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A few articles for your review on nail biting and what constitutes "habits" I hopw you are able to further research on this most interesting topic. Thank you for your question. Blurry Words and Fuzzy Deeds The Attribution of Obscure Behavior Daniel T. Gilbert University of Texas at Austin Shawn E. McNulty University of Texas at Austin Traci A. Giuliano University of California, Los Angeles J. Eric Benson Department of Community and Regional Planning University of Texas at Austin ABSTRACT When people attempt to infer the existence of traits from another's behavior, they categorize the behavior, characterize the actor in trait terms, and then correct that inference with information about situational constraints. The 1st 2 stages require fewer attentional resources than does the 3rd. However, when behavior is obscure (i.e., difficult to categorize because its features are not easily apprehended), the 1st stage should consume resources on which the 3rd stage depends, and undercorrected inferences should result. In 2 experiments, behavior was made obscure by distorting its visual or acoustical parameters. Although the obscure behaviors could logically have been attributed to the constraining situations in which they occurred, Ss who observed such behaviors were especially unlikely to correct their trait characterizations of the actors. ---------------------------------------------------------------------------- ---- This research was supported by National Science Foundation Grant BNS-8819836 to Daniel T. Gilbert. We thank Olga Colon, Valerie Edwards, Greg Hixon, Josh Holohan, Cheryl Irwin, Britt Simpson, Janet Spence, Al Swinkels, and Maggie Walsh for their generous help with the production and execution of these experiments. We also thank Eliot Smith, Bill Swann, Yaacov Trope, and several anonymous reviewers for their helpful comments on a previous version of this article. Correspondence may be addressed to Daniel T. Gilbert, Department of Psychology, University of Texas, Austin, Texas, 78712. Electronic mail may be sent to firstname.lastname@example.org Received: August 20, 1990 Revised: March 11, 1991 Accepted: June 11, 1991 ---------------------------------------------------------------------------- ---- The study of behavioral perception must occasionally strike the layperson as an exercise in the obvious. After all, most of us have the experience of effortlessly reading the inner states of others from their overt behavior: Our interpretations of a knit brow, a coy smile, or an angry rebuke are drawn so quickly and easily that "typically they are not experienced as interpretations at all" ( Heider, 1958 , p. 82). Although the identification of action is constrained by the action's context (a condemned prisoner's nervous laughter rarely means "I am having a dandy time"), by the action's effects on the environment (a boxer's kidney punch rarely means "I love you"), and by the actor's dispositions (the compliments of a notorious ingratiator rarely mean anything at all), these constraints are usually less than complete and thus leave ample room for interpretation. Just what a person is doing can, under many circumstances, be a matter of debate ( Ryle, 1949 ; Taylor & Crocker, 1981 ; Vallacher & Wegner, 1985 ; cf. Baron, 1988 ; McArthur & Baron, 1983 ). A variety of factors can alter the identification of a behavior, and Trope (1986) has developed an elegant model of how these alterations influence subsequent attributional work. In essence, the model argues that the identity one assigns to an action can govern the inferences one draws about the actor: Weeping will afford different inferences about an actor when it occurs at a wedding than at a funeral, and prior knowledge of these formal occasions may even change the perceived extremity of the emotional display. In short, the answer to "Why?" is conditioned by the answer to "What?" The trait-attribution model of Gilbert, Pelham, and Krull (1988) incorporates Trope's (1986) insight by postulating three conceptually distinct stages in the process of trait inference: Categorization of the behavior, characterization of the actor in trait terms, and correction of the trait inference with information about facilitative or inhibitory situational forces (see also Quattrone, 1982 ). Thus, the utterance "Nice to see you" may be categorized as an affable statement, the speaker may be characterized as having a genuine affinity for the recipient of the remark, and, finally, this inference may be corrected for the fact that the recipient is a powerful superior whose presence demands polite affability. The key feature of this model is that the three stages are assumed to vary in the amount of conscious attention they require. Specifically, the correction stage is presumed to require more attentional resources than either the categorization or characterization stages and should thus be more easily impaired by competing cognitive demands. A variety of experiments have substantiated this prediction. For example, subjects who saw a woman behave anxiously under anxiety-provoking circumstances categorized the behavior as manifest anxiety, characterized the woman as dispositionally anxious, and then corrected this inference by taking into account the constraining circumstances of her behavior. However, when subjects were asked to perform simultaneously an additional resource-consuming task, they were much less likely to correct their characterizations. Although these "cognitively busy" subjects noticed and remembered the anxiety-provoking circumstances, they failed to use that information to institute correction and persisted in considering the woman dispositionally anxious ( Gilbert, Pelham, & Krull, 1988 ; see also Gilbert, Krull, & Pelham, 1988 ; Gilbert & Osborne, 1989 ). According to Trope's (1986) model, the categorization of an action may influence subsequent attributions by changing the information on which those attributions are predicated (e.g., perceived behavioral extremity). The Gilbert, Pelham, and Krull (1988) model suggests an additional route by which behavioral categorization may influence attribution. In previous tests of this model, extraneous tasks (e.g., digit rehearsal, visual scanning, and so on) were used to deplete perceivers' overall processing resources and, as the model predicted, this depletion led to failure at the fragile correction stage. The same logic suggests that if the relatively effortless categorization stage was somehow made more effortful, then correction could be similarly disrupted. In other words, obscure behavior (i.e., behavior that is difficult to categorize because its key features are difficult to apprehend) may pose an interpretive challenge that requires attention, and if attentional resources are used to meet this challenge, they may be unavailable for correction. Perceivers who are forced to deliberate about what an action is may have fewer attentional resources with which to ponder what the action means . Categorization, then, may influence subsequent attributions not only by altering the information on which these attributions are based (as Trope's  model predicts and research has shown) but also by siphoning off attentional resources that are necessary for the completion of attributional processing. Two experiments tested hypothesis this. Experiment 1 Method Overview Subjects watched a silent videotape of an anxious-looking female target who was having a discussion with a stranger. Half the subjects learned that the target was discussing an anxiety-provoking topic (her sexual fantasies), and the remaining subjects learned that the target was discussing a mundane topic (her ideal vacation). Half the subjects in each of these conditions watched a normal videotape (easy categorization), and the remaining subjects watched a visually degraded videotape in which the target's behavior was obscured (difficult categorization). After viewing the videotape, subjects estimated the level of the target's trait anxiety. (Most of the procedures and measures in this experiment were used previously by Gilbert, Pelham, & Krull, 1988 , and Gilbert & Osborne, 1989 .) Subjects Subjects were 34 female students at the University of Texas at Austin who participated to fulfill a requirement in their introductory psychology course. All subjects had normal, near normal, or corrected-to-normal vision. Only native speakers of English were eligible to participate. Instructions On arrival at the laboratory, subjects were greeted by a male experimenter who gave them a brief oral introduction to the experiment, provided them with complete written instructions, and then escorted each subject to a cubicle (equipped with video monitor) where she remained for the duration of the experiment. The written instructions explained that subjects would be asked to watch a short clip from a videotape of a getting-acquainted conversation that had ostensibly taken place earlier in the year. This conversation was alleged to have been part of a project on the role of discussion topics in friendship formation. Subjects were told that two unacquainted female students had been assigned to discuss 1 of 10 different topics for about 5 min and that subjects would be seeing a short (approximately 20 s) clip from that conversation. It was explained that during the getting-acquainted conversation the camera had been positioned behind one of the discussants, and thus only one of the discussants (the target) would be visible on the videotape. Manipulation of Situational Constraints Subjects were shown a list of the 10 potential discussion topics (dual career couples, scary movies, drug testing, health and nutrition, sexual fantasies, ideal vacations, humiliating accidents, student housing, women in art, and interracial dating). Subjects were told that, to protect the privacy of the discussants, the videotape would be shown without any sound, but that they would be able to tell which of the 10 topics was being discussed because the topic would appear in subtitles at the bottom of the screen. All subjects were shown a video clip in which the target appeared extremely anxious and uneasy. 1 Half the subjects were randomly assigned to the anxious topic condition. In this condition, the subtitle indicated that the target was discussing an anxiety-provoking topic (i.e., her sexual fantasies). The remaining subjects were assigned to the mundane topic condition. In this condition subjects saw precisely the same behavior that was seen by subjects in the anxious topic condition. However, in this condition, the subtitle indicated that the target was discussing a mundane topic (i.e., her ideal vacation). In the anxious topic condition, then, the target's behavioral anxiety could logically be attributed to the nature of the topic she was discussing and thus was not necessarily indicative of dispositional anxiety; in the mundane topic condition, however, the same behavior could not logically have been caused by the nature of the discussion topic and thus was an excellent indicator of dispositional anxiety. Manipulation of Behavioral Obscurity Half the subjects were randomly assigned to the normal behavior condition. Subjects in this condition were shown a high-quality videotape that was free of visual noise. The remaining subjects were assigned to the obscure behavior condition. Subjects in this condition were shown a videotape that had been visually degraded by misadjusting the tracking mechanisms on two videocassette recorders and then using those machines to make successive copies of the master tape. The final fourth generation tape had a great deal of visual noise (streaking, blurring, color dropout, and snow), which interrupted the flow of the action and obscured some of its details. It is important to note that the subtitle was inserted only after the videotape had been visually degraded; as such, the subtitles on both the normal and degraded tapes were equally legible. Dependent Measures Subjects rated the target's dispositional anxiety on four 13-point bipolar scales that were anchored at the endpoints with the phrases (a) is probably comfortable (uncomfortable) in social situations, (b) is a calm (nervous) sort of person, (c) is generally relaxed (anxious) with people, and (d) is (not) easily embarrassed. Each scale was preceded by the phrase, "When I think about how the woman in the film actually is in her day to day life, I think she..." Procedure Before the experiment began, subjects were shown a silent, 20-s, visually normal practice tape of a woman (not the target) sitting in a room, and they were then given an opportunity to rate that woman on the trait-anxiety measures. This enabled subjects to become familiar with both the procedure and the dependent measures. After the practice tape was over, the experimenter started the experimental tape. Subjects watched a 20-s clip of an anxious-looking woman (the target) who was apparently engaged in conversation with an off-screen partner. When the tape ended, subjects were allowed up to 30 