Changing Behavior by Memory Aids - CiteSeerX

Psychological Review 2009, Vol. 116, No. 2, 408 – 438

© 2009 American Psychological Association 0033-295X/09/$12.00 DOI: 10.1037/a0015512

Changing Behavior by Memory Aids: A Social Psychological Model of Prospective Memory and Habit Development Tested With Dynamic Field Data Robert Tobias EAWAG—Swiss Federal Institute of Aquatic Science and Technology This article presents a social psychological model of prospective memory and habit development. The model is based on relevant research literature, and its dynamics were investigated by computer simulations. Time-series data from a behavior-change campaign in Cuba were used for calibration and validation of the model. The model scored well in several system-analytical tests, including the replication of the data and the forecast of later developments based on earlier data. Additionally, the calibrated parameter values indicate that the accessibilities of intentions decay at the same rate as retrospective memories. However, the accessibilities may stay high due to a reminder, the effectiveness of which depends on a person’s commitment to performing the behavior. Furthermore, the effect of the reminder decays over time. This decay is much slower than the development of habits, which, after about a month, were nearly fully developed if the person had executed the behavior sufficiently often. Finally, over time, habits were shown to replace the reminding effect of the external memory aid. This article points to a new understanding of the role of habits in supporting the performance of repeated behaviors through remembering. Keywords: prospective memory, habit development, behavior change, computer simulation, time-series data

target behavior, the positive attitude of the persons toward the behavior, and the social impetus in favor of performing it. A popular explanation cites that habits hinder behavior change. On the other hand, if a campaign results in performance of the new behavior, people still often eventually return to their old behavior. The popular explanation for this tendency is that no habits were actually developed for the new behavior. Consequently, habits are mainly perceived as problematic for behavior change, and the metaphor of breaking habits is often used in behavior-change literature (e.g., Dahlstrand & Biel, 1997). This article examines the role of habits in behavior-change campaigns. The model presented and tested explains how habits develop and affect behavior. In contrast to a popular view, this article follows a new line of thinking on habits by regarding them more as support for goal-directed behavior than as its adversary (e.g., Wood & Neal, 2007). This article argues that habits do not affect behavior in the form of reflexlike automaticities that, after being activated by situational cues, are performed inevitably if not actively suppressed. The mechanism by which habits affect behavior is assumed to be by facilitating the remembering of habitual behavior. Therefore, changing habitual behavior is a problem of memory. In situations in which habitual behavior is performed, almost no cognitive resources are invested (e.g., Vohs, Baumeister, & Ciarocco, 2005). Only behaviors that can be remembered without any effort will be performed. Due to the memory-supporting effect of habits in such cases, habitual behavior will be performed without a person even realizing that another behavior was intended in this situation. As a consequence, people must be reminded about the new behavior to change habitual behavior in real-world settings.

Changing behavior might be one of the most important applied tasks of psychologists as inadequate individual behaviors can often lead to problems for an entire group of people or even to societal and global problems. The three common global approaches to changing individual behaviors are to (a) make it easier for the individuals to perform the target behavior or more difficult to perform competing behaviors, (b) convince individuals to perform the target behavior (e.g., Mosler, Schwarz, Ammann, & Gutscher, 2001), and (c) influence social networks or dynamics in cases where resources are unequally distributed or the decision to perform a behavior depends on the performance of this behavior by other persons (e.g., in organizing collective actions, as investigated by Tobias & Mosler, 2008). For all three approaches, effective techniques for changing behavior have been developed and successfully applied. Yet behavior-change campaigns still fail surprisingly often in spite of the ease of performing the

The campaign during which the data were gathered was initiated by Hans-Joachim Mosler (EAWAG) and implemented with the aid of the Universidad de Oriente in Santiago de Cuba. The data preparation was carried out by Martha G. Rodrı´guez Rodelo (EAWAG). The computer simulation was implemented by Christian Wu¨rzebesser (EAWAG). Some of the analyses were performed during a postdoctoral position at the Center for the Study of Institutional Diversity of Arizona State University. I thank Thomas Buchsbaum, Marco Janssen, Eike von Lindern, Hans-Joachim Mosler, Peter Reichert, Andrea Tamas, and Christian Wu¨rzebesser for helpful comments and discussion. The model was presented at the 2007 European Simulation and Modelling Conference in St. Julians, Malta. Correspondence concerning this article should be addressed to Robert Tobias, EAWAG, P.O. Box 611, 8600 Du¨bendorf, Switzerland. E-mail: [email protected] 408

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Remembering intended behaviors and methods to avoid forgetting them are investigated as prospective memory research. Prospective memory research (see McDaniel & Einstein, 2007, for an overview) investigates the circumstances that make remembering the behavior execution easy or difficult (e.g., Einstein & McDaniel, 1990; Marsh, Hicks, & Hancock, 2000) and also takes into consideration memory aids (Intons-Peterson & Fournier, 1986) and their effectiveness (e.g., Guynn, McDaniel, & Einstein, 1998). The research focuses on laboratory settings where subjects execute rather meaningless actions while performing distracting ongoing tasks within a specified time period. In fact, research in real-world settings over longer periods of time is scarce. Most field research investigates only differences in prospective memory performance between groups (e.g., Henry, MacLeod, Phillips, & Crawford, 2004, for an overview related to age differences; Heffernan & Bartholomew, 2006, and Heffernan et al., 2005, for the influence of drug use on prospective memory performance) or tasks (e.g., Kvavilashvili & Fisher, 2007, and Sellen, Louie, Harris, & Wilkins, 1997, for the difference between event-based and time-based tasks). These field studies show the limited external validity of laboratory experiments (e.g., Rendell & Craik, 2000, for principal differences between results from real-world-like laboratory experiments and actual real-world data) and the importance of situational cues and memory aids for remembering (e.g., Maylor, 1990). However, these studies did not investigate the processes that led to the differences in the prospective memory performance or the changes over time. Finally, field studies on prospective memory rarely consider behavior change. This article presents a field study that promotes waste separation using reminders as a behavior-change measure. In contrast to similar studies (e.g., Holland, Aarts, & Langendam, 2006), data were gathered in the form of time series on a daily basis for a month. To analyze these dynamic data, a comprehensive dynamic model was developed based on the literature. The model is designed to support planning and guiding campaigns in which memory aids are used. Because forgetting is a major problem in many everyday behavior situations such as taking medicines (Sheeran & Orbell, 1999) or shopping for specific products (Shapiro & Krishnan, 1999), memory aids are an efficient instrument to improve the success of a campaign. This article’s model comprises several assumptions not commonly considered in prospective memory research. Cognitive resources available for a prospective memory task are assumed to depend on situational circumstances and will vary for different performances of real-world behaviors. The performance of prospective memory tasks depends not only on the cognitive resources available for this task but also on the strength of the habits developed for the intended behavior. Furthermore, prospective memory performance depends on events and situational influences that, occurring prior to the moment of remembering the behavior, increase the accessibility of the target behavior. The effect of these events and situational influences depends on a person’s commitment to performing the behavior (referred to in prospective memory research as the subjective importance of the behavior). Of particular interest for this article are external memory aids or reminders. The effect of a reminder depends on time and location: Over time, the salience of a reminder decays, and its effect decreases. Similarly, if there is an increase in distance between where the intended behavior has to be remembered and the reminder, the

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reminder’s effectiveness decreases. Finally, it is assumed that intended behaviors are forgotten at the same rate as the contents of retrospective memory. The reason intended behaviors have been remembered more easily in some studies (e.g., Dockree & Ellis, 2001; Goschke & Kuhl, 1993) is that these memory contents are more important to a person and therefore are recalled more frequently and more effectively. In addition to these assumptions regarding prospective memory phenomena, the model presented here also includes some new ideas about habit phenomena. Habits are defined as slowly developing associations between situational cues and repeatedly performed behavior options. This concept is distinguished from other phenomena commonly related to habits. In many cases, affective connotations of behaviors that conflict with instrumental considerations are the cause of performing bad habits and not the aforementioned associations. In an actual situation of (mostly unconsciously) deciding on a behavior, often what one feels like doing is more important than what one thinks would be the best behavior. Other factors aside from memory limitations and the affective connotations of behavior may determine what behavior a person performs in a specific situation. The model considers the subjective feasibility of behavior options, convictions, norms, and tension states provoked, for example, by unsatisfied needs or cognitive dissonance. Given that all of these factors can lead to a repetition of past behaviors, the frequency of behavior performance is not enough to define the strength of habits. The model assumes that habits develop for behaviors performed in a specific situation and for similar behaviors in similar situations. Consequently, if a behavior is performed often, it might be remembered in other situations and performed even more often, resulting in positive feedback. However, if a behavior is performed too seldom, it might be forgotten more often and will then be performed even less, resulting in negative feedback. This article’s model leads to a new understanding of behavior change and the maintenance of change (e.g., Schwarzer, 2008, for a current overview). Behavior change can be blocked by memory limitations, and, in such cases, it is necessary to remind the person to perform a behavior at a critical moment. After a behaviorchange measure such as a persuasion event, reminders can be used to maintain that change. In some cases, a reminder can be enough to lead to the repetition of the new behavior. However, the effect of a reminder decays over time. Yet this decrease of the reminder effect can be compensated for by the development of habits. If strong habits develop, the new behavior will be performed so long as the situational circumstances remain unchanged. For the habits to develop, the reminder must lead to the frequent performance of the new behavior. To increase the effect of the reminder, further measures might be necessary, particularly those that increase the commitment to performing the behavior (e.g., implementation intentions, self-commitment, etc.). To conclude, the problem of changing daily behavior is not about old habits but about the lack of new habits. To overcome this problem, measures must be taken to focus on the new behavior until new habits are developed. In the first section of the article, the model is formulated as 10 principles and related to the relevant literature. Mathematical formalizations are proposed and implemented as a computer simulation (see, e.g., Gilbert & Troitzsch, 1999; Sun, 2008). In the second section, the first empirical test of the model is presented. The model replicates time series of measured behavior without any

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systematic errors and better than any simpler version of the model. Furthermore, in cross-validations, it was demonstrated that the model’s data-fitting abilities are generalizable and that later data can be forecasted based on earlier data. The model parameters can be estimated accurately and their values interpreted in a meaningful way supporting the assumptions of the model. In the third section, findings are related to the current state of prospective memory and habit research, and possible extensions of the model are also discussed for the purpose of future findings. Furthermore, some shortcomings of the study are addressed and a global conclusion drawn.

