () propose that habit in exercise should be measured in separate components. Phillips and Gardner () interpreted this as habitual instigation (thought) to exercise and execution. What does this study add? Extended pervious work and identified two distinct behavioural phases (preparation and performance) for exercise. Habit model revealed that temporal consistency was the strongest predictor in both phases of exercise. Intention and habit of preparatory behaviour predicted exercise fluctuations in gym members.

There is accumulating neural evidence to support the existence of two distinct systems for guiding action selection, a deliberative

This study was designed to examine the moderating influence of habit strength on daily action planning effects on physical activity and sedentary behavior. A 2 by 2 design was used with experimental factors corresponding to action planning interventions for (a) engaging in physical activity and (b) limiting or interrupting sedentary behavior. At the end of each day for 1 week, university students (n = 195) completed (a) a questionnaire about their behavior during the day and behavioral intentions for the following day and (b) a planning intervention(s) corresponding to their randomly assigned experimental condition. Action planning increased physical activity in those with weak habits but decreased physical activity in those with strong habits compared with those who did not create action plans. Action planning did not impact sedentary behavior. Action planning was a useful behavior change technique for increasing physical activity in people with weak habits, but may be iatrogenic for those with strong habits.

Much of the global burden of disease is associated with behaviors--overeating, smoking, excessive alcohol consumption, and physical inactivity--that people recognize as health-harming and yet continue to engage in, even when undesired consequences emerge. To date, interventions aimed at changing such behaviors have largely encouraged people to reflect on their behaviors. These approaches are often ineffectual, which is in keeping with the observation that much human behavior is automatic, cued by environmental stimuli, resulting in actions that are largely unaccompanied by conscious reflection. We propose that interventions targeting these automatic bases of behaviors may be more effective. We discuss specific interventions and suggest ways to determine whether and how interventions that target automatic processes can enhance global efforts to prevent disease.

This meta-analysis explored the efficacy of the Theory of Planned Behaviour (TPB) dependent on behaviour and methodological moderators. A lack of hierarchical analysis in previous reviews risks confounding these moderators. Here moderating roles of behaviour type, length of follow-up, sample age and behavioural measure are explored hierarchically amongst prospective tests of the TPB, controlling for past behaviour where possible. Searching identified 237 prospective tests from 206 articles. Random-effects meta-analytic procedures were used to correcting correlations for sampling and measurement error. Behaviour type moderated the model; physical activity and diet behaviours were better predicted (23.9% and 21.2% variance explained, respectively) whilst risk, detection, safer sex and abstinence from drugs were poorly predicted (between 13.8 and 15.3% variance explained). Methodological moderators were also apparent: age of sample moderated relations with student samples better predicted for physical activity, and adolescent samples better predicted for abstinence behaviours. Behaviours assessed in the shorter term, and those assessed with self-reports (compared with objective measures) were also better predicted. Both behavioural and methodological characteristics moderated relations amongst model components. The results can aid selection of appropriate targets upon which to base interventions.

What are the psychological mechanisms that trigger habits in daily life? Two studies reveal that strong habits are influenced by context cues associated with past performance (e.g., locations) but are relatively unaffected by current goals. Specifically, performance contexts but not goals automatically triggered strongly habitual behaviors in memory (Experiment 1) and triggered overt habit performance (Experiment 2). Nonetheless, habits sometimes appear to be linked to goals because people self-perceive their habits to be guided by goals. Furthermore, habits of moderate strength are automatically influenced by goals, yielding a curvilinear, U-shaped relation between habit strength and actual goal influence. Thus, research that taps self-perceptions or moderately strong habits may find habits to be linked to goals. (C) 2011 Elsevier Inc.

BACKGROUND: Popular theories of health behavior have often been criticized for neglecting an affective component to behavioral engagement. PURPOSE: This study reviewed affective judgment (AJ) constructs employed in physical activity research to assess the relationship with behavior. Studies were eligible if they included: (a) a measure of physical activity; (b) a distinct measure of AJ (e.g., affective attitude, enjoyment, intrinsic motivation); and (c) involved participants with a mean age of 18 years or older. METHODS: Literature searches were concluded in September, 2009 among five key search engines. This search yielded a total of 10,631 potentially relevant records; of these, 102 passed the eligibility criteria. Random effects meta-analysis procedures with correction for sampling and measurement bias were employed in the analysis. RESULTS: Articles were published between 1989 and 2009, with sample sizes ranging from 15 to 6,739. Of the studies included, 82 were correlational and 20 were experimental, yielding 114 independent samples. The majority of the correlational samples reported a significant positive correlation between AJ and physical activity (83 out of 85), with a summary r of 0.42 (95% CI 0.37 to 0.46) that was invariant across the measures employed, study quality, population sampled and cultural variables. Experimental studies demonstrated that persuasive, information-based, and self-regulatory interventions failed to change AJ; by contrast, environmental and experiential interventions showed promise in their capability to influence AJ. CONCLUSIONS: The results point to a medium-effect size relationship between AJ and physical activity. Interventions that change AJ are scarce despite their potential for changing physical activity. Future experimental work designed to evaluate the causal impact ofAJ on physical activity is required.

OBJECTIVE: To provide a broad overview of the state of physical activity (PA) research in the form of (1) definitions of PA, (2) health benefits, (3) prevalence, (4) correlates and (5) interventions. DESIGN: A high-level overview of published reviews of the literature. RESULTS: Regular PA is an effective primary and secondary preventative strategy against at least 25 chronic medical conditions with risk reduction typically in the 20-30% range. While approximately 75% of adults meet recommended PA guidelines, the prevalence is slightly lower for women compared to men, and considerably lower for youth, older adults and those in higher income countries. Motivation, self-efficacy and self-regulation remain consistent correlates of PA. Interventions show PA changes in the small effect size range for adults and youth but the heterogeneity is considerable across studies. Only a few (aggregate of behavioural regulation strategies, supervision, high frequency of contact) reliable moderators of intervention success were identified across study quality, sample characteristics, theory/behaviour change techniques and delivery modes/settings. CONCLUSION: PA research should continue to examine the mechanisms causing health outcomes, the dose that can lead to clinically relevant changes in health status, the scope and validity of PA surveillance and innovative behaviour change techniques, while improving the reach and duration of PA interventions.