s to rate the target on the four trait-anxiety measures. At the end of the experiment all subjects were asked to recall the discussion topic. Finally, subjects were debriefed and dismissed. Results and Discussion We expected that subjects who viewed obscure behavior would make correspondent inferences about the target and that subjects who viewed normal behavior would not. Ratings on the four trait-anxiety scales were averaged to create a trait-anxiety index. The omission of the embarrassment item improved the reliability of this index (coefficient a changed from .79 to .84), and thus the item was deleted. The three-item index was submitted to a 2 (topic: anxiety-provoking or mundane) * 2 (behavior quality: normal or obscure) analysis of variance (ANOVA), which revealed a main effect of topic, F (1, 30) = 6.95, p < .01, and a main effect of behavior quality, F (1, 30) = 7.69, p < .01. These main effects were qualified by the predicted Topic * Behavior Quality interaction, F (1, 30) = 6.30, p < .05. As Table 1 shows, subjects who saw normal behavior considered the anxious-looking target to be more trait-anxious when she was discussing a mundane rather than an anxiety-provoking topic. In contrast, subjects who saw obscure behavior considered the target to be equally trait-anxious regardless of the topic she discussed. In short, subjects who should have found the target's behavior easy to categorize showed evidence of having used the discussion topics to correct their characterizations of the target, whereas subjects who should have found the behavior difficult to categorize did not. Our own viewing of the degraded videotape left us confident that the target's behavioral anxiety was obscure but, with a bit of effort, detectable. Still, one might wonder whether subjects who saw that videotape could actually detect the anxious behavior at all. The magnitude of the ratings leaves little doubt that subjects did ultimately observe considerable state anxiety. Subjects who saw a degraded videotape rated the target as very anxious (i.e., well above the midpoint of the scale) and, in fact, made ratings that were indistinguishable from the ratings of subjects who saw a normal videotape that was subtitled with a mundane topic. In addition, all subjects recalled the discussion topic that the target had ostensibly been discussing, indicating that they had noticed and remembered this information, too. Experiment 2 The results of Experiment 1 suggest that when an action cannot be categorized unthinkingly, perceivers will devote attentional resources to the task and thus have fewer resources with which to correct their characterizations of the actor. As provocative as this initial finding may be, it is far from conclusive. First, because the target's behavior was held constant and the presence of situational constraints was manipulated, we predicted no difference between the attributions of the two groups of subjects who viewed obscure behavior. There are, of course, always several reasons why two experimental conditions might yield equivalent data, and a bolder procedure entails manipulating the nature of the target's behavior and predicting a larger difference for subjects exposed to obscure rather than normal versions of that behavior. In Experiment 2 we did just that. Second, Experiment 1 provided no evidence to indicate which stage of processing (the initial categorization of the behavior or the subsequent attributional correction) was affected by behavioral obscurity. In the second part of Experiment 2, we confronted this issue directly. Finally, we hoped to show that the phenomenon demonstrated in Experiment 1 transcended experimental particulars; as such, Experiment 2 used verbal (rather than nonverbal) behavior and examined attributions of attitude (rather than emotion). Method Overview Female subjects listened to an audio recording of a "dating game" in which a male contestant answered "loaded questions" posed by a female questioner. The wording of the woman's questions clearly indicated that she preferred men who had either a traditional or modern sex role orientation, and the man always made claims that were consistent with the woman's preference. Half the subjects in each of these conditions listened to a normal version of the tape, and the remaining subjects listened to an acoustically degraded version. All subjects then attempted to estimate the male contestant's true sex role attitudes. Subjects Seventy-eight female students at the University of Texas at Austin participated to fulfill a requirement in their introductory psychology course. Only native speakers of English were eligible to participate. Instructions On arrival at the laboratory, subjects were greeted by a female experimenter who gave them a brief oral introduction to the experiment, provided them with complete written instructions, and then escorted each subject to a cubicle equipped with headphones that were connected to a tape player in another room. The instructions explained that the experimenter was interested in "the process by which people form impressions of others-in particular, how women form impressions of men." Subjects were told that they would listen to a brief audio recording of two college students who were playing "the dating game." Ostensibly, this recording had been made as part of an earlier experiment in which male students had attempted to win a date with an attractive female student. Subjects were told that their task was to listen to an excerpt from this dating game and then make judgments about the male contestant's true attitudes toward women and their roles in society. Subjects were reminded that "male contestants are often willing to bend the truth a little bit in order to get a date with an attractive female." Manipulation of Target's Behavior Subjects were randomly assigned to one of two target-behavior conditions. In the traditional condition, subjects heard the woman ask questions in which she expressed a strong preference for men with traditional sex role orientations (e.g., "I like it when men come after me, and not the other way around. Who do you think should do the asking out?"), and they then heard the man indicate that he possessed such an orientation (e.g., "I like to be the one to ask a girl out"). In the modern condition, the woman's questions betrayed a clear preference for men with modern sex role orientations (e.g., "When I see something or someone I want, I go after it. Who do you think should do the asking out?"), and the man then indicated that he possessed such an orientation (e.g., "I admire a woman who's not afraid to make the first move"). In both conditions, then, a male target made self-descriptive claims that were entirely consistent with the constraints of the leading questions he was being asked (see Ginzel, Jones, & Swann, 1987 ; Swann, Giuliano, & Wegner, 1982 ). Manipulation of Behavioral Obscurity Half the subjects were randomly assigned to the normal behavior condition. These subjects listened to a recording of the dating game that was played at a fairly high volume so that the recording was extremely easy to hear. The remaining subjects were assigned to the obscure behavior condition. These subjects listened to a recording in which the man's constrained responses (but not the woman's constraining questions) were acoustically degraded by lowering the volume to a just audible level. In the normal behavior condition, one could not help but clearly hear the man's responses, but in the obscure behavior condition, one had to concentrate rather intensely to hear precisely what he said. Subjects in the obscure behavior condition were asked to forgive the fact that a short circuit in the man's microphone had caused the poor recording of his responses. Dependent Measures Global trait measures. Subjects rated the male target on three 13-point bipolar scales that were preceded by the phrase "I think the male contestant has ..." and were anchored at the endpoints with the phrases (a) very traditional (progressive) attitudes toward women, (b) very conservative (liberal) attitudes about relationships, and (c) very old-fashioned (modern) attitudes about dating. These scales were intended to assess subjects' global impressions of the male target's sex role orientation. Specific trait measures. Subjects rated the male target on 10 items taken from the Marital Relations (Male Form) Subscale of the Male-Female Relations Questionnaire (MFRQ; Spence, Helmreich, & Sawin, 1980 ). The MFRQ requires subjects to respond to each item on a 5-point bipolar scale that is anchored at the endpoints with the phrases strongly disagree and strongly agree. Subjects estimated the male target's agreement with 10 statements that were chosen for their face validity. All of the items were phrased such that agreement indicated a traditional sex role orientation (e.g. "I would expect to be head of the house simply because I'm a man"). These items were intended to assess subjects' specific impressions of the male target's sex role orientation. Experimental checks. Three experimental checks were made at the end of the session. First, subjects were asked whether they had normal, slightly impaired, or highly impaired hearing. Second, subjects were asked to report how difficult it had been for them to hear the audiotape on a 4-point Likert-type scale that ranged from not at all difficult to very difficult. Third, subjects were shown two of the woman's questions and were asked to recognize the gist of the man's response. So, for example, subjects read "When the female asked 'Who do you think should do the asking out, men or women?' the male said that (a) men should do the asking or (b) women should do the asking." Subjects were asked to choose one of these responses or to state that they did not know which of these responses was correct. This measure ensured that only subjects who had heard and understood the audiotape would be included in the analyses. Procedure Subjects listened to an audiotape in which a male contestant was subtly urged to claim either a modern or traditional sex role orientation. The audiotape was either acoustically normal or degraded. Subjects then completed the global trait measures (three trait scales), the specific trait measures (10 MFRQ items), and the experimental checks. Finally, subjects were probed for suspicion, debriefed, and dismissed. 2 Results and Discussion Experimental Checks All subjects reported having normal or near normal hearing. However, given routine variation in the auditory acuity of subjects, we expected that some subjects in the obscure behavior condition might actually fail to hear the audiotape altogether and thus respond randomly when presented with the dependent measures. In fact, 13 subjects failed to recall the gist of the male target's verbal responses (i.e., they failed an extremely easy two-item recognition memory test), and these subjects were excluded from all analyses. Although inclusion of such subjects adds random noise to the data and thus decreases the significance levels of the effects, it has virtually no effect on the pattern of results. Finally, a 2 (target's response: traditional or modern) * 2 (behavior quality: normal or obscure) ANOVA revealed that subjects in the normal behavior condition found the audiotapes much easier to hear than did subjects in the obscure behavior condition ( M s = 0.03 and 2.12, respectively on a 0- to 3-point scale), F (1, 61) = 271.92, p < .001. Global Trait Measures The three trait scales were averaged to create a global sex role orientation index, and this index was submitted to ANOVA. The analysis revealed a main effect of target's response, F (1, 61) = 4.06, p < .05, and the expected Target's response * Behavior Quality interaction, F (1, 61) = 3.14, p = .08 (means are shown in the top portion of Table 2 ). Planned comparisons revealed that subjects who heard obscure behavior made correspondent inferences about the target, t (61) = 2.68, p < .01, whereas subjects who heard normal behavior did not, t < 1. In other words, subjects who were forced to devote considerable attention to categorizing the target's behavior as traditional or modern were especially unlikely to correct their corresponding characterizations of the target's attitude. Indeed, there was a significant positive correlation between difficulty of categorization (i.e., answers to the question "How difficult was it to hear the tape?") and the tendency to draw correspondent inferences about the target, r (63) = .20, p = .05. This correlation is especially impressive because difficulty of categorization was only roughly assessed by self-report, and people are known to be quite poor at reporting accurately on such mental operations (e.g., Jacoby, Kelley, & Dywan, 1989 ; Nisbett & Wilson, 1977 ). It is worth noting that in both this and the previous experiment, subjects who observed normal behavior showed no evidence of correspondence bias. This may surprise those who consider the correspondence bias an unrelenting phenomenon, but, in fact, the bias has failed to appear