A Model of Prospective Memory and Habit Development The model can be formulated with 10 abstract principles, which are necessary and sufficient to explain the effect of memory aids on behavior performance, in the long run, in field settings. Figure 1 gives an overview of the 10 principles: Principles 1, 2, and 3 refer to general aspects of behavior selection; Principles 4 –7 define processes of forgetting and reminding; and Principles 8 –10 define processes of habit dynamics. The key concepts used in this article are defined in Table 1. The 10 principles can be summarized as follows. Principle 1— determinants of behavior execution. A behavior will be performed only if (a) it is possible, (b) it is remembered at that moment, and (c) the preference for its performance is higher than that for any competing behavior. Principle 2— determinants of behavior preference. The preference for a behavior is determined by (a) convictions and norms, (b) affective influences, and (c) tension states provoked, for example, by unsatisfied needs or requests. Principle 3— determinants of remembering a behavior. Whether a behavior is remembered depends on (a) its time-

dependent accessibility (i.e., ease of remembering), (b) the situation-dependent cognitive resources available, and (c) habits that foster remembering and also depend on situational cues. Principle 4 —forgetting. The accessibility of intended behaviors decays over time, which makes remembering their performance increasingly difficult at the critical moment. Principle 5—reminding by events. Accessibility can be increased by a variety of events (e.g., social interactions, information from mass media, or other circumstances activating associations with the intended behavior) that serve as reminders to perform the behavior. The effect depends partly on the person’s commitment to performing the behavior. Principle 6 —reminding by behavior execution. Accessibility is increased by performing the behavior. If a behavior is performed more frequently in specific circumstances, it is less likely to be forgotten. Principle 7—reminding by situational cues. Situational cues such as external memory aids increase the accessibility of a behavior, which increases the probability of remembering its performance at the critical moment. This effect is moderated by the person’s commitment to performing the behavior and decays over time. Principle 8 — habit decay. Habit strength decays over time. Habit effects in a particular situation weaken the longer a behavior is not performed in that situation. Principle 9 — habit gain. Habit strength increases by associating behavior performance with situational cues. The performance of a behavior in a particular situation is increasingly

Figure 1. Schematic overview of the principles used to define the model. Comprehensive principles defining the structure of the model are gray. Principles defining processes formalized in the model are black. The numbers refer to the numbers of the principles. Principles are explained in the text. Acc. ⫽ accessibility.

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Table 1 Definitions of Key Constructs Construct Accessibility Accessibility threshold Affective attitude Behavior frequency Behavior preference Commitment intensity Event Habit strength Instrumental attitude Salience Similarity function

Definition Ease of remembering. In laboratory experiments, often operationalized as time of reaction to a stimulus (where a faster reaction signifies a higher accessibility). In this article, accessibility refers only to the effect of past events in facilitating a memory task. Situation-specific influences are represented by the accessibility threshold. Threshold the accessibility must reach for a memory content to be remembered in a specific situation. This threshold represents situation-specific influences on a memory task. The effect of past events is defined by the accessibility. Affective connotation of a behavior (i.e., how much a person likes or dislikes performing a behavior). Must be distinguished from other affective influences such as expected affective consequences of a behavior or affective reactions to selecting, performing, or interrupting a behavior. Percentage of opportunities to perform a behavior, within a time step, in which the behavior is actually executed. Considers that each opportunity might occur in other circumstances or even involve other actions. Conscious and unconscious psychological factors influencing the selection of behavior alternatives that are perceived as feasible options and remembered. The feasible and remembered alternative with the highest preference is selected. The strength of any form of internal pressure felt by a person to perform a behavior. Can be the importance to the person of performing the behavior, the effects of forming implementation intentions, preparing the behavior execution, internalized or implicit requests, etc. Any momentary influence that increases accessibility (e.g., conversations with other persons, information or signs, performing certain behaviors, etc). Strength of the association between situational cues and a behavior. Behaviors with high habit strength can be performed without much thought and are seldom forgotten. Performing a competing behavior with lower habit strength instead of one with high habit strength is perceived as difficult. Degree to which it is felt “worth it” to perform a behavior or degree to which behavior is seen as helpful in attaining currently activated goals. Instrumental attitudes are determined by expectations, convictions, and norms. The amount a perceived object attracts attention and thus the chance the object has psychological or behavioral effects on a person. An object is more salient if it is big, colored, moving, strange, noise making, etc. Function of the behavior frequency expressing the similarity to a reference situation of the circumstances and actions of an opportunity to perform a behavior.

triggered by situational cues with the increased frequency of performance in that situation. Principle 10 — habit gain for actions not executed. Habits develop not only for the action performed in a specific situation but also for similar actions in similar situations. Stronger habits develop when the situations are increasingly similar. Many of these principles may seem trivial, and, in fact, some are often assumed without further discussion. Nevertheless, each principle is an assumption that must be founded and tested. Furthermore, it is shown that each principle is necessary and that all principles together are sufficient to explain empirical data. To allow quantitative testing of the model’s assumptions and to investigate the consequences of the interplay of all assumptions, the model presented in this article was formalized (i.e., the assumptions were formulated using mathematical equations to run computer simulations). Such formalization leads to an unambiguous, complete, and precise definition of the model. However, all equations used in this article have to be understood as pragmatic formalizations of the underlying principles. They are initial approximations that may need revision in the light of more detailed data. The model presented here can be understood as a pragmatic social psychological model designed for field investigations into the effects of behavior-change measures and campaign planning. Such a model needs to be relatively simple and robust to deal with the low quality of field data. However, all processes related to behavior-change measures used in a campaign, such as the reminders in the present case, must be explicitly represented. Finally, the model should be simple to interpret within campaign development decision-making processes.

To comply with these requirements, the model was designed as a highly abstract, time-discrete, and deterministic agent-based simulation (e.g., Mosler, & Martens, 2008, for a recent application). It represents the processes of individual persons, each of whom modeled as an agent. Considering the difficulty of gathering data, the model was designed on the highest abstraction level possible that still allows investigation of all relevant aspects of campaigns using memory aids. For the same reason and because a continuoustime simulation would require that the exact moment of each behavior performance and event be known, a time-discrete approach has been used. That is, the simulation runs in steps, in this case, of days. Finally, the model is deterministic. As such, it avoids assumptions about probability distributions, which are difficult to determine empirically. Instead, deterministic models are simpler to test and their results more easily interpreted.

Behavior Selection The model explains and predicts the effects of memory aids on behavior performance; however, memory processes are not the only influences on behavior. Therefore, the model must deal with determinants of behavior performance in general. Behavior is modeled here with the concept of behavior frequency defined as the percentage of opportunities to perform a behavior within a time step in which the behavior is actually executed. It is assumed that in opportunities in which the behavior is performed at a low behavior frequency, the behavior is also performed at higher behavior frequencies. Principle 1: Behavior execution is determined by feasibility, recallability, and preference. Three determinants exist for the execution of a behavior, namely, (a) its execution must be possible, (b) the behavior option must be remembered, and (c) the prefer-

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ence for its performance must be higher than for any competing behavior. Even though the first two determinants are obvious, they are often ignored in social psychological research. An exception is the theory of planned behavior (Ajzen, 1991), which considers the first two determinants with the construct of perceived behavior control. However, the focus of social psychological research is on explaining behavior performance based on the preference. Here, preference signifies the tendency to execute one behavior option over another if both are equally possible and remembered. As is explained in Principle 2, reflected intentions are only one aspect of this preference. Prospective memory research usually ignores the fact that behaviors might be remembered but not performed due to other reasons. According to Marsh, Hicks, and Landau (1998), the most important reason for not executing a planned behavior is the conscious changing of plans. This change might happen either because execution was not possible or because other behaviors became more important. For this reason, it is important to consider all three determinants of behavior execution even if a particular study has focused on only one of them. It is important to note that behaviors with very low preference might also be selected because no other feasible behavior option is remembered. Conversely, behaviors with very high preference might not be selected due to an alternative with an even higher preference. Determinants of behavior preference are discussed briefly in the next principle. The rest of this article focuses on the memory aspect of behavior selection. Principle 2: Behavior preference is determined by convictions, norms, affective connotations, and tension states. In this article, behavior performance is explained with memory processes. However, to contrast this approach with other approaches that explain behavior performance based on behavior preference, other determinants of behavior performance are discussed briefly. For example, social psychological approaches mostly focus on convictions and norms. Some of the most established theories are the theory of reasoned action (Fishbein & Ajzen, 1975) and its successor, the theory of planned behavior (Ajzen, 1991). Both use the attitude concept as a main explanatory factor, where attitudes consist of instrumental and affective appraisals (e.g., Breckler & Wiggins, 1989; Rhodes & Courneya, 2003). These should be considered separately as their correlation is often low and measures designed to change them are very different (Mosler, Tamas, Tobias, Caballero Rodrı´guez, & Guzma´n Miranda, 2008). In addition, their influence on behavior might differ depending on the actual conditions. To illustrate, Conner and Armitage (1998) proposed a two-process model of the attitude– behavior relation in which instrumental considerations only affect behavior if a person is able and willing to think about what behavior to perform. Thus, the model considers cognitive factors of the behavior preference (i.e., convictions and norms) and affective factors (i.e., the affective connotations of the behavior options). The former dominates the formation of the preference in situations with high cognitive involvement. In contrast, the latter has greater influence when the person is not willing or able to think much about what to do.1 Finally, cognitive tension states derived from unsatisfied needs, dissonance, requests, and so forth affect behavior. If sufficiently high, this influence can override all other previously mentioned factors. Therefore, high preferences for behaviors might originate