in any number of classic studies. For example, Jones, Davis, and Gergen (1961) asked subjects to make inferences about a job applicant who claimed to have the temperament for a desirable job, and subjects showed no evidence of correspondence bias. Ross, Amabile, and Steinmetz's (1977) well-known experiment is often cited as an example of correspondence bias, but a close reading shows that quizmasters showed no such effect. Indeed, it is now clear that subjects can be expected to show little or no bias when they are held accountable for their inferences ( Tetlock, 1985 ), when they discuss their judgments with others ( Wright & Wells, 1985 ), when a familiar target is highly motivated to convey his or her true dispositions ( Fleming, Darley, Hilton, & Kojetin, 1990 ), when subjects attend primarily to the nonverbal behavior of a "leaky" target ( Gilbert & Krull, 1988 ), when the target is distinctly unenthusiastic ( Jones, Worchel, Goethals, & Grumet, 1971 ), and when the dependent measures stray too far from the target's behavior ( Cantor, Pittman, & Jones, 1982 ; Sumpton & Gregson, 1981 ). Laboratory experimentation is, of course, meant to demonstrate possibilities and not to provide actuarial information; thus, one simply cannot say how often the correspondence bias emerges in day-to-day life and how often it is attenuated by other factors. What is known is that there are a number of variables that promote and inhibit the bias, and knowledge of these variables is critical to an understanding of the bias itself. Specific Trait Measures Subjects' ratings of the target on the 10 MFRQ items were averaged to create a specific sex role orientation index. This 10-item index was submitted to ANOVA, which revealed only the expected Target's Response * Behavior Quality interaction, F (1, 61) = 8.50, p < .01. As the lower portion of Table 2 shows, subjects who heard obscure behavior made correspondent inferences about the target, t (61) = 1.96, p = .05, whereas subjects who heard normal behavior did not. 3 Which Stage of Processing Was Affected? The results of Experiment 2 are consistent with the hypothesis that difficult categorization can impair subsequent attributional correction; however, they are also consistent with the alternative hypothesis that difficult categorization affects the categorization stage rather than the subsequent correction stage of processing. It is possible, for example, that behavioral obscurity simply caused subjects to perceive the target's responses as extreme (i.e., especially liberal or conservative). As such, subjects who observed obscure behavior would be expected to make more extreme attributions about the target even if they did perform adequate attributional correction, simply because their initial perceptions of the behavior were "perceptually inflated" (see Trope, 1986 ; Trope, Cohen, & Maoz, 1988 ). To rule out this alternative hypothesis, we asked 54 female subjects to participate in a replication of Experiment 2. However, this time we told the subjects that the male target was a paid actor and that their job was simply to determine how well he played his part. In particular, subjects were asked to rate the target's behavior, rather than his disposition, on three global dimensions (traditional/progressive, conservative/liberal, and old-fashioned/modern). The results of this follow-up experiment strongly supported our original (and not the alternative) hypothesis. Subjects considered the obscure behavior much more difficult to hear than the normal behavior, M s = 2.18 and 0.00, respectively; F (1, 44) = 188.88, p < .001. Nine of the 54 subjects showed imperfect memory for the target's behavior and were thus excluded from all analyses. (It is comforting to note that this was precisely the same proportion as failed the memory test in Experiment 2.) The remaining subjects' ratings on the three scales were highly reliable (coefficient a = .88) and were thus averaged to create a behavioral perception index. An ANOVA on this index revealed only a main effect of target's response: Subjects thought that the modern target made much more progressive, liberal, and modern statements than did the traditional target, M s = 6.46 and 8.71, respectively; F (1, 41) = 14.61, p < .001. Behavior quality had absolutely no main or interactive effects on these judgments (both F s < 1). In addition, self-reported difficulty during categorization was completely uncorrelated with the perceived extremity of the behavior, r (43) = .07, ns. In short, behavioral obscurity affected subjects' attributions about the target but did not affect their perceptions of the target's behavior. This finding should not be seen as undermining the validity of Trope's (1986) model. That model suggests that when behavior is ambiguous, information about situational constraints will be used in resolving this ambiguity; thus, knowing that tears (ambiguous behavioral features) are being shed at a wedding (happy situation) rather than at a funeral (sad situation) may cause perceivers to identify the behavior as especially joyous. Why, then, didn't knowledge of situational constraints (e.g., the modern woman's leading questions) affect our subjects' perceptions of the male target's responses (e.g., cause them to perceive his responses as especially liberal)? The key, we believe, lies in the difference between ambiguous and obscure behavior. Ambiguous behavior is behavior in which clearly identifiable indicators (e.g., tears) may signal more than one inner state (e.g., joy or sadness). Obscure behavior, on the other hand, is behavior in which indicators are difficult to identify ("What's she doing with her hands?"), but once identified ("Oh. I see. She's biting her nails") can be reasonably interpreted to indicate only one inner state ("She must be nervous"). In short, behavioral ambiguity and obscurity are not the same thing, and thus Trope's model does not (in our view) make predictions that run counter to our findings. General Discussion People are rarely stumped by the question "What is that fellow doing?" Actions generally speak for themselves, and most of us are able to categorize the behaviors of others with an ease that may occasionally belie the intricate nature of that process. Yet, one need only miss a key phrase in a colleague's talk, or look away from a film at a critical moment, to be reminded of how fragile the understanding of action can be. When a momentary distraction causes that understanding to collapse, conscious attention must be called on to guide its reconstruction. The present studies demonstrate some of the consequences of calling on conscious attention in this way. When attention is devoted to the categorization of behavior, it cannot fuel the effortful components of the attributional analysis. As such, only the relatively effortless components of that analysis are completed, and strong dispositional characterizations are the result. In short, the contemplation of what may undermine the discovery of why. Rather than reiterate the theoretical implications of this finding, we will close by discussing some traditional questions on which it may have some bearing. Understanding the Unfamiliar When individuals interact with members of other races, genders, and nationalities, they often draw more dispositional inferences from those behaviors than from the identical behaviors of their cohort. People think of out-groups in simple ways and are thus overly influenced by the unrepresentative actions of their members ( Linville & Jones, 1980 ; Quattrone & Jones, 1980 ). In addition, unfamiliarity with the norms that govern an out-group member's behavior may not allow us to estimate the consensus that such actions enjoy. We may assume, for example, that an Israeli acquaintance is particularly bold without realizing that close physical proximity is a Middle Eastern custom and, as such, reveals little about the acquaintance as a unique individual. Our research suggests an additional mechanism by which such effects may be produced. Despite the universality of spontaneous emotional expressions Ekman, 1971 ), some attitudes and emotions are displayed by different cultures in different ways. A Brit may affect a stoic smile when unhappy, a Japanese may nod despite strenuous disagreement, and even members of familiar subcultures may use words in ways that confound their usual meaning ("That saxophone player is really bad "). Even when an outsider knows these display rules, it is likely that their application requires considerable thought ("Let's see, if hot is cool and bad is good, then radical must mean ..."). To the extent that out-groups attach different behavioral signs to the thing being signified, then passing from the former to the latter may be something of a labor. One reason why we draw dispositional inferences about out-group members, then, may be that we work hard to understand what they are doing and are thus unable to give much thought to why they are doing it. Categorization by Actors and Observers Jones and Nisbett (1972) suggested that there is "a pervasive tendency for actors to attribute their actions to situational requirements, whereas observers tend to attribute the same actions to stable personal dispositions" (p. 80, italics added). Despite some notable exceptions Monson & Snyder, 1977 ), this simple proposition has proved enormously reliable and robust. One explanation of the effect is that actors and observers assign different identities to the actor's behavior. Actors often have "inside information" that may enable them to construe their behavior in ways that an observer might not. Whereas an observer may think of eating meat loaf as an expression of hunger, the actor may know that eating Aunt Shirley's meat loaf is an act of duty and sacrifice. As such, actors and observers may occasionally make different attributions because they are, in fact, making attributions for different behaviors (cf. Kahneman & Miller, 1986 ). But even when actors and observers do agree about the identity of an action, it seems reasonable to suggest that actors will generally find it easier to categorize their own behavior than will observers. Knowledge of present intent and past behavior may enable the actor to make sense of actions that would initially strike an uninformed observer as quite anomalous. The person who places a rubber chicken on a friend's grave certainly knows that this was the favorite prop of the departed comedian, but an uninformed observer will have to work particularly hard to understand an offering of a silly toy. Our experiments suggest that the payoff for the observer's hard work may be an undercorrected dispositional inference about the actor. The divergent attributions of actors and observers may be, at least in part, a consequence of the relative difficulty each has in determining just what behavior it is that needs attributing. Hidden Meaning of Action Experimental psychologists have long considered clinical judgment to be something of a slow-moving target. In addition to having undue confidence in their judgments ( Oskamp, 1965 ) and perceiving relations where none exist Chapman & Chapman, 1967 ), psychodynamic therapists have also been accused of showing a profound bias toward dispositional explanations of human behavior. As Nisbett and Ross (1980, p. 244) remarked, "Freud risked elevating the fundamental attribution error to the status of a scientific principle." Although it is difficult to gauge the pervasiveness of this dispositional bias, one line of reasoning suggests that the bias is a natural consequence of the clinical task. The goal of most insight therapies is to identify a person's behavior in ways that the person might not. A client may claim that she is visiting her mother every Tuesday and taking a class in quantum mechanics, but the therapist's job is to discover the transcendental connection that allows both actions to be similarly construed. Clinicians must certainly exert considerable effort to uncover the single category to which apparently disparate behaviors belong; it is no meager insight to realize that frequent visits with a critical parent and enrollment in a demanding seminar may both be subtle means of undermining one's sense of self-worth. Our research suggests that because such categorizations are difficult to achieve, clinicians may be able to devote less thought to the situational antecedents of the behaviors that they have categorized. Ironically, it may be the clinician's shrewd categorization of action that casts a somewhat dispositional pale on the analysis of its causes. Our studies suggest that observers should be inclined to draw dispositional inferences for those actions whose "hidden meanings" challenge discovery. In fact, the more deeply obscured the meaning of behavior, the more likely observers should be to embrace a dispositional explanation for it-a speculation that brings whole schools of psychotherapy to mind. Conclusion A popular bit of graffiti has it that "time is nature's way of keeping everything from happening at once." In a similar vein, it might be said that consciousness is nature's way of keeping people from thinking all their thoughts at the same time. Indeed, the most fundamental limit of consciousness is that it takes but a single object, and thus thinking about one thing necessarily precludes thinking about another. Drawing inferences from human behavior is no exception to this rule: To the extent that we stop to ponder one aspect of a person's behavior, we may be prevented from considering another aspect, and our ultimate understanding of the person may therefore be incomplete. The present experiments suggest that when what becomes a tough question, social perceivers may never ask why. References Baron, R. M. (1988). An ecological framework for establishing a dualmode theory of social knowing.(In D. Bar-Tal & A. W. Kruglanski (Eds.), The social psychology of knowledge (pp. 48-82). Cambridge, England: Cambridge University Press.) Cantor, N., Pittman, T. S. & Jones, E. E. (1982). Choice and attitude attribution: The influence of constraint information on attributions across levels of generality. Social Cognition, 1, 1-20. 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Approximately half the subjects in each condition saw one actress, and the remaining subjects saw the other. The identity of the actress had no effect on any of the analyses to be reported and is not discussed further. 2 It may be useful to keep in mind that subjects in this and the previous experiment observed behavior that was dynamic and ongoing. In such situations, a subject may be thought of as executing the categorize-characterize-correct sequence with each new quantum of behavior. When the categorization stage takes too much capacity (i.e., time or attention), the subject should not be able to execute the correction stage before she has to go back and execute the categorization stage again for the next behavioral quantum. 3 In fact, subjects who heard normal behavior showed an unexpected reversal, t (61) = 2.10, p < .05. This reversal was due primarily to ratings on two items that described emotional displays: (a) I think I should be emotionally stronger and tougher than my wife and (b) I expect my wife to be more emotionally dependent on me than I am on her. These items (and no others) mapped directly on to one of the modern target's responses and thus caused subjects to infer that the modern target had a traditional attitude toward emotional displays. Indeed, an ANOVA performed on an eight-item index that excluded these two MFRQ items revealed only the expected Target's Response * Behavior Quality interaction, F (1, 61) = 8.25, p < .01. Most important, the unexpected reversal was no longer reliable, t (61) = 1.57, p > .12. Habits as Knowledge Structures Automaticity in Goal-Directed Behavior Henk Aarts Department of Psychology and Language Eindhoven University of Technology Ap Dijksterhuis Department of Social Psychology University of Nijmegen ABSTRACT This study tested the idea of habits as a form of goal-directed automatic behavior. Expanding on the idea that habits are mentally represented as associations between goals and actions, it was proposed that goals are capable of activating the habitual action. More specific, when habits are established (e.g., frequent cycling to the university), the very activation of the goal to act (e.g., having to attend lectures at the university) automatically evokes the habitual response (e.g., bicycle). Indeed, it was tested and confirmed that, when behavior is habitual, behavioral responses are activated automatically. In addition, the results of 3 experiments indicated that (a) the automaticity in habits is conditional on the presence of an active goal (cf. goal-dependent automaticity; J. A. Bargh, 1989 ), supporting the idea that habits are mentally represented as goal-action links, and (b) the formation of implementation intentions (i.e., the creation of a strong mental link between a goal and action) may simulate goal-directed automaticity in habits. ---------------------------------------------------------------------------- This work was facilitated by a J. F. Schouten Institute for User System Interaction grant and a Royal Netherlands Academy of Arts and Sciences fellowship. We thank John Bargh, Peter Gollwitzer, Sander Koole, Rene Lion, Edwin Poppe, Peep Stalmeier, and Marcel Zeelenberg for their helpful comments on earlier versions of this article. We are grateful to Ankie Bosch, Erica Derijcke, Peter De Vries, Alderina Fokkema, Christel Van Vught, and Ronald Willemsen for their help in collecting the data. Correspondence may be addressed to Henk Aarts, Department of Psychology and Language, DG 0.23, Eindhoven University of Technology, Eindhoven, the Netherlands, 5600 MB. Electronic mail may be sent to email@example.com Received: November 24, 1997 Revised: April 23, 1999 Accepted: May 14, 1999 ---------------------------------------------------------------------------- ---- The majority of people's actions are executed on a routine basis. The better part of the behavioral repertoire is frequently exhibited in the same physical and social environment and has taken on a habitual character (e.g., James, 1890 ; Ouellette & Wood, 1998 ; Triandis, 1980 ). These habits are extremely useful in that they enable one to perform one's actions in a mindless, automatic fashion. James (1890) emphasized the importance of habits: "We must make automatic and habitual, as early as possible, as many useful actions as we can" (p. 122). James's reasoning was straightforward. The more actions one can delegate to the unconscious, the more room there is to do things that necessarily require conscious processing. Writing an article would be a more difficult affair if typing (and driving, and taking a shower, and even brushing one's teeth) required conscious planning. Despite a large contemporary literature on automatic processes in social and nonsocial cognition and behavior (e.g., Bargh, 1989 , 1997 ; Hasher & Zacks, 1979 ; Norman & Shallice, 1986 ; Smith & Lerner, 1986 ), the available research on the concept of habit is largely confined to studies in which the role of past behavior was investigated in the context of attitude-behavior models. An important contribution in this field was made by Bentler and Speckart (1979) , who investigated students' consumption of alcohol and marijuana. These authors suggested that such actions become habitual over time and, importantly, that they can be instigated without mediation of attitudes or intentions (or "products of reasoning" in general). Indeed, the results of their study clearly showed that a measure of habit (obtained by self-reported frequency of behavior in the previous 2 weeks) does predict future behavior directly, indicating that such behavior is initiated automatically, that is, without deliberation and thought. The work of Bentler and Speckart (1979) has been replicated by many other investigators in a wide variety of behavioral domains. Ouellette and Wood's (1998) meta-analysis of studies on habits showed that the direct influence of past behavior on future behavior was most pronounced for behaviors executed frequently and consistently in a stable context. Behaviors carried out less often were more accurately predicted by consciously formed intentions toward the behavior. This pattern of results indeed confirms the assumption that when a behavior has been performed many times in the past, future behavior becomes increasingly under control of an automatized process, whereas a behavior executed less frequently is (still) guided by evaluative interpretations and considerations (as expressed, for instance, in the theory of reasoned action; Fishbein & Ajzen, 1975 ). In summary, there is ample evidence indicating that habitual behavior (with habitual defined as a function of relative frequency of past performance) is automatic. It is determined by past behavior and not mediated by attitudes, intentions, or other concepts referring to more deliberate or conscious processes (see also Aarts, Verplanken, & van Knippenberg, 1998 ; Triandis, 1980 ). The direct influence of past behavior performed frequently on future behavior also underscores the behaviorists' maxim that behavior is largely influenced by habit (e.g., Hull, 1943 ; Skinner, 1938 ; Watson, 1914 ). Actually, the direct relation between past and future action shows that people simply do things as they did them before. Because the concept of habit is strongly rooted in behaviorist approaches to learning theory, it was assumed for a long time that mental (or cognitive) processes do not mediate the automatic activation of habitual responses to environmental stimuli. In contemporary research, however, it is often argued that cognition does play a role in the direct control of environmental cues over behavior (e.g., Bargh & Chartrand, 1999 ; Bargh & Gollwitzer, 1994 ; Norman & Shallice, 1986 ; Ronis, Yates, & Kirscht, 1989 ). In line with this research, our main hypothesis is that habits are mentally represented and that they can be activated automatically. Specifically, we conceive of habits as a form of goal-directed automatic behavior (cf. goal-dependent automaticity; Bargh, 1989 ). Habits are represented as links between a goal and actions that are instrumental in attaining this goal. The strength of such links is dependent on frequent co-activation of the goal and the relevant actions in the past. The more often the activation of a goal leads to the performance of the same action under the same circumstances, the stronger the habit (i.e., the link between goal and action) will become. Our purpose was to investigate some key assumptions derived from this conceptualization. First, we hypothesized that habitual behavior can be activated automatically. Second, we assumed that this automatic activation does not occur for behavior that is not habitual (not represented as a goal-action link). Third, we assumed that the automatic activation of a habitual action is goal dependent. That is, actions are automatically activated provided that the relevant goal is activated in the first place. Development and Representation of Habits Goals refer to desired, or anticipated, outcomes or end states ( Austin & Vancouver, 1996 ; Gollwitzer & Moskowitz, 1996 ; Locke & Latham, 1990 ). These goals can be the consequence of physiological needs, such as thirst and hunger, as well as various other "needs" or motives, such as restoring personal hygiene, making friends, acquiring knowledge, or becoming a professor (e.g., Geen, 1995 ; Mook, 1996 ). When goals are not pursued often, actions that one can deploy to attain desired outcomes are not specified in these goals. Instead, goal activation guides organisms to select an action or to form a plan to perform a certain action (see also Carver & Scheier, 1981 ; Cohen, 1996 ; Srull & Wyer, 1986 ), but it does not lead to the immediate instigation of an action. Hence, when pursuing relatively unfamiliar goals, people are likely to ponder the possible actions they can use to achieve the goal before they engage in an action. 1 When goals are pursued regularly, however, the need to pay conscious attention to details dwindles (e.g., Anderson, 1982 ; Fitts & Posner, 1967 ; Newell & Rosenbloom, 1981 ). When people select the same actions more often and when these actions lead to goal achievement in a satisfactory manner, the actions become mentally linked to the goal. That is, selecting and performing the same goal-directed behavior frequently and consistently leads to associations between the goal and the instrumental actions (i.e., to the formation of a habit). As a result, activation of these goals spreads automatically to the associated actions (cf. Anderson, 1993 ; M‰ntyl‰, 1993 ). The exhibition of habits, then, is the result of the automatic and immediate activation of the habitual action on the instigation of a goal. These goals are critical. In the case of habits, the instigation of the goal to act is necessary to activate the associated actions automatically. Many well-practiced or skilled actions, such as typing, driving a car, and riding a bicycle, are usually qualified as automatic or habitual, but they require the activation of a goal. For instance, an undergraduate student needs a goal to use a bicycle (e.g., traveling to attend lectures at the university) to activate subsequent behavioral steps such as "going to the garage to get the bike" and "turning left at the statue of the hairy dromedary." In a sense, habits can be seen as hierarchical mental representations in which activation of a goal leads to activation of a number of associated behaviors lower in the hierarchy. The proposal of such a hierarchical structure of action is consistent with others who have previously proposed such a representation ( Carver & Scheier, 1981 ; Gallistel, 1985 ; Miller, Galanter, & Pribram, 1960 ; Mischel, 1973 ; Powers, 1973 ; Vallacher & Wegner, 1987 ; see also Schank & Abelson, 1977 ). It should be noted that our perspective on the development of (the cognitive representation of) habits is also partly