not only from positive attitudes but also from cognitive tension states. Habits are not considered a determinant of the behavior preference, but they do affect the behavior selection via memory processes, which is further explained in Principle 3. Nevertheless, past behavior does affect preferences. However, this is not due to associations of situational cues with behavior options but results from other causes, such as simple decision heuristics that use previous decisions as a default for the next decision in the same situation, strategic considerations targeting consistent behavior decisions, the development of norms or affective connotations, and so forth. To close this discussion of preference, two notes are added to avoid any misunderstanding. First, the model does not use any concepts such as consciousness or awareness. Thus, behavior selection based on preferences can be sometimes more or sometimes less conscious. Even if the majority of the influences on the preference can be made consciously, for example, when required in an interview, they will be mostly unconscious at the moment of behavior selection. Second, as mentioned in Principle 1, preference is a relative selection criterion. In addition, behaviors of very low preference can be selected if no feasible behavior option with a higher preference is remembered. Thus, memory effects can override the entire preference structure. Preferences are more a limiting factor for remembered (and feasible) behaviors to be performed in a situation. This also explains why habitual behaviors are performed even if other behavior options would be preferred (e.g., Danner, Aarts, & de Vries, 2007; Ji & Wood, 2007). Finally, this principle, together with Principle 1, emphasizes the multitude of factors that influence behavior performance. In particular, the repetition of a behavior might be due to (a) a lack of feasible alternatives, (b) the repetition of a previous decision, (c) certain decision heuristics, (d) the development of norms, (e) the development of affective connotations, (f) a strong commitment to performing the behavior, or (g) the development of habits that affect behavior selection via memory processes. Thus, the operationalization of any of these processes by the frequency of past behavior performances might be confounded by the other processes.

Forgetting and Remembering the Execution of Behavior Although the investigation of memory phenomena is one of the oldest areas of experimental psychology (e.g., Ebbinghaus, 1885), the investigation of memory effects on behavior execution— known as prospective memory research—is relatively new (first 1

Because habits have a maximum impact on conditions of minimal cognitive involvement, the argument that, under this condition, affective influences dominate the behavioral preferences might be seen as a contradiction to findings that show weaker affective reactions related to habitual rather than nonhabitual behaviors (e.g., Wood, Quinn, & Kashy, 2002). However, no such contradiction exists: The affective influences considered in the model are affective connotations of behaviors and not emotions provoked by selecting, performing, or interrupting behaviors. Under the lowest cognitive involvement, how much one likes or dislikes performing a behavior influences behavior selection the most. Under these conditions, the fewest affective reactions to the selection and performance of the behavior are to be expected.

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experiment by Loftus, 1971; for a recent overview, see McDaniel & Einstein, 2007). The model developed here considers only the literature dealing with the effects of situational cues, in particular, external memory aids such as notes or objects placed as reminders (Intons-Peterson & Fournier, 1986), and omits studies on the effects of aging and neuropsychological studies. Principle 3: Remembering depends on accessibilities, cognitive resources, and habits. At a specific moment, some behavior options are easier to remember than others. Yet sometimes even easy-to-remember options are forgotten, whereas, in other situations, behaviors that are usually difficult to recall are remembered. The ease of remembering is called accessibility (e.g., Higgins, 1996). The extent to which a behavior option must be accessible to be remembered depends on the cognitive resources available for memory tasks. These resources depend mainly on the situation in which a behavior should be performed but may also vary between persons. The need for cognitive resources to remember to execute a behavior has been intensively investigated in prospective memory research. Recent studies have indicated that prospective memory tasks always need cognitive resources (e.g., Marsh, Hancock, & Hicks, 2002; Smith, Hunt, McVay, & McConnell, 2007), although to a varying extent. This is captured in two-process models that comprise an increase in accessibility, which is automatic, and monitoring for cues, which requires cognitive resources (e.g., Guynn, 2003; Loft & Yeo, 2007; McDaniel & Einstein, 2000; McDaniel, Guynn, Einstein, & Breneiser, 2004; Smith & Bayen, 2004). The distinction between these two processes is essential for the present model. However, the processes are defined more generally based on the underlying dynamics. Accessibility dynamics do not involve any cognitive resources. More important for the present model is the fact that accessibilities do not vary with situational characteristics but decay over time. Although situational cues might increase accessibilities, removal of these cues does not result in the accessibilities returning immediately to their previous value. The accessibilities are contrasted with the accessibility of memory content (e.g., a behavior option) that must be reached to be remembered (henceforth referred to as accessibility threshold). This threshold varies directly with situational (and personal) characteristics. On one hand, these characteristics influence the cognitive resources a person is willing and able to invest in a memory task. On the other hand, situational cues can activate habits. The associative connections between a situation and a behavior facilitate the act of remembering this behavior in a situation in which it is often performed (Aarts, Verplanken, & van Knippenberg, 1998; Danner et al., 2007). The accessibility threshold therefore expresses to what degree situational characteristics facilitate or hinder remembering a behavior, whereas the accessibility represents the effect of past events on the present memory task.2 According to this model, there are three ways to remember a behavior at a critical moment. First, persons can exert themselves to remember to perform the behavior. However, for everyday behaviors, this effort usually cannot be kept up for longer periods, and the cognitive resources available for remembering a behavior are here assumed to be determined by the situation. Second, the accessibility might be increased, for example, by using a reminder. Such external memory aids increase the accessibility of the behavior and reduce the need for cognitive resources (e.g., Marsh et al., 1998). No cognitive resources are necessary to retrieve a behavior

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option (Loft & Yeo, 2007; McDaniel et al., 2004) unless the reminder is not optimally effective—as may be the case in most real-world settings. In such cases, remembering a behavior might still depend on the availability of cognitive resources. Third, after several performances of the behavior in similar circumstances, the behavior becomes habitual, and the habits facilitate remembering by lowering the accessibility threshold. If strong habits are developed, the behavior might be remembered without the need for any cognitive effort or memory aids. The assumption of situation-specific cognitive resources needs some further consideration because of potential problems when applying the model to real-world settings. In such investigations, it is difficult to track each and every behavior performance and situation. A more abstract conceptualization that does not refer to specific situational conditions is preferable. A solution might be to relate the cognitive resources to the frequency of behavior performance. The idea behind this concept is that the situations in which a behavior is performed vary according to the cognitive resources available, particularly considering distractions. It is reasonable to assume that if a person seldom remembers a behavior, remembering will only happen in circumstances where it is easy (i.e., situations with almost no distractions but where many cognitive resources are therefore available for the memory task). Conversely, if a behavior is performed more often, the likelihood increases that some performances occur in circumstances where remembering is more difficult due to a lack of cognitive resources, for example, in the presence of more distractions. This conceptualization does not necessarily mean that an increased frequency of behavior performance makes it more difficult to remember. In fact, in Principle 6, it is shown that the contrary might be the case: Behavioral performance increases the accessibility, and the more often a behavior is performed, the easier it is to remember it at a critical moment. The point here is that more frequent behavior performance increases the likelihood the behavior will have to be remembered in circumstances where remembering is more difficult. Performing a behavior often facilitates remembering, but situational circumstances might hinder remembering, particularly if the behavior is performed frequently. The general discussion of this model outlines some ideas of circumstances that lead to the dominance of one of these influences over the other. The three concepts mentioned in this principle—accessibilities, cognitive resources, and habits—are considered in the computational model. As discussed, the situational influence on the cognitive resources available for the prospective memory task is modeled as a function of the behavior frequency. For a behavior frequency BF to be remembered, its accessibility must be greater than or equal to the accessibility threshold calculated as follows: 2 Because investigations of accessibility dynamics never consider habits, including them might lead to some confusion about construct labels. The label accessibility was used because all evidence on this construct uses this label. However, as habits reduce the cognitive effort to remember a behavior, in a strict sense, habits increase accessibility because they make remembering easier. Yet, because habits are clearly situation dependent, they form part of the accessibility threshold, as defined above. The concept of accessibility as used here refers to only one type of ease of remembering, namely, the one increased by events and that decays over time.

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414 ThresholdBF ⫽ CAT ⫹ WBFAT ⫻ BF ⫺ WHAT ⫻ HSBF.