based on the recent work of Bargh (1990 ; see also Bargh & Gollwitzer, 1994 ). He has suggested that when the same choices are frequently pursued and implemented in a given situation (or as the result of a given goal), an association between the mental representation of that situation and the representation of the goal-directed action will emerge. Frequent coactivation of a particular situation and a particular behavioral decision increases the strength and accessibility of that association. Hence, frequent and consistent performance of a goal-directed action in a specific situation facilitates the ease of activating the mental representation of this behavior (and hence the resulting action itself) by the situation. Similar principles have also been proposed and empirically established for the activation of other mental representations, such as attitudes and stereotypes ( Devine, 1989 ; Fazio, Sanbonmatsu, Powell, & Kardes, 1986 ; for an overview, see Higgins, 1996 ). Activation of Goal-Directed Action That the environment is indeed capable of activating goal-directed behavior automatically has been established in the domain of motivations. In a test of their automotive model, Bargh and colleagues ( Bargh, 1997 ; Bargh, Gollwitzer, Chai, & Barndollar, 1999 ) showed that goals (and the associated actions) can be elicited directly by the environment. Activation in this case occurs without the person's awareness. They found that participants who were primed with achievement or affiliation goals behaved in accordance with the primed goal (solving either many or a few word puzzles in the presence of a confederate who appeared to be not skilled in the task). In other words, the situation elicited the relevant behavior as dictated by the primed goal. Support for the idea that the activation of actions depends on the underlying mental representation and that this representation differs as a function of one's personal history was obtained by Bargh, Raymond, Pryor, and Strack (1995) . They established that power (as a situational feature) and sexuality are mentally associated, but only for those with sexual harassment tendencies. Hence, this research shows not only that the environment can have a direct impact on goal-directed behavior but also that this effect is dependent on whether goal and action are associated: The environment affects goal-directed behavior automatically only if goals are associated with the environment (i.e., when this goal was pursued earlier in the same situations). Our conceptualization of habit is comparable with this perspective. The environment can activate goal-directed behavior automatically, but only when this behavior is habitual, that is, only when the behavior is associated with the activated goal. In summary, this evidence supports the idea that goal-directed behavior is mentally represented and can be automatic. As we have argued, our conceptualization of habits is based on these ideas. We conceive of habits as a form of goal-directed automatic behavior. Habits are represented as associations between goals and actions that allow the instigation of automatic behavior on the activation of these goals by the environment. The degree of "habitualness" of behavior is argued to be a consequence of the frequency with which these goal-directed actions have been performed in similar situations in the past. In the present article, we report three experiments that were designed to test the key assumptions about habits formulated earlier. In these experiments, we studied cycling behavior among Dutch college students as an example of habitual behavior. Our main purpose was to investigate whether, and under what circumstances, bicycle use is capable of being directly activated by travel goals such as "going to attend lectures at the university" and "going shopping at the city center mall." Experiment 1 In the first experiment, we tested the hypothesis pertaining to the key assumption that habits can be seen as mental associations between travel goals and (transportation) action and, hence, that these goals can activate habitual transport behavior automatically. That is, we hypothesized that habitual action is activated automatically on the instigation of a goal and that such actions are not activated among people for which the behavior is not habitual. A secondary aim was to show that goals can exert their influence on habitual responses when people are not aware of the relation between an earlier-primed goal and the habitual response. In this experiment, habitual and nonhabitual bicycle users were primed or not primed with travel goals (e.g., having to attend lectures) and then asked to respond to the word bicycle after being presented with locations (e.g., university) that corresponded to the earlier-activated travel goal. Response latencies on the location-bicycle links served as the dependent variable. We assumed that habitual bicycle users would show enhanced accessibility and thus respond faster to the word bicycle than nonhabitual bicycle users but only after being activated with the goal to travel. We did not expect habitual and nonhabitual bicycle users to differ in their speed of responding when the goal to travel was not activated. Method Participants and design. Fifty-four students at the University of Eindhoven participated in the experiment, receiving 5 Dutch guilders (approximately $3) in return. Because the experiment focused on bicycle use for short trips, only university students were recruited who lived in or around Eindhoven and who owned a bicycle. However, these participants varied in the frequency with which they used their bicycles, which is crucial for obtaining different levels of bicycle habit strength (i.e., nonhabitual vs. habitual). Participants were randomly assigned to one of the two experimental conditions: a goal priming condition and a no goal priming control condition. Selection of materials. Initially, a pilot study was conducted to obtain travel goals and corresponding locations for which bicycle use constituted a realistic transport mode option for all participants. Forty-two University of Eindhoven students were presented with 60 different locations inside and outside the city and were asked to assess the usefulness of four travel modes (bicycle, bus, walking, and train) to these locations. We obtained 5 locations (e.g., a shopping area, called "heuvelgalerie," a sports center, and a popular night-life area, called "stratumseind") for which a bicycle constituted a realistic option for all students (percentages mentioning use of a bicycle for travel to these destinations were nearly all 100%). Thus, these locations represent travel destinations for which a bicycle is a realistic travel mode. We subsequently asked the students to mention the main reason to travel to each of the 5 locations. For each location, we selected the most frequently mentioned reason, which provided us with descriptions of five travel goals (e.g., shopping at the city center mall). These five travel goals and corresponding travel destination-bicycle pairs composed the targets of interest in Experiment 1 (as well as in the subsequent experiments). In addition, 5 locations were obtained involving trips for which a bicycle did not represent an option (e.g., Maastricht, a city approximately 90 km from Eindhoven). Furthermore, for each of the other three travel modes (walking, bus, and train), 5 location-option combinations and 5 location-no option combinations were obtained. The latter 35 location-travel mode units (i.e., all except the first 5) served as filler trials in the association task (described later). Experimental task and procedure. Participants worked in separate cubicles. Computers were used to run the experiments and the computer program provided all instructions. As a cover story, participants were told that they would take part in a study conducted by the Department of Psychology and Language. Moreover, they were told that the study consisted of three separate tasks designed by different department research teams. In reality, the first task served as the manipulation phase for goal priming, and the second task was designed to study the effects of habit and goal priming on the speed of responding to the target bicycle trips. Habit strength was assessed in the third task. In the first task, announced as the "media and information use inventory task," we were allegedly interested in several aspects of communication and language (e.g., participants were asked to estimate how many hours a week they watch television, whether they make use of video text, and so on). As part of this task, half of the participants learned that they had to read five different sentences that designated students' activities in daily life (goal priming condition). Participants were told that we were interested in how long it takes individuals to read each sentence. This information was allegedly helpful for the purpose of designing new communication systems. Participants were instructed to press a button after they had carefully read the sentence. The five sentences actually described the five different travel goals (e.g., going shopping at the city center mall) that corresponded to the five travel locations (e.g., heuvelgalerie) obtained in the pilot study and used later in the travel mode association task. It is important to note that we did not provide the travel locations per se; rather, we provided only the reason to go to each location (e.g., going shopping). Each description thus consisted of one short sentence representing a goal to travel to a destination with a certain transport mode. The sentences were presented in random order in the center of the computer screen. The other half of the participants (those in the control condition) did not read these sentences as part of the inventory task and thus started with the second task without being exposed to (or primed with) the travel goals. 2 The second task was announced as an association task allegedly designed to study relations between all sorts of locations and travel behavior. Participants were told that 40 different location words would appear briefly on the screen followed by a mode of transport. Their task was to indicate, as quickly and as accurately as possible, whether the presented mode would constitute a realistic means of transport for the previously presented location. Furthermore, they were told that 200 different locations were stored in the memory of the computer and that the computer would first randomly select the 40 locations. An hourglass was displayed on the computer screen, simulating the selection procedure. By using this (fake) selection procedure, we hoped to further reduce any perceived connection between the goal priming task and the association task. The 40 location-transport mode trials were presented in random order and preceded by eight practice trials. Thus, the five target location-bicycle trials were embedded in the filler trials. This relatively large number of fillers was incorporated to create a genuine multiple response situation, that is, to ensure that participants had to respond to different locations with different travel modes. An experimental trial consisted of the following sequence of events: (a) presentation of a row of asterisks (i.e., fixation point) for 500 ms, (b) presentation of the location word for 200 ms, (c) presentation of a row of asterisks (i.e., postmask) for 100 ms, and (d) presentation of a travel mode. Thus, the stimulus onset asynchrony (SOA; i.e., the time between presentation of the location and the travel mode) was set at 300 ms. This time interval was assumed to be too short to allow participants to form expectancies and to implement strategic processes, and hence we measured automatic responses this way (cf. Neely, 1991 ). The travel mode word remained on the screen until the participant responded. Everything appeared at the same location on the screen. Responses were collected from the PC's keyboard. Participants pressed a key labeled yes or no. To obtain maximum speed during the task, participants were instructed to keep their fingers above the keys throughout the task. Response latencies were measured from the onset of the travel mode, but participants had to respond within 3 s. If participants completed a trial within the allotted time, the message "pay attention" was presented for 2 s on the screen, indicating that the computer would initiate the next trial. However, if no response was given after 3 s, the message "please, respond faster" was presented, followed by the announcement of the next trial. The dependent variable was the response latency across the five target location-bicycle pairs. With the third task, introduced as the leisure time inventory, we measured habit strength. Participants' estimates of frequency of bicycle use in the recent past for different trips were used to obtain information on their bicycle habit strength. This operationalization coincided with Hull's (1943) early work on habit formation in which he proposed that, as the number of repeated pairings between a situation (e.g., travel location) and a response (e.g., travel mode) increases, so does the strength of that association or habit. Specifically, participants were presented with a sample of 10 travel locations; for each location, they were asked to count the number of times they had traveled there with their bike in the previous 2 weeks. The presented locations consisted of the five target destinations and five other locations situated near the target locations. Next, we averaged the frequency estimates of bicycle use across the 10 destinations and, on the basis of a median split, categorized participants as nonhabitual or habitual in regard to bicycle use. Because this measurement procedure could serve as a prime for locations and bicycle, we decided to measure habit strength at the end of the experimental session, that is, after the goal priming and association tasks (see Bargh & Chartrand, 1999 , on the subject of unwanted effects of priming). Furthermore, as a means of attenuating possible influences of the previous tasks on the estimates, participants were explicitly instructed to be as accurate as possible in their recall ( Aarts & Dijksterhuis, 1999 ; cf. Thompson, Roman, Moskowitz, Chaiken, & Bargh, 1994 ). After the measurement of habit strength, participants were thoroughly debriefed. The debriefing indicated that participants were unaware of the hypotheses under investigation. Moreover, they did not perceive a link between the two tasks and, therefore, perceived no connections between the travel locations used in the goal priming and association tasks. Therefore, we could conclude that we succeeded in creating two ostensibly unrelated tasks. Results and Discussion All participants completed the five target trials within the allotted time. Only latencies concerning yes responses across the five location-bicycle pairs were included in the analyses (99.3% of all responses, a percentage that might be expected on the basis of our pilot study). We computed the average response latency across the five target location-bicycle trials for each participant. Response latencies were subjected to a 2 (goal priming: present vs. absent) * 2 (habit strength: nonhabitual vs. habitual) between-subjects analysis of variance (ANOVA). The means for each cell in the design are displayed in Table 1 . As Table 1 shows, habitual participants' responses were slightly faster than nonhabitual participants' responses, F (1, 50) = 3.89, p < .06. The main effect of goal priming was nonsignificant, F (1, 50) = 0.10. More important, however, the ANOVA revealed the predicted two-way interaction of habit strength and goal priming, F (1, 50) = 5.87, p < .02. Planned comparisons showed that, in the no goal prime condition, habitual participants' response latencies did not differ reliably from nonhabitual participants' response latencies, F (1, 50) = 0.11, ns. However, habitual participants' response latencies were significantly faster than responses of nonhabitual participants in the goal priming condition, F (1, 50) = 9.21, p < .005. Planned comparisons between the no goal prime and goal prime conditions yielded no reliable effect in the nonhabitual group, F (1, 50) = 2.30, ns, and a marginally significant effect in the habitual group, F (1, 50) = 3.22, p < .08. This second effect shows that habitual participants reponded faster after being primed with travel goals. The results of Experiment 1 supported our predictions. Habitual bicycle users who were primed with travel goals showed faster responses than nonhabitual bicycle users. Furthermore, this effect did not appear in the absence of goal priming. As hypothesized, the data show that activation of travel goals is required to reveal the mental accessibility of the habitual travel behavior. In general terms, the automaticity of habitual behaviors is conditional on the presence of a goal (cf. goal-dependent automaticity; Bargh, 1989 ). Experiment 2 The main goal of Experiment 2 was to compare habits with conscious planning. We have argued that habits are represented as links between goals and actions instrumental in attaining these goals and that these links are the result of frequent coactivation of goal and action. However, there is another way in which strong links between goals and actions are established, namely through the formation of implementation intentions ( Gollwitzer, 1993 , 1996 ). These intentions take the form of "I will do x whenever situation z occurs," and hence they link an action to a goal. Such intentions are strategically formed by individuals to promote the initiation of goal-directed action, especially when the performance of the action has to be postponed and alternative actions can interfere. For instance, a person intending to drive an alternative route when going home from work (a nonhabitual goal-directed action that is often insufficiently implemented) may increase the chances of indeed driving this route by planning. Our hypothesis is that habits can be simulated by implementation intentions but that habitual behavior does not profit from planning (for a similar line of reasoning, see Bargh & Gollwitzer, 1994 ). This idea stems from the assumption that strategic planning leads to essentially the same goal-action links (e.g., "I will use the bicycle when having to attend lectures at the university") as the ones we assume to represent habits. These intentionally formed associations between goal and action are functionally equivalent to habitual associations, and hence such actions may be automatically activated as well ( Gollwitzer, 1993 ). The only difference is that habits are the result of frequent past behavior, whereas links stemming from implementation intentions are the result of conscious (recent) planning. However, and this is important, because habits are already backed by strong links between goals and action, it is anticipated that only nonhabitual individuals may benefit from planning in the sense of enhanced accessibility or faster responses to a travel mode after goal priming. Habitual people, on the other hand, already possess these strong links and are expected not to benefit from planning. Thus, by comparing habits and planning, we may be able to show similarities between more chronic (habitual) and temporary primed forms of automatized actions ( Bargh, Bond, Lombardi, & Tota, 1986 ; Fazio et al., 1986 ; Srull & Wyer, 1986 ). To test these ideas, we examined the interaction between bicycle habit strength and the formation of implementation intentions (by means of planning travel goals) using the same association task as in Experiment 1. All participants were given the same five travel goals. However, two different planning procedures were designed. Participants in the experimental condition planned to use the bicycle for travel goals, whereas participants in the control condition were required to plan a different action, namely to repair a flat tire of a bicycle. This was done to ensure that we did not make the term bicycle more accessible for one group than for the other. Experiment 2 served two further purposes. First, we tried to replicate the effect of habit on associative strength between goals and actions under conditions of goal activation by manipulating goals in a different manner. In this experiment, participants learned that they actually had to travel on finishing the experiment. Second, we included mediator variables to rule out alternative explanations for the observed effects of habit and planning. For instance, the two planning conditions could yield differences in strength of travel goal activation or perceived feasibility of using a bicycle. As a result, the effect of planning on response times could be attributable to variances in these variables. Also, it can still be argued that the effects of bicycle habit are guided by evaluative rather than goal-directed automatic processes and, thus, may be mediated by perceived desirability of (or attitudes toward) bicycle use. For the present purpose, three potential mediators seemed relevant to test for mediator effects: strength of travel goal instigation, perceived feasibility of bicycle use, and desirability of bicycle use. Method Participants and design. University students were recruited who lived in or around the city of Eindhoven and who owned a bicycle. Fifty-three undergraduates were randomly assigned to one of the two experimental conditions: a related planning condition and an unrelated planning condition (control). Habit strength (nonhabitual vs. habitual) was measured. Participants received 5 Dutch guilders (approximately $3) in return for taking part. Experimental task and procedure. Participants were told that they would take part in research conducted by the Department of Psychology and Language and that three separate tasks designed by different research teams had to be performed. The experiment was run on computers. The computer program provided all instructions. Participants worked in separate cubicles and were provided with three consecutive tasks: a planning task, an association task, and the habit measure. In the first task, participants learned that the study involved the relation between language and planning in daily life and that they would be requested to plan the steps required to perform a certain task. Initially, all participants were told that one of five different travel goals should be personally attained by use of a bicycle after the experiment and that they had to report on their experiences of the attained goal. Subsequently, they were instructed to read the descriptions of five travel goals (see Experiment 1). Participants learned that the decision as to which of the goals they had to attain would be revealed to them after the experimental session. Furthermore, to stress the importance of the task, we informed part icipants that the study was designed to test whether the language used in the planning task is affected when individuals do not know in advance which goal they have to attain. At this point, the instructions for the two conditions began to differ. Half of the participants were then told to imagine having a flat tire that had to be repaired. These participants were asked to plan the subgoals required to repair the flat tire. This condition was referred to as the unrelated planning condition and could be treated as a control condition, because participants were requested to plan activities not directly related to the attainment of the travel goals. They were handed a booklet containing separate sheets listing five major subgoals of repairing a flat tire (e.g., searching for the repair kit and looking for a spot to fix the flat tire). This was done to keep the working load and procedure similar to the related planning condition (as described subsequently). Moreover, as a means of ensuring that participants perceived the task as realistic, they were told that the five subgoals emerged from the planning activities of students in a previous study. For each subgoal, participants were requested to write down when (time of the day), where (locating the spot to attain the subgoal), and how (procedure) they would accomplish it. In the experimental condition (referred to as the related planning condition), participants were asked to plan the five travel goals. They were also provided with a booklet, but this time the booklet contained the assignment to plan the three steps of the five travel goals on separate sheets. For each travel goal, participants were requested to write down when (time of the day), where (locating the district of the destination, e.g., in the city center), and how (the route of travel) they would accomplish it. It should be noted that we asked participants to write down the district and not the location itself. All participants were given 6 min to complete the planning task. After the planning task, as part of a larger questionnaire, participants responded to the following three questions: How likely do you believe it is that you will use the bicycle for one of the five travel goals? To what extent do you believe that, on average, the bicycle is a feasible mode to use for the five travel goals? To what extent do you believe that, on average, the bicycle is a desirable mode to use for the five travel goals? The first item served as a check of the strength of the travel goals, and the last two items captured the perceived feasibility of and desirability of (attitide toward) using the bicycle for the five travel goals. All items were accompanied by unipolar 9-point response scales ranging from not at all (1) to very much (9). After completion of the planning task and questionnaire, participants learned the same association task as in Experiment 1. Moreover, we used the same cover story and the same fake selection procedure as in Experiment 1 to further minimize the perceived connection between the target locations used in the planning and association tasks. The dependent variable was the response latency across the five target location-bicycle pairs. After completing the association task, participants