(1)

This equation consists of two parts: (a) the influence of the cognitive resources (CAT ⫹ WBFAT ⫻ BF ) and (b) the influence of habits (WHAT ⫻ HSBF). The influence of the cognitive resources is modeled with a constant (CAT) and a linear function of the behavior frequency BF. The dependency of accessibility threshold on behavior frequency means that more frequent behavior performance creates an increased likelihood that some performances take place in circumstances where remembering is more difficult due to a lack of cognitive resources (e.g., with more distractions). Therefore, this influence is added to the threshold. The strength of the influence of circumstances that make remembering more difficult is set with the weight WBFAT. Given that habits (HSBF) make remembering easier, they are subtracted. Again, this influence is weighted (WHAT). The argument for this principle is that a behavior option’s accessibility that is increased by events and then decays over time must be distinguished from its accessibility threshold, which depends on situational characteristics. These situational characteristics determine the cognitive resources available for the memory task and activate habits that foster remembering. The latter in particular is focused upon when investigating this principle, as habits are not usually considered in prospective memory research. Principle 4: The accessibilities of intended behaviors decay over time. It is generally assumed that, as time passes, it becomes more difficult to remember to perform a behavior. Even though experience and a long line of memory research make this principle appear obvious, some studies actually indicate that intended behaviors are not or are only very slowly forgotten over time. For example, Goschke and Kuhl (1993; similarly, Dockree & Ellis, 2001) stated that the accessibilities of intended behaviors are higher than those of not-intended behaviors. As a possible explanation, they mentioned a slower decay of the accessibilities of intended behaviors (than those of not-intended ones). In Hicks, Marsh, and Russell (2000), increasing retention intervals even led to a higher prospective memory performance. However, these studies did not consider that accessibilities not only decay but also increase due to events and cues. Here, accessibilities of intended behaviors are assumed to decay at the same speed as any other cognition, but they are more frequently increased by events or cues related to these behaviors or by active internal processes that keep the accessibilities high (e.g., recollections, Ellis, 1996; rehearsals, Kvavilashvili, 1987). This view corresponds with Goschke and Kuhl’s (1993) second explanation and is also consistent with Marsh, Hicks, and Watson (2002), who stated that, when prospective memories are kept more accessible, cognitive resources are drawn off from the ongoing task. In very short retention intervals as used by Hicks et al. (2000), accessibilities increase more than decay, leading to a higher performance after 15 min than after 5 min. However, this result cannot be generalized to the expectation of a better memory performance, for instance, after 3 days than after 1 day. Freeman and Ellis (2003) also mentioned the possibility that, in the case of very simple and specific tasks, the higher accessibilities of prospective intentions after a short delay “might reflect the operation of covert motoric or SPT-type3 encoding or rehearsal operations aimed at preparing these actions for imminent execution” (p. 227).

Many possibilities exist to formalize the assumption of decaying accessibilities. Rubin and Wenzel (1996) fitted 105 different functions to 210 data sets and concluded that a logarithmic or power function might best describe empirical forgetting curves. However, these functions have two major shortcomings. First, they are purely descriptive and do not refer to any underlying dynamic process. Second, they describe the decay over time from when the subjects learned the material perfectly. In the present case, many different events are expected to increase accessibility a little each time. Therefore, a formalization was preferred that represents a plausible process in a simple form and does not require monitoring of the many times an accessibility-increasing event occurred. The simplest form of such a process is a proportional decay (for more complex equations, see, e.g., Higgins, 1996). This means that higher accessibility denotes quicker decay. Mathematically, this is formulated as follows: AccDecay ⫽ Accessibility ⫻ ADP,

(2)

where ADP is the accessibility decay parameter that determines the speed of the decay. The dynamics of accessibility decay are visualized in Figure A1, in Appendix A. Figure A1 also shows that ADP for retrospective memory contents is about .84. Therefore, if it is true that prospective memories decay slower than retrospective memories, ADP should be calibrated to a value considerably smaller than .84. To conclude, accessibilities of intended behaviors are assumed to decay at the same speed as accessibilities of other memory content. This decay is assumed to be fast shortly after an accessibility increase (i.e., while accessibility is high) and then slower when accessibility is lower (i.e., the more time that has passed since being reminded). Principle 5: Events increase the accessibility. As mentioned in the discussion of Principle 4, both accessibility decay and events that increase accessibilities are considered for the model. At this point, the concept of an event is intentionally kept vague as it comprises any momentary influence that increases accessibility. Obviously, for empirical investigations, the events must be clearly defined and based on evidence. Examples of events are conversations with other persons, information or signs, performing certain behaviors, and so forth. One well-investigated event is the establishment of the event-based target cue, particularly setting up an external memory aid (Goschke & Kuhl, 1993; Intons-Peterson & Fournier, 1986) or planning the execution of a behavior (Guynn et al., 1998). In fact, the moment when a person definitively decides to execute a behavior and plans the specific details of action in a particular situation is the focus of an entire research area. This decision, or, more precisely, the “linking [of] a certain goaldirected behavior to an anticipated appropriate situational context” (Gollwitzer & Brandsta¨tter, 1997, p. 187), is termed an implementation intention (e.g., Gollwitzer, 1999). Research on implementation intentions focuses mainly on behavior preference (Principle 2) and, to a limited extent, includes the effects of situational cues (Principle 7). Nevertheless, some investigations have shown that the extent of the preparation activity (Rise, Thompson, & Verplan3 SPT stands for subject performed task (Cohen, 1981). Under this condition, the actions to be memorized are not only read but also enacted during encoding.

CHANGING BEHAVIOR BY MEMORY AIDS

ken, 2003) and the strength of the commitment to behavior execution (Gollwitzer, 1999) will modulate the effect of this event. In addition, when prospective memory tasks are accorded greater importance, performance improves (e.g., Kliegel, Martin, McDaniel, & Einstein, 2007). These influences are merged into one variable—commitment intensity. Here, commitment intensity is defined as the strength of any form of internal pressure to perform a behavior.4 This pressure can lead to cognitive tension states and thus affect the preference according to Principle 2. However, this study focuses on the moderating effects of commitment intensity on memory processes. It is assumed that a higher commitment intensity (i.e., greater importance of behavior performance, increased strength of implementation intentions, etc.) entails a stronger reminding effect of events and situational cues (the latter being discussed in Principle 7). The findings can be formalized with the following equation: AccGainEvent ⫽ AGCEvent ⫹ WCIEvent ⫻ CI.

(3)

In one time step (e.g., a day) during which an event takes place, accessibility increases by a constant, AGCEvent, independent from the commitment intensity (CI ) and a certain proportion (WCIEvent) of the commitment intensity. Given that no other events have been investigated, all types of events are assumed to operate in an identical manner. However, each event may have other parameters (i.e., another constant and weight for the commitment intensity). By modeling an event every day and calibrating the parameters of its effect individually, any data can be fitted. Therefore, the point events must be considered restrictively to test the cognitive processes model and to produce forecasts. In brief, Principle 5 captures that temporary circumstances can increase the accessibilities of intended behaviors and that this increase depends partly on commitment intensity. Principle 6: Performing the behavior increases the accessibility. Another type of event is the execution of the behavior itself. In an experiment by Hacker, Herrman, Pakossnik, and Rudolf (1998), students had to note the context of certain words during classes. Their performance improved as the number of trigger words increased such that the task to be remembered was executed more often. This indicates that more frequent behavior performance facilitates remembering because accessibility is increased by behavior execution. The accessibility-increasing effect of behavioral performance is considered in the model differently than the events introduced in Principle 5. The point of Principle 6 is that performing a behavior facilitates remembering it, which increases the frequency of performing it. As explained in Principle 3, if a behavior is performed more frequently, it becomes more likely that the behavior has to be remembered in circumstances in which performing memory tasks is more difficult. Thus, an increased frequency of behavioral performance does not always facilitate remembering. For the purpose of formalization, the gain in accessibility due to behavior performance (AccGainBeh) must depend on the behavior frequency. For the sake of simplicity, the same function (WBFAT ⫻ BFExe) is used here as was used for the accessibility threshold (see Equation 1). The function is weighted by the parameter WBFAGBeh , which defines whether frequent behavior execution either facilitates remembering (WBFAGBeh ⬎ ADP) or hinders it (WBFAGBeh ⬍ ADP). Furthermore, a constant (AGCBeh) is added that is not calibrated but is rather calculated to stabilize the start behavior frequency (sBF). De-

415

pending on the frequency of the executed behavior (BFExe), the accessibilities of all behavior frequencies are increased by the same amount of AccGainBeh , defined as AccGainBeh ⫽ AGCBeh ⫹ WBFAGBeh ⫻ WBFAT ⫻ BFExe.