reported their frequency estimates of bicycle use across 10 travel destinations (see Experiment 1). On the basis of a median split, they were categorized as nonhabitual or habitual in regard to bicycle use. Debriefing indicated that participants had no idea about the true nature of the experiment. First, all participants were unaware of the hypotheses under investigation. Furthermore, although most participants expressed a belief that the tasks were dealing with the same subject (i.e., the tasks focused on travel behavior), none of them had actually noticed that some of the locations in the association task were related to the travel goals in the planning procedure. In summary, none of the participants indicated suspicion as to the actual relation between the tasks. Not surprisingly, some participants spontaneously asked which trip they were supposed to make, revealing that we succeeded in the instigation of actual travel goals. Of course, we informed all participants that the travel goals were provided only to test our hypotheses, but we added that they were free in making whatever trip they wanted to make. Results and Discussion All participants completed the five target trials within the allotted time. Only latencies concerning yes responses across the five location-bicycle pairs were included in the analyses (99.25% of all responses). We computed the average response latency across the five target bike-location trials for each participant. Response latencies were subjected to a 2 (planning: unrelated vs. related) * 2 (habit strength: nonhabitual vs. habitual) between-subjects ANOVA. The means for each cell in the design are displayed in Table 2 . As can be seen in Table 2 , habitual participants' responses were faster than nonhabitual participants' responses, F (1, 49) = 4.66, p < .04, thereby replicating the effect of habit strength on response latency in the goal activation condition of Experiment 1. The main effect of planning was also highly significant, F (1, 49) = 9.04, p < .005. Participants' response latencies in the related planning condition were faster than participants' responses in the unrelated planning condition. More important, the ANOVA revealed the predicted two-way interaction of habit strength and planning, F (1, 49) = 5.56, p < .03. Planned comparisons revealed that habitual participants' response latencies were not affected by type of planning, F (1, 49) = 0.02, ns. However, nonhabitual participants' response latencies were significantly faster in the related planning condition than in the unrelated planning condition, F (1, 49) = 13.95, p < .003. These latter results show that planning (or formation of implementation intention) facilitated the speed of nonhabitual participants' responses, whereas this was not the case for habitual participants. Potential mediators of the observed effects. Three variables were measured after completion of the planning task. With these measures, we wanted to rule out potential mediators. Specifically, we measured strength of goal activation and perceived feasibility and desirability of bicycle use. We first examined whether there were any significant effects of planning and habit on the three mediators, and we subsequently performed a 2 (planning: unrelated vs. related) * 2 (bicycle choice habit strength: nonhabitual vs. habitual) between-subjects analysis of covariance (ANCOVA) with the potential mediators as covariates. Strength of travel goal activation. Participants in all conditions believed to the same extent that they had to use the bicycle for one of the five travel goals ( M = 7.55, SD = 1.88). Nonhabitual participants did not differ significantly from habitual participants, F < 1, and participants in the unrelated planning condition did not differ from participants in the related planning condition, F < 1. The interaction of habit strength and planning was also nonsignificant, F < 1.52. Note that the mean was well above the midpoint of the 9-point scale, suggesting that, on average, participants took the manipulation of goal instigation seriously. Perceived feasibility of bicycle use. There were no differences between conditions with respect to participants' conviction that the bicycle was a feasible option to use for the travel goals ( M = 8.36, SD = 0.86), as indicated by nonsignificant effects of habit strength, planning, and their interaction (all F s < 1). Perceived desirability of bicycle use. Participants in the different conditions judged the bicycle equally desirable ( M = 7.42, SD = 1.67). The main effects of habit strength and planning and their interaction were nonsignificant (all F s < 1). An ANCOVA with the three mediator variables as covariates yielded the same pattern of significant results for habit, planning, and their interaction: F (1, 46) = 5.54, p < .03; F (1, 46) = 9.63, p < .005; and F (1, 46) = 5.52, p < .03, respectively (these effects were much the same as the ones resulting from the original ANOVA's). Taken together, then, these analyses indicate that the observed pattern of results is attributable neither to the "magnitude" of goal activation nor to the perceived feasibility and perceived desirability of using the bicycle as a mode of transport for the five travel goals. The first conclusion to be drawn is that, in Experiment 2, we replicated the results of Experiment 1. After activation of a travel goal, habitual bicycle users responded faster to bicycle trips than nonhabitual bicycle users. Moreover, we obtained a reliable effect of planning. When travel goals were furnished with related implementation intentions, participants responded much faster than those who formed unrelated implementation intentions. The latter group was much slower in linking the bicycle to the travel destinations. As hypothesized, this effect was present only among nonhabitual bicycle users. Habitual bicycle users did not benefit from planning, which supports the idea that these participants already possessed strong associations between travel goals and transport behavior. The observed effects were not mediated by judgmental or strategic processes, as indicated by the mediator analyses. Together, the results of Experiments 1 and 2 suggest that habits are mentally represented as associations between goals and actions. In addition, we demonstrated that the links between goal and action can be simulated, namely when the transport behavior is linked (by planning) to the travel goal. Experiment 3 It should be noted that, in Experiments 1 and 2, the strength of the goal-action link was assessed with a task in which participants were requested to associate a transport mode option with a briefly presented travel location that corresponded to the earlier-activated travel goal. However, although we assume that the inclusion of locations does not influence the hypothesized process, the purpose of Experiment 3 was to replicate the major finding of Experiments 1 and 2 by experimentally manipulating the presence versus absence of the locations words. Specifically, we sought to test whether the speed of responding to bicycle use is enhanced as a function of the locations. Our conceptualization entails that these locations are irrelevant and that it is the activated goal that is responsible for the habitual response. However, one may argue that the presented locations themselves can enhance the accessibility of the habitual response. Experiment 3 was designed to rule out this possibility. To assess the influence of the presence versus absence of location words, we used a procedure largely similar to the sequential priming paradigm ( Fazio et al., 1986 ; Neely, 1977 ). In the present procedure, participants were briefly presented with a prime word and required to indicate whether a subsequent word was a verb or not. In other words, we wanted participants to produce a response of the form "word x is an action" after being briefly presented with another word. Among these prime words, we included the five location words as well as five neutral words. All of these words were immediately followed by the word cycling (SOA: 300 ms). Goal activation was experimentally manipulated, and habit strength was measured by self-reported frequency of past bicycle use. If the results of Experiments 1 and 2 are not the product of the presence of location words, as we hypothesize, habitual bicycle users should respond faster to the word cycling than nonhabitual bicycle users, irrespective of whether it was preceded by a location (but, of course, dependent on goal priming). Hence, we predicted an interaction between habit strength and goal activation. Conversely, if presentation of location words does affect the responses, a three-way interaction effect among goal activation, habit strength, and the presence versus absence of location words should emerge. Method Participants and design. University students were recruited who lived in or around the city of Eindhoven and who owned a bicycle. Eighty-nine undergraduates participated in the experiment, receiving 7.5 Dutch guilders (approximately $4.50) in return. They were randomly assigned to experimental conditions. Habit strength (nonhabitual vs. habitual) was measured. Experimental task and procedure. Participants were informed that they would take part in research conducted by the Department of Psychology and Language and that three separate tasks designed by different research teams had to be performed. The experiment was run on computers. The computer program provided all instructions. Participants worked in separate cubicles and were provided with three consecutive tasks: a goal activation task, a verb verification task, and the habit measure. As part of the introduction to the experimental session, participants learned that one part of the study involved actual performance of behavior and that they would be requested to perform a certain task. Specifically, participants were told that one of five tasks should be executed at home after the experiment and that they had to report on their experiences later. Participants learned that the task they had to perform would be announced after the experimental session. Subsequently, they were instructed to carefully read the descriptions of the five respective tasks and to press a button after they had read each description. The descriptions were presented in random order in the center of the computer screen. Half of the participants received five tasks unrelated to travel behavior (e.g., making a telephone call or watching a movie on TV). This condition, labeled the unrelated goal condition, could be treated as a control condition. In the experimental condition (referred to as the related goal condition), participants were exposed to the five travel goals used in the previous two experiments. After activation of the goal, participants were confronted with the verb verification task, allegedly designed to study people's capacity to detect different types of words. Participants were informed that there would be two words presented one after the other on the screen and that they were to press the yes or no button to indicate, as quickly and accurately as possible, whether the second word was a verb or not. For the sake of clarity, participants were told that the verbs designated mundane activities people perform and that the nonverbs designated mundane objects. No explanation or instructions were given regarding the prime words (cf. Bargh, Chaiken, Govender, & Pratto, 1992 ). An experimental trial consisted of the following sequence of events: (a) presentation of a row of asterisks (i.e., fixation point) for 500 ms, (b) presentation of the prime word for 200 ms, (c) presentation of a row of asterisks (i.e., postmask) for 100 ms, and (d) presentation of a (second) target word. The second word remained on the screen until the participant responded. Everything appeared at the same location on the screen. Responses were collected from the PC's keyboard. Participants pressed a key marked yes or no. To obtain maximum speed during the task, participants were instructed to keep their fingers above the keys throughout the task. Response latencies were measured in milliseconds from the onset of the second word to the time participants pressed a button. The interval between word trials was 2 s. Participants were required to respond to two blocks of 80 word pairs. In each block, 40 of the target words were verbs, and 40 were not verbs. Among the 40 verbs, we presented the verb cycling five times. In one block (Block 1), the verb cycling was preceded by location words. In the other block (Block 2), cycling was preceded by words not designating a location. Thus, the word trials were identical for the two blocks with the exception that, in one block, cycling was preceded by location primes (location-cycling pairs), whereas this was not the case in the other block (no location- cycling pairs). In each block, the other word pairs served as fillers. Within blocks, the word trials were presented in random order and preceded by eight practice trials. As a means of controlling for order effects, participants were randomly assigned to one of the two order conditions: Block 1-2 versus Block 2-1. Participants were allowed to rest for 30 s between blocks. The dependent variable was response latency across the five cycling trials. After completion of the verb verification task, participants reported their frequency estimates of bicycle use across 10 travel destinations. On the basis of a median split, they were categorized as nonhabitual or habitual in regard to bicycle use. Debriefing indicated that participants had no idea about the true nature of the experiment. First, all participants were unaware of the hypotheses under investigation. Second, none of the participants indicated suspicion as to the actual relation between the tasks. Results and Discussion Only latencies concerning yes responses across the cycling trials were included in the analyses (98% of all responses). We computed the average latency across the five neutral word-cycling trials and the five location-cycling trials for each participant. Response latencies were subjected to a 2 (goal activation: unrelated vs. related) * 2 (habit strength: nonhabitual vs. habitual) * 2 (location prime: absent vs. present) ANOVA. Goal activation and habit strength were between-subjects variables. The ANOVA revealed that habitual bicycle users responded slightly faster to cycling than the nonhabitual users, although this effect failed to reach significance, F (1, 85) = 1.57, ns. However, the expected interaction between goal activation and habit strength was significant, F (1, 85) = 5.35, p < .03. No other effects were reliable (including the three-way interaction among goal activation, habit strength, and location prime, F < 1). Table 3 shows the Goal Activation * Habit Strength effect on latencies, with the means collapsed across the two types of prime words. To explore the nature of the Goal Activation * Habit Strength interaction, we conducted planned comparison tests. These tests showed that, in the unrelated goal activation condition, habitual participants' response latencies did not differ reliably from nonhabitual participants' response latencies, F (1, 85) = 0.58, ns. However, habitual participants' response latencies were significantly faster than responses of nonhabitual participants in the related goal activation condition, F (1, 85) = 6.34, p < .02. Comparisons between the unrelated and related goal activation conditions yielded no reliable effect in the nonhabitual group, F (1, 85) = 1.95, ns, and a marginally significant effect in the habitual group, F (1, 85) = 3.58, p < .07. In summary, the results of Experiment 3 indicate that the automatic activation of a habitual response is conditional on the presence of a travel goal, thereby replicating the results of the previous experiments. This interaction effect emerged regardless of whether travel locations were presented or not presented, suggesting that the locations did not influence the observed pattern of results. In addition, the fact that the locations did not facilitate the speed of responding to the habitual travel mode indicates that location words are not semantically related to cycling per se. Hence, the presence of these location words in the previous experiments cannot account for our findings. General Discussion In the introduction, we defined habits as goal-directed automatic behaviors. Habits are mentally represented as associations between goals and actions. These associations are shaped by frequent performance of actions and require the activation of the goal to become manifest. The more frequently one engages in a certain goal-directed behavior in similar situations, the stronger the association becomes and, hence, the easier it is to automatically elicit the behavior by activating the goal. These ideas were supported in three experiments: Habitual responses were activated on the instigation of a goal. Furthermore, these same responses were not activated in the absence of the instigation of a goal, and they were not activated when they were not habitual. In Experiment 2, we tested an additional hypothesis. In this experiment, we compared habitual goal-action associations with associations resulting from conscious planning, and some participants formed implementation intentions such as "I intend to perform action x whenever situation z occurs" (cf. Gollwitzer, 1993 ; Gollwitzer & Brandst‰tter, 1997 ). These intentions are supposed to lead to the formation of associations between goals and actions and were hypothesized to be functionally equivalent to habitual associations. Indeed, participants who formed implementation intentions showed enhanced associative strength between travel goal and travel mode, just as did participants for whom the use of this travel mode was habitual. Our results indicate that this effect is manifest only when behavior is not habitual. Participants who had already developed strong habits did not benefit from planning. This makes sense because these participants already possessed strong links between goals and behavioral responses. With the formation of implementation intentions, people seem to be able to simulate goal-directed automaticity in habitual behavior ( Gollwitzer, 1996 ; Orbell, Hodgkins, & Sheeran, 1997 ). As such, these findings extend empirical investigations on the similarities between chronic and temporary sources of accessibility in the domain of social constructs and attitudes ( Bargh et al., 1986 ; Fazio et al., 1986 ). The theoretical significance of the present findings lies in the emphasis on the cognitive mechanism that mediates the often empirically established direct link between frequency of past behavior and later behavior (e.g., Aarts et al., 1998 ; Ouellette & Wood, 1998 ). Our results in regard to automatic activation of associative links support the notion that frequently pursued goals (or habits) are mentally represented in a way comparable to other mental structures that are often repeatedly consulted and automatically activated, such as stereotypes and attitudes (e.g., Bargh & Gollwitzer, 1994 ; Devine, 1989 ; Fazio et al., 1986 ; Kruglanski, 1996 ). Breaking Habits Through Planning Our studies are confined to the domain of travel behavior. However, although the present results cannot simply be generalized to all kinds of frequently performed goal-directed behaviors, it is worthwhile to speculate on possible ways in which planning can help to break harmful or undesirable habits (e.g., drinking too much, eating the wrong foods, and applying social stereotypes). After all, our results suggest that associations between goals and actions that arise from frequent co-activation (i.e., habits) can be simulated by planning. Is it possible to "replace" one association with a different association? Of course, planning can assist only on occasions in which the pursued goal can indeed be achieved by multiple actions (either objectively or subjectively assessed). If the habitual action is the only possible one, there is no alternative action one can or will plan. But let us restrict ourselves to goals that can be attained in multiple ways. As an example, imagine a person who always uses a car to travel from home to work and decides to use a bicycle instead. Can this person increase the probability of performing the counterhabitual intended action by planning this action? This is presumably dependent on the relative strength of the habitual association and the association that is the result of planning. It is likely that an association developed through planning can override a habitual association if the former is stronger. In recent treatments of action control (e.g., Norman & Shallice, 1986 ), it has often been argued that if multiple behavioral representations are activated, the one with the highest activation level will ultimately "win" the fight for dominance and guide overt behavior. This means that if goal activation leads to the activation of multiple behavioral representations (i.e., the habitual one and the planned one), the one with the highest activation level will guide overt behavior. The activation level of the alternative representations, in turn, is conditional on the strength of the associations between the activated goal and the different behavioral representation. An interesting avenue for further research would be to investigate how much planning is needed and what the content of the plan should be before planned behavior can override habitual behavior. Our results, which show that a little planning as to when, where, and how to achieve a goal is enough to create associations that are as strong as habitual associations (at least when the strength of these associations is measured soon after planning), are promising in this regard. Return of the Habit The concept of habit has a long-established history in theory and research. William James (1890) devoted an entire chapter to the concept of habit and the utility of habitual behavior. Early sociologists conceived of habits as behavioral patterns that serve as functional rules to control a society (e.g., Durkheim, 1893 ; see also Camic, 1986 ). They used a broad definition of habits to account for the stability of social institutions. The term habit was also used by theorists writing on evolutionary processes, who invoked the concept to denote the elementary behaviors of lower species. It was in this sense that Darwin (1859) wrote of such things as the "feeding habits of British insects." Darwin's work was related to the physiological literature of that time, revealing an interest in the movements of decapitated chickens, headless frogs, brainless cats, and the like. Later, the term was used for reflex actions, which were conceived as motor responses activated by nerve cells excited by stimuli external to the organism ( Fearing, 1930 ). In psychology, research on habits has long been dominated by behaviorist approaches to learning theory, typically providing a rather "mechanistic" account of the rise of behavioral responses (e.g., Skinner, 1938 ; Watson, 1914 ). That is, habits were merely conceptualized as automatic responses to stimulus cues with no consideration of the intervention of mental processes. However, this view was a bit rigid in the sense that it portrayed the actor as a victim of habitual programs, which seems rather unlikely from a functional point of view. As we have argued here, habits are goal directed, and their activation is dependent on goals (see also Hull, 1931 ; Tolman, 1932 ). People automatically enter the garage and take their car or bicycle only if there is a reason to do so (even though they may not be aware of this reason at the time of action). Lately, the term habit has rarely been used as an explanatory concept in the psychology of human behavior (some exceptions to this rule can be found in the introduction). In our view, understanding of mundane behavior and even behavior in general can benefit greatly from the psychology of habits Aarts, Paulussen, & Schaalma, 1997 ; Verplanken & Aarts, in press ). At this time, many researchers consider human behavior as being guided by reasoning. Accordingly, much effort is devoted to trying to explain various, if not all, actions by studying the relations among attitudes, intentions, and behavior. We believe, however, that although the emphasis on more reason-based and deliberate processes is helpful for an understanding of certain behaviors, it is not the only useful concept for insight into behavior in general. Much of what people do in daily life becomes highly automatized. In these cases, consciousness has delegated the onset and the proceeding of behavior to the unconscious. 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Habit, attitudes, and planned behaviour.(Is habit an empty construct or an interesting case of goal-directed automaticity? In W. Stroebe & M. Hewstone (Eds.), European review of social psychology. Chichester, England: Wiley.) Watson, J. B. (1914). Behavior: An introduction to comparative behavior. (New York: Holt) 1 In current research on the goal concept, different dimensions are postulated on which goals may vary, such as level of abstraction, difficulty, complexity, and temporal range (e.g., Austin & Vancouver, 1996 ; Gollwitzer & Moskowitz, 1996 ). Of course, these goal dimensions are not necessarily orthogonal. For the sake of argument, however, the focus here is on the functionality of relatively short-term goals. For example, an undergraduate's wish to travel to the university to attend lectures can be seen as a short-term goal, one that may be functional in achieving a long-term goal (e.g., earning money or becoming a professor). 2 One may remark that the goal priming condition and the control condition differ on more aspects than simply goal priming (e.g., some people did spend more time in the laboratory than others). However, such differences can have an impact on the main effect of goal priming but not the hypothesized, and critical, two-way interaction of habit strength and goal priming.
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