(4)

The main argument of Principle 6 is that behavior execution increases the accessibility of the behavior, thus making it easier to remember. However, according to Principle 3, a higher frequency of behavior performance signifies an increased likelihood that its performance took place in conditions that make remembering more difficult. Thus, behavior frequency can be either beneficial or obtrusive to remembering intended behaviors depending on which influence dominates. Principle 7: Situational cues increase the accessibility depending on the commitment. The effect of situational cues, particularly from an external memory aid, can be modeled in the same manner as the effect of events in Principle 5. However, the effect of situational cues does not depend so much on time, given their usual permanence in place, but rather on the location of the behavior performance relative to the location of the situational cue or memory aid. For example, if waste is mostly separated in the kitchen, a reminder placed in the kitchen will have a greater effect than one placed in the bathroom. The model considers the location where the behavior performance has to be remembered (i.e., how far away from the reminder the behavior is to be performed). However, this theoretically simple assumption leads to problems with empirical investigation in real-world settings. In such studies, the exact location of behavior performance is usually unknown, and a further assumption is added to the model, namely, that when a behavior is performed more often, it is more likely performance sometimes happens far away from the reminder. More specifically, for the times the intended behavior is performed close enough to the reminder, the effect of the reminder is strong. If a behavior is performed frequently, there will be many times that the behavior has to be remembered so far from the reminder that its effect is similarly small for all of these behavior performances. Finally, a set of behavioral performances will happen at a distance from the reminder where the effect of the memory aid varies greatly for each behavior performance. A logistic function can thus be used to describe the relationship between the number of behavioral performances and the effect of a reminder. The computational model uses the following equation:

4 Commitment intensity is not related to attitudes. While attitudes express how favorably a behavior is perceived, the commitment expresses an internal pressure or subjective importance of performing a behavior. Behaviors appraised with negative attitudes might be seen as important (e.g., paying taxes), while positively appraised behaviors might be perceived as not important (e.g., watching TV). Only in cases where persons have to explicitly commit themselves (e.g., in the form of a public selfcommitment or explicitly forming an implementation intention) is a correlation between attitudes and commitment expected, due to the fact that persons with negative attitudes will not accept such an explicit commitment.

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416

冢

1

冣

1 1 ⫹ e0.5⫻SS 1⫹e Sf共BF 兲 ⫽ 1 ⫺ ⫻ DP . 1 1 ⫺0.5⫻SS ⫺ 0.5⫻SS 1⫹e 1⫹e 共0.5⫺BFTS兲⫻SS

⫺

(5)

This function is called the similarity function, Sf(BF), because it defines the degree to which the situations of performing a behavior with a certain frequency are similar to performing the behavior right at the location of the reminder. The core of this equation is the upper left division: a logistic function of BF with the turningpoint parameter TS (defining the behavior frequency at which the steepest point of the function is located) and the slope parameter SS (defining the maximal steepness of the function). The rest of the multiple-division operation is a linear transformation to assure that the function runs through 0 and 1, for a behavior frequency of 0 and 1, respectively. It is a pure mathematical necessity without any substantial meaning. The function is multiplied by the dissimilarity parameter, DP, that defines the maximal dissimilarity of situations. This expression is subtracted from 1 to set the function to 1 for a behavior frequency of 0. Due to the linear transformations, Equation 5 is complicated. However, the tendency expressed by this equation is simple and graphically represented in Figure A2 in Appendix A. In addition to the location of the behavior performance, the characteristics of the reminder must be captured in the model. Considerable research has been carried out to determine the effectiveness of external memory aids. Guynn et al. (1998) concluded that effective reminders refer to the behavior and the situation in which it is to be executed, or the reminder should be set up at the places where the behavior is to be executed. These reminders then activate the association between the behavior and the situation. Other studies, including Marsh et al. (2000), have focused more on the salience of the situational cues that lead to stronger effects. This aspect of salience bears a further characteristic of reminders, as, over time, the salience of any cue should decay as it becomes familiar. The quality of the reminder and its salience depend not only on the reminder but also on subjective experience (Smith et al., 2007). Finally, as explained in Principle 5, a reminder alone will not have much effect if there is no interest in performing the behavior. The activation of the association between a situational cue and a behavior also depends on the intensity of commitment (i.e., the importance of the behavior performance to the person, the strength of implementation intentions, etc.), as investigated in the area of implementation intentions (e.g., Chasteen, Park, & Schwarz, 2001; Orbell, Hodgkins, & Sheeran, 1997). Overall, considering situational characteristics, characteristics of the reminder, a decaying salience, and the influence of the commitment, the effect of a reminder on the accessibility of an intended behavior can be formalized in the following way: AccGainRem ⫽ 共AGCRem ⫹ WCIRem ⫻ CI 兲 ⫻ Sf 共BF 兲 ⫻ SalienceRem.

(6)

For every time step a reminder is in place, the accessibility of the intended behavior increases by AccGainRem. This effect depends on the accessibility gain constant of the reminder AGCRem , which models the general quality of the reminder. For an unremarkable reminder, AGCRem will be close to 0, whereas, for an eye-catching reminder, this constant should have higher values.

The extent to which the commitment intensity CI influences the effect of the reminder is controlled by the weight of the commitment intensity, WCIRem. The effect of the reminder then depends on the distance from the reminder at which the behavior is performed such that this influence of behavior-performance location is expressed with the similarity function Sf(BF) (see Equation 5). Finally, the salience of the reminder, SalienceRem, decays over time, making the reminder less effective the longer it is used. The salience is always set to 1 at the moment of setting up the reminder, and then a proportional decay is assumed equal to the decay of accessibilities (see Equation 2). However, the salience of the reminder decays at a different rate, which is set by the salience decay parameter SDPRem: SalienceDecay ⫽ Salience ⫻ SDPRem.

(7)

To maintain the simplicity of the model, the formalization in this article does not consider that the quality of the reminder and its salience depends partially on subjective experience (Smith et al., 2007). If necessary, this aspect can easily be considered by changing the parameters from being globally specified to those that can be set to different values for each case. This principle can be summarized as four key points: (a) The extent to which a memory aid is a reminder for behavior performance depends on the distance from the reminder at which the behavior is performed (expressible at an abstract level using a logistic function of behavior frequency), (b) the effect of a reminder depends on its quality and on (c) the commitment to perform the behavior, and (d) the effect of a reminder decays over time.

Development of Habits Although situational effects on behavior have been of great interest in behaviorist psychology (e.g., Skinner, 1938; Watson, 1913), the investigation of habits is a relatively new but booming topic in cognitive psychology (for a recent overview, see, e.g., Wood & Neal, 2007). Although several directions of research can be distinguished, the extensive amount of neurological research (e.g., Packard & Knowlton, 2002; Squire, Stark, & Clark, 2004; Yin & Knowlton, 2006) is not discussed due to its only tangential relevance to the current model. The literature review focuses on social and cognitive psychological research. Social psychological research mainly focuses on investigating the power of the habit construct to explain behavior (e.g., Bamberg, Ajzen, & Schmidt, 2003; Mittal, 1988; Verplanken & Melkevik, 2008) and on practical aspects of methods of changing habits (e.g., Dahlstrand & Biel, 1997; Eriksson, Garvill, & Nordlund, 2008; Fujii & Ga¨rling, 2005). Even though this line of research illustrates the importance of this construct and presents ideas related to dealing with habits practically, no information is provided about the actual processes by which habits impact behavior and how they develop over time. The only information that could be found regarding the development of the strength of habits is very imprecise. For example, Breckler and Wiggins (1989) estimated that a behavior has to be repeated between 12 and 15 times for a habit to be established, whereas Ronis, Yates, and Kirscht (1989, p. 213) estimated that at least 10 repetitions need to be performed, twice a month, to achieve this end. Likewise, Ouellette and Wood (1998) assumed that a behavior has to be executed at

CHANGING BEHAVIOR BY MEMORY AIDS

least weekly or even daily, always in the same situation, to develop a regular habit. Still, behaviorist research suggests that the effect of reinforcement learning initially leads to fast gains and then increasingly slower ones until a maximum performance is reached after about 14 days (Tolman & Honzik, 1930, p. 267). On the other hand, cognitive and clinical psychological research focuses on describing and modeling very specific phenomena related to habits, routine behavior, or implicit learning (e.g., action slips during simple tasks such as making a cup of coffee or reaction times to specific problems). The models that were developed to replicate, integrate, and explain the findings give a good overview of this field (e.g., Botvinick & Plaut, 2004; Cooper & Shallice, 2000; Kruschke, 2001; Sun, Slusarz, & Terry, 2005). However, these models are too complex for the data that can be gathered in field studies and focus on details abstracted by the model developed here. The fundamental principle behind these models is that of associative learning, that is, increased later activation of behavior by certain situational cues when the frequency of the behavior performance in the presence of the cues increases. If the behavior is not performed in the presence of these cues for some time, then the associations decay. Some models also consider the similarity between cues and behaviors that lead to similar activations. Therefore, to maintain a parsimonious model corresponding to the data available in field studies, it is necessary to consider the decay and increase of associations between cues and behavior, as well as the effect of similarities between different cues and behaviors. Principle 8: Habit strength decays over time. There seems to be an implicit consensus that habits disappear if a behavior is not performed for a certain period of time. The model presented here assumes a proportional decay of habit strength (as for the accessibility decay in Equation 2 and the decay of the Salience of the reminder in Equation 7). The speed of habit strength decay is controlled by the habit decay parameter, HDP: HabitDecay ⫽ HS ⫻ HDP.

(8)

As explained in Principle 4 and illustrated in Figure A1 in Appendix A, this formulation leads to a fast decay when habits are strong and a slow decay when they are weak. It is expected that HDP will be calibrated to a much smaller value than the accessibility decay parameter given that forgetting occurs much more rapidly than habit development. Principle 9: Habit strength increases by associating behavior performance with situational cues. An increase in habit strength is commonly understood as the increase that occurs in the strength of association between some situational cue and the behavior with each execution. Thus, every time a behavior is performed, a certain value is added to the habit strength. The combination of such a constant increase with a proportional decay, as assumed in Principle 8, then leads to the observed early rapid increase in habit strength, which later slows down. Because a varying number of behavior performances take place within a time step of the model, the increase in habit strength also depends on the behavior frequency. To scale the habit strength for any behavior frequency between 0 and 1, the increase in habit strength further depends on the habit decay parameter and on the already-achieved habit strength, which leads to the following equation: HabitGainExe ⫽ 共HS ⫻ 共1 ⫺ BFExe兲 ⫹ BFExe兲 ⫻ HDP.

(9)

417

Equation 9 expresses a linear relation between the increase in habit strength and the executed behavior frequency, BFExe. The greatest possible increase in habit strength is the value of the habit decay parameter HDP. Thus, habit strength can never exceed 1. To also allow lower behavior frequencies to reach the maximum value of the habit strength, habit strength, HS, can compensate for the low behavior frequency. Thus, the model postulates two methods for obtaining maximum habit strength: performing the behavior (a) frequently or (b) regularly over a long period of time. This principle states that habits develop more slowly both when the behavior is performed with low frequency and immediately following a behavior change or increase in behavior frequency. Principle 10: Habits also develop for similar actions in similar situations. Principle 9 above describes the means by which habit strength increases for actually executed actions. The question remains whether these action executions have any effect on the strength of habits related to other actions in other situations. Many authors have noted that the development of habits depends on the similarity between actions and situations (e.g., Botvinick & Plaut, 2004; Ouellette & Wood, 1998). This means that, although a certain action is not actually performed in a certain situation, its habit strength may increase if a similar action is performed in a similar situation. A related and well-investigated phenomenon is behavior shaping (Skinner, 1953, 1958; as an example of a more recent study, see Eckerman, Hienz, Stern, & Kowlowitz, 1980). Although behavior shaping does not exactly pertain to the development of habits, both paradigms investigate the development of associations without the use of cognitive resources. For behavior shaping, it has been shown that a change in behavior can build on associations developed for similar behaviors in similar situations. Similar relations have also been found for cognitively more demanding processes such as those investigated in the area of far transfer of learned material (see Barnett & Ceci, 2002, for an overview). Namely, an increased number of differences between the training situation and the situation in which the learned material is applied entail a lower probability of application. These studies also propose dimensions on which the similarity of situations might vary; for habit development, the dimensions of physical, temporal, and social context are of particular importance. Similarly, Belk (1975) identified five types of situational characteristics: physical surrounding, social surrounding, temporal perspective (time at which a behavior is performed), task definition (intentions or roles related to a behavior), and antecedent states (e.g., moods). The most general and accurate approach to similarity is feature matching (Tversky, 1977). A situation can be described with features where the number of features shared by two situations signifies the degree of similarity between them. Sharing a greater number of features denotes greater similarity between the two situations (Forbus, Gentner, & Law, 1995; Thorndike, 1906). Conversely, having fewer features in common implies less similarity between situations (see also Botvinick & Plaut, 2004, for the importance of considering not only similarities but also differences). Furthermore, which features are to be considered depends on the attention devoted to them (Kruschke, 2001). Even though this approach might be interesting for laboratory experiments, it is not suitable for field studies because all features of all situations of

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418

all investigated persons can never be known (Barnett & Ceci, 2002). Such a model would be far too complex. A much more idealized approach, similar to the one used in Principle 7, is applied here. As for the different situational effects on accessibility, similarity is expressed as a function of the frequency of behavioral performances. If a behavior is performed infrequently, similarity exists between the situations in which this happens and the actions involved in the behavior performance. When a behavior is performed more often, there is an increased likelihood that this might happen in situations with different characteristics and involve different actions. Similarity, then, decreases as the frequency of behavior performance increases. Again, a logistic function describes the relationship between the frequency of behavior performance and the similarity of situational and motor characteristics of the performances. If the motor characteristics are identical for all behavior performances, the same function can be used as for the definition of situational differences (Principle 7). Thus, the effect of performing a behavior in a specific situation on habit strength for performing it in another situation is directly proportional to the similarity of these situations. Formally, this can be expressed as follows: HabitGainBF ⫽ min共1, max共0, Sf 共BF兲 ⫹ 共1 ⫺ Sf 共BfExe兲兲兲兲 ⫻ HabitGainExe.

(10)

The similarity function (see Equation 5) is transposed to run through 1 for the executed behavior frequency. Values greater than 1 are set to 1, and values less than 0 are set to 0. The resulting value is then multiplied by the increase in habit strength for the executed behavior frequency, as calculated in Equation 9. The tendency described by this equation is illustrated in Figure A3 in Appendix A. The consequence of this principle is quite dramatic but can easily be tested. If habit strength increases for similar situations and if habits lower the accessibility threshold as stated in Principle 3, after a while, the frequency with which a behavior is performed

can increase without outside interference. If the reminder can stabilize a sufficiently high frequency of behavior performance, habit strength will not only increase for the situation in which the behavior is performed but will, according to Principle 10, increase for similar actions and situations. This lowers the accessibility threshold, according to Principle 3, which leads to remembering the behavior more easily in other situations. The behavior might then be performed in these somewhat different situations, which again increases the habit strength for actions in even other, different situations, and so forth. However, this positive feedback can develop into negative feedback due to the effects described in Principle 9. If the behavior is performed too seldom, then habit strength decays more rapidly than it increases. The decreased habit strength results in the behavior being performed even less frequently, which reduces the habit strength even more, and so forth. This leads to empirical cases where the behavior frequency increases relatively late after the reminder is set up and other cases where the behavior frequency stays stable on a low level for a period and then decays completely to zero.

Overview Summary of the model. To summarize the 10 principles and their formalizations, a schematic layout of the model is given in Figure 2 (where blocks represent the equations of the computational model), and the model’s parameters are compiled in Table 2. The basic idea of the model is that the ease of remembering a certain behavior option may vary (i.e., a behavior’s accessibility may vary) and that the extent to which a behavior must be accessible to be remembered also depends on other factors. The rhombus in the center of Figure 2 symbolizes that, if the accessibility is greater than or equal to a threshold value, a behavior option is remembered; otherwise, it is not (Principle 3). Of all remembered behavior options, the one with the highest preference is selected (Principle 1). Accessibilities are assumed to decay over time (Principle 4, AccDecay) and are increased by (a) events

Figure 2. Schematic diagram of the prospective memory model. Blocks contain equations. Arrows are variables. The diagram is explained in the text. AG ⫽ accessibility gain; BF ⫽ behavior frequency; CI ⫽ commitment intensity; HS ⫽ habit strength; Beh ⫽ behavior; Rem ⫽ reminder; Acc. ⫽ accessibility; Eq. ⫽ equation.

CHANGING BEHAVIOR BY MEMORY AIDS

419

Table 2 Global Parameters (Set to the Same Value for All Cases) of the Model in Alphabetical Order, With Their Abbreviations, Ranges, and Calibrated Values Abbreviation

Parameter

Range

Value

SD

r

ADP AGCEvent1 AGCEvent2 AGCRem CAT DP HDP SDPRem SS TS WBFAGBeh

Accessibility decay parameter Accessibility gain constant for event of setting up reminder Accessibility gain constant for event of interviewer visit Accessibility gain constant for effect of reminder Constant of accessibility threshold Dissimilarity parameter of the similarity function Habit decay parameter Decay parameter of the salience of the reminder Slope parameter of the similarity function Turning-point parameter of the similarity function Weight of behavior frequency in accessibility gain due to executing the behavior Weight of behavior frequency in accessibility threshold Weight of commitment intensity in accessibility gain for event of setting up reminder Weight of commitment intensity in accessibility gain for event of interviewer visit Weight of commitment intensity in accessibility gain for effect of reminder Weight of habit in accessibility threshold

[0, 1] [0, 1] [0, 1] [0, 1] [0, 1] [0, 1] [0, 1] [0, 1] [0, ⬁) [0, ⬁) [0, 1]

0.760 0.615 0.269 0.037 0.504 0.790 0.082 0.028 19.522 0.523 0.851

0.098 0.098 0.115 0.062 0.078 0.089 0.027 0.020 3.112 0.045 0.183

.084 .210 .125 .226 .332 .465 .391 .204 .015 .384 .506

[0, 1] [0, 1]

0.446 0.757

0.120 0.165

.237 .309

[0, 1]

0.540

0.170

.150

[0, 1]

0.578

0.132

.145

[0, 1]

0.515

0.075

.129

WBFAT WCIEvent1 WCIEvent2 WCIRem WHAT

Note. Column SD gives the standard deviation of the parameter values of optimizations with similarly good fit. Column r holds the correlations of the model fit (root-mean-square error) to the differences of the parameter values, to the values of the parameter set with the best fit.

(Principle 5, AccGainEvent), (b) performing the behavior (Principle 6, AccGainBeh), and (c) situational cues such as reminders (Principle 7, AccGainRem). Two accessibility gains (AccGainBeh and AccGainEvent) depend on behavior frequency, and two (AccGainEvent and AccGainRem) depend on commitment intensity. These two parameters are the only parameters of the model that can have different start values for each case (sBF or CI). Note that the simulation always starts with a commitment intensity of zero, and the start value of the commitment intensity is only set at the moment the reminder is established. The complete equation for updating the accessibility is as follows: Acct⫹1 ⫽ Acct ⫺ AccDecay ⫹ AccGainEvent ⫹ AccGainBeh ⫹ AccGainRem.

(11)

In every time step of the simulation, the accessibility decays according to Equation 2 (Principle 4). If an event happens in the time step, the accessibility is increased according to Equation 3 (Principle 5) and, if the behavior is performed, according to Equation 4 (Principle 6). Finally, if a reminder is in place, the accessibility is increased according to Equation 6 (Principle 7). Moreover, all accessibility increases can occur in one time step and have a cumulative effect. Furthermore, it must be considered that the reminder’s salience decays according to Equation 7. This decay is calculated prior to the determination of the accessibility for the next time step, according to Equation 11. The accessibility threshold depends on the cognitive resources available for the memory task, which in turn depends on characteristics of the situation in which the behavior has to be remembered. Given that the situational characteristics of real-world behavior performances are usually unknown, the computational model uses a function of the behavior frequency to represent

situational influences. With higher frequencies of behavior execution, the likelihood increases that some performances take place under circumstances where remembering is more difficult (Principle 3). Habit strength decreases the accessibility threshold (Principle 3), decays over time (Principle 8), and is increased by the execution of the behavior (Principles 9 and 10). The complete equation for habit dynamics is as follows: HSt⫹1 ⫽ HSt ⫺ HabitDecay ⫹ HabitGain.

(12)

In every time step, habit strength decays according to Equation 8 (Principle 8) and increases according to Equations 9 and 10 (Principles 9 and 10), if the behavior is performed. Preferences, according to Principle 2, are assumed here to be constant, but data were gathered to test the correctness of this assumption. Hypotheses derived from the model’s assumptions. To allow a quantitative test of the model’s assumptions, hypotheses were derived from the principles, and the falsification criteria were defined based on the computational model. Hypothesis 1 (Principles 1 and 2): Given that behavior selection depends on several factors, behavior frequency can systematically vary independent of the behavior preference being measured as instrumental or affective attitude. This hypothesis is falsified if the behavior dynamics can be explained sufficiently well with the dynamics of the instrumental and affective attitudes. Hypothesis 2 (Principle 3): Remembering depends on accessibility, the cognitive resources available (using behavior frequency as the indicator of situational influences on these resources), and habits. This hypothesis is falsified if (a) some of these dynamics are identical and as a result the parameters of Equation 1 cannot be identified (i.e., the

420

TOBIAS

calibration does not deliver univocal values for the parameters), (b) the accessibility threshold is not influenced by the behavior frequency (i.e., WBFAT ⫽ 0), or (c) the accessibility threshold is not influenced by the habit development (i.e., WHAT ⫽ 0). Hypothesis 3 (Principle 4): Accessibility decays over time. This hypothesis is falsified if ADP ⫽ 0. A stronger form of the hypothesis states that accessibilities of intended behaviors decay at the same speed as retrospective memories. The stronger hypothesis is falsified if ADP has a value considerably smaller than .84. Hypothesis 4 (Principle 5): Events increase accessibility. This increase depends on the person’s commitment to performing the behavior. This hypothesis is falsified if WCIEvent ⫽ 0. Hypothesis 5 (Principle 6): Performing a behavior increases its accessibility. This hypothesis is falsified if WBFAGBeh cannot be identified because, for this case, the model using Equation 4 has no more explicative power than the model using Equation 1 alone. Hypothesis 6 (Principle 7): The effect of a reminder depends on the person’s commitment to performing the behavior. This hypothesis is falsified if WCIRem ⫽ 0. Hypothesis 7 (Principle 7): The effect of a reminder decays over time. This hypothesis is falsified if SDPRem ⫽ 0 (i.e., the salience does not decay at all) or if the salience decays at about the same rate as accessibilities (i.e., reminders have no effect). Hypothesis 8 (Principle 7): A logistic function of behavior frequency is the simplest function with which to consider the distance of a behavior performance from a reminder. This hypothesis is falsified if simpler functions, or not considering the similarity function in the reminder effect, lead to equally good fits of the model to the data. Hypothesis 9 (Principles 8 and 9): Habit strength decays over time and is increased by behavior performance. This hypothesis is falsified if HDP ⫽ 0. Hypothesis 10 (Principle 9): Habits develop faster if the behavior is performed with higher frequency. However, strong habits also develop for behaviors performed with low frequency if they are executed regularly over long periods. This hypothesis is falsified if a habit increase function independent of behavior frequency leads to the same fit of the model to the data. Hypothesis 11 (Principle 10): Habits develop not only for the executed actions but also for similar actions in similar situations. Therefore, the behavior frequency should increase further if it is already sufficiently high. If the behavior frequency is too low, it should decrease further. This hypothesis is falsified if the behavior shows developments that deviate from this prediction.

Hypothesis 12 (Principle 10): A logistic function of behavior frequency is the simplest means to consider the differences between situations for different actions aggregated in the behavior frequency. Furthermore, the function used to model the distance to the reminder (Hypothesis 8) can also be used to model the similarity of situations regarding habit development. This hypothesis is falsified if the model fit does not depend on the similarity function or if simpler functions fit equally well. To test these hypotheses empirically, the data must fulfill the following requirements. First, as the model describes dynamic processes, time-series data are necessary to investigate and test the model. Second, as the model concerns behavior change, the data must be gathered during a period of behavioral changes. Finally, as the model focuses on the forgetting of the behavior while the preferences for the behavior are assumed to be constant, such a campaign should not use persuasive means unless preferences were already high before its start. In the next section of the article, an empirical investigation is presented that complies with these requirements.

Testing and Analyzing the Model With Dynamic Field Data The study presented here used data from a campaign carried out in Santiago de Cuba, Cuba, to promote waste separation for recycling. The gathered data are well suited for testing and investigating the model because the behavior frequency was measured every day and the campaign changed behavior significantly. Moreover, relatively high behavior frequencies had already been preferred before the campaign, and no persuasive means were applied during the campaign. Finally, the participants had already mentioned before the campaign that the critical factor for not performing the behavior was forgetting.

Method Testing and investigating a dynamic model as presented above is a very different process from the procedures of testing hypotheses traditionally carried out in psychology. Therefore, the methods are explained in detail. Initially, the gathering of time-series data in the field is addressed, and then the system analysis of the simulation model is explained. Campaign and data gathering. The data were gathered at the beginning of 2005 in a campaign designed to reduce deposited solid waste in Santiago de Cuba. On the basis of expert interviews and a representative survey that was conducted about a year prior to the beginning of the campaign (Binder & Mosler, 2007; Mosler, Drescher, Zurbru¨gg, Caballero Rodrı´guez, & Guzma´n Miranda, 2006; Mosler et al., 2008), promoting waste separation for recycling was identified as a possible way to reduce deposited waste. Although attitudes toward waste separation had been very positive, performance was low because the participants forgot to separate their waste at the moment of disposal. A simple intervention based on reminders was consequently implemented: Every household in the sample received a sheet of paper with the text (in Spanish): “Please classify and separate! Glass—Aluminum—Paper— Cardboard—Plastic.” Without any further persuasive elements, the

CHANGING BEHAVIOR BY MEMORY AIDS

sheet was distributed during the first interview, along with the request for participants to display it in their houses at the location where most of the waste was collected. The data were gathered during the pilot phase of the campaign, when the reminders were tested before being applied on a large scale. The random-route method was used for sampling (e.g., Hoffmeyer-Zlotnik, 2003): Sixty-four households received the reminders and every day filled out a short questionnaire that asked about their behavior and some psychological constructs. Approximately once a week, interviewers collected the questionnaires. This daily data gathering by short questionnaires is referred to as monitoring. Measuring data every day raises several problems, particularly in view of the reactivity of the measurements and the subjects’ fatigue. As a result, the daily questionnaire had to be kept short and simple. For this article, only three constructs are considered: the behavior, an affective attitude, and an instrumental attitude. It is necessary to consider the attitude measures because new (rather than habitual) behaviors were being investigated. As a result, the performance of this behavior might not be constrained only by the forgetting of its performance but also by a low preference for it. For the behavior, each item had two subitems—since two institutions collected solid waste for recycling—that inquired about the quantity of the separated solid waste. Each subitem had six categories for answering, coded as 1, .75, .5, .25, .1, and 0, and the two answers were added. The item for the affective attitude asked to what degree the participant was keen to separate solid waste for recycling. Four categories of answers ranged from I feel a lot like doing so (coded as 1) to I do not feel at all like doing so (coded as 0). For the instrumental attitude, the questionnaire asked to what degree the person thought it was worth separating solid waste for recycling. Seven answer categories ranged from much more benefit than effort (coded as 1) to almost the same benefit as effort (coded as 0) to much more effort than benefit (coded as ⫺1). No data about forgetting, habits, or commitment strength were measured. Additionally, behavior frequencies prior to the intervention were also unclear, as most of the time series began immediately with the campaign. However, for the purposes of testing and investigating the dynamic properties of the model, the time series of behavior frequencies were sufficient. Test and system analysis of the simulation model. The system analysis of the model was performed in three steps. In the first step, the model was calibrated. That is, the values of the parameters were varied systematically until a good fit of the simulated and empirical data was found. This was performed not only for the full model, as previously presented, but also for a number of variants, each of which was missing one or two parts of the full model. Based on this, the fit of the model and the importance of each of its parts were evaluated to test whether the model structure was sufficient and necessary to replicate and explain the dynamics of the data. In the second step, cross-validations were used to test the model’s capability to generalize its results to other data not used for calibration and to forecast future developments based on earlier data. In the third step, parameter values and the preciseness with which they could be calibrated using the available data (i.e., the empirical identifiability of the model) were investigated. The model was calibrated to the data using an adaptation of the shuffled complex evolution approach presented by Duan, Gupta, and Sorooshian (1993). For all optimization runs, at least 10,000 iterations were calculated, with the majority of optimization runs

421

converging within 5,000 iterations. Only the behavior frequency at the beginning of the time series (sBF) and the commitment intensity at the moment of reminder setup (CI) had case-specific values. All other parameters were restricted to have the same value for all individual cases. These 16 global parameters were used for the analysis. Only two accessibility-increasing events, as described in Principle 5, were documented in the data: (a) the day the reminder was set up and (b) visits by the interviewers at the households. All events of one type (i.e., all days of setting up the reminder or all interviewer visits) were calibrated to the same constant and weight for all households. Reminder installations were labeled Event 1, and interviewer visits were Event 2. The time series of the instrumental and affective attitudes were not used within the simulation model. Instead, these data were only used to investigate to what extent the behavior preference could explain the dynamics of the observed behavior. No perfect fit can be expected. Not only is human behavior nondeterministic, it is also dependent on other factors (Principles 1 and 2). Furthermore, because of errors in the field data, a model cannot perfectly fit the data. In fact, it should not fit the data perfectly, as this would indicate overfitting (see, e.g., Pitt & Myung, 2002). However, the model has to replicate the global tendencies of the dynamics without systematic biases, which will be investigated qualitatively based on the plots of the time series. Furthermore, the model should perform substantially better than pure random values or constant averages per case and still better than any simpler version of a model derived from it. Eighteen simplified versions of the model were investigated, each with one or two parts missing. These could be either parameters set to a specific value (e.g., zero) or the use of different functions (e.g., a linear instead of a logistic function). Table B1 in Appendix B gives an overview of the investigated versions. The calibration of these versions of the model was performed in the same manner as the calibration of the full model. However, as a starting point, the optimal parameter set for the full model was used to give the variants the best chance of reaching a fit equally good as or even better than the full model. Thus, any lesser fit of a nonfull version of the model indicates its shortcoming. A decrease in the fit of a model by its simplification indicates the greater importance of the respective model part. To quantify the fit, three indices are used: (a) the mean absolute error (MAE), (b) the root-mean-square error (RMSE), and (c) the explained variance (R2). These indices were calculated over the entire data set and for each case separately. Given that much of the variance in the data comes from differences between cases, the latter values quantify how well the actual individual dynamics are explained. This is a difficult task because it requires explaining the behavior for a specific individual on a specific day; however, this is a central aspect of a dynamic model. To calculate the mean of the casewise fit indices, the only cases used were those that showed actual systematic dynamics for the behavior frequency. Furthermore, the fit of the model was investigated qualitatively, based on the plots of the time series. Probably the most important fit criterion is the absence of any systematic deviances of the simulated data from the empirical. For this, a number of key characteristics of the empirical time series were extracted, and then, whether the simulated time series could replicate these characteristics was checked. If a systematic error is reported in the results, this refers to pervasive patterns of deviances of the simu-

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lated data from the empirical, rather than just several cases with poor fit. The change in complexity resulting from the omission of parts of the full model was not quantified (as, e.g., proposed by Pitt, Myung, & Zhang, 2002) because it was not necessary to decide between a somewhat more complex but better fitting version and a somewhat less complex but not-that-well fitting version. The fit of a model to empirical data is only one criterion by which to evaluate the model’s quality, and, as previously mentioned, overly perfect fit might be problematic. Pitt and Myung (2002) argued that not the goodness of fit is decisive but the “generalizability of a model’s data-fitting abilities” (p. 421). A commonly used approach for model testing in this regard is crossvalidation, whereby the model is calibrated by a part of the available data (training or calibration data), and its ability to be generalized is tested by the rest of the data (validation or test data). This analysis tests the model’s ability to generalize results to data not included in the calibration and to forecast future developments. Thus, not only is the pragmatic usability of the model tested but also whether it is overly complex. Specifically, if the model is too complex, it will fit the training data very well but will perform poorly on the validation data because idiosyncrasies of a particular data sample are replicated (e.g., Pitt et al., 2002). In this study, two types of cross-validation were performed: one that tests whether the results can be generalized and one that tests the prognostic abilities of the model. In the first type, random samples containing half of the cases were chosen to test whether the calibration generalized to the other half of the cases. Three such random splits were performed to ensure that the results did not depend on a particular sample. For each split, the crossvalidation was performed in both directions (i.e., using the sample to calibrate and the rest of the cases to test, and using the rest of the cases to calibrate and the sample to test). For each set and each direction, five optimizations were performed, starting from randomly set parameter values. Because one set might be easier to fit than another, the fit of the test data is compared not only to the fit of the training data but also to the fit of the model that was calibrated, with all data, to the cases included in the test data. In the second type of cross-validation, all cases were divided into a calibration period and a forecasting period. To investigate the number of days necessary to reach acceptable forecasts based on the calibration, the cutoff day was varied between 1 and 24 days after setting up the reminder, thus using 7% to 92% of the data for calibration and the rest for testing the forecast. Because the dynamics changed over the course of the 26 days of the investigation, pure statistical extrapolation of the data would not lead to adequate results. Instead, a priori knowledge must be used to build a forecast based on the most diagnostic information in the data. For each cutoff day, five optimizations, starting from randomly set parameter values, were performed with the data of the calibration period. The optimization with the best fit to the calibration data (which does not usually lead to the best fit of the forecast) was then used for the forecast. A comparison of the forecast to the calibration period is not meaningful. Consequently, the forecast was compared to the fit of the model calibrated, with all data, to the data of the forecasting period. The final step in the analysis focused on the parameter values. The preciseness to which the parameter values could be calibrated (i.e., whether similarly good solutions of the calibration lead to similar parameter values) was investigated. One hundred optimi-

zations were performed with different, randomly set start values for the 16 global parameters, and the extent to which calibrated parameter values varied for solutions with a similarly good fit was analyzed. Similar goodness in fit was defined as an RMSE up to 15% higher than the optimal solution. Ten optimizations did not reach that criterion and were thus replaced by optimizations from other random values. A large variance in the parameter values indicates that the data were not sufficient to calibrate the model and that the model might be too complex. The variance is expressed with the standard deviation, and the tolerance is set based on a comparison with psychometric data. Unipolar psychometric scales usually have between four and six categories. Thus, a scale from 0 to 1 would have a resolution between .2 and .333. If the standard deviation of the calibrated values is between 0.1 and 0.167, the calibration would still have the accuracy of a psychometric scale in 68% of the cases. However, a large variance in a parameter value might also be due to problems with the optimization, as some parameters might be more difficult to optimize than others. In particular, as the optimization approach used optimizes all parameters at once, less sensitive parameters will be less adequately estimated than more sensitive ones. To account for this influence, the correlation of the fit of the solutions with the difference of the parameter values to the value of the best solution was calculated. A high correlation indicates an important influence of the respective parameter on the fit of the model. However, a low correlation might also be due to a low variance of the values and does not necessarily indicate a parameter’s low importance. If the parameters can be calibrated sufficiently well (i.e., if the model can be identified empirically), then their values can be interpreted.

Results The results are presented according to the analyses explained in the Method section, above. First, the empirical data are presented. Then, the fit of the model to these data is compared to the fit of simplified versions of the model. The third section of results addresses the cross-validations, including the investigation of the forecasting abilities of the model. Finally, the identifiability of the model and the parameter values are discussed. Empirical data. From the 64 households included in the sample, 47 (73%) time series entered the following analysis with a total of 1,170 data points. No or insufficient data were gathered for nine cases (14%), and in seven cases (11%), the behavior performance did not reach its maximum, even though it stabilized on a high level, which indicates that the limiting factor was not forgetting but rather difficulties in behavior execution or low preferences. As only the part of the model that deals with memory processes is investigated here, the latter cases were excluded as well. Additionally, in one case, the reminder was set up 20 days after the start of data gathering. Even though this case was not used for the system analysis, it might be seen as an anecdotal control case that shows the monitoring alone had no or only a minimal effect on remembering the behavior. In Figures 3 and 4, the empirical and simulated behavior frequencies of the 47 analyzed cases and the control case are plotted. To facilitate keeping track of the massive amount of information presented in these figures, the cases are roughly arranged from the lowest impact to the highest impact made by the reminder. The data contain

CHANGING BEHAVIOR BY MEMORY AIDS 1: sBF = 0.00, CI = 0.00

4: sBF = 0.00, CI = 0.30

2: sBF = 0.00, CI = 0.00

5: sBF = 0.00, CI = 0.30

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3: sBF = 0.00, CI = 0.20

6: sBF = 0.00, CI = 0.40

7: sBF = 0.00, CI = 0.40

8: sBF = 0.00, CI = 0.45

9: BF = 0.05, CI = 0.00

10: sBF = 0.10, CI = 0.00

11: sBF = 0.10, CI = 0.10

12: sBF = 0.00, CI = 0.60

13: sBF = 0.00, CI = 0.60

14: sBF = 0.00, CI = 0.65

15: sBF = 0.00, CI = 0.75

16: sBF = 0.00, CI = 0.65

17: sBF = 0.00, CI = 0.70

18: sBF = 0.00, CI = 0.75

19: sBF = 0.00, CI = 0.75

20: sBF = 0.00, CI = 0.75

21: sBF = 0.00, CI = 1.00

22: sBF = 0.00, CI = 1.00

23: sBF = 0.10, CI = 0.50

24: sBF = 0.10, CI = 0.85

Figure 3. Individual data of behavior frequencies for Cases 1–24. The x-axis represents time in days from 0 to 30. The reminder was set up on Day 4. The y-axis represents the behavior frequency in percentage of separated solid waste from 0 to 1. The thick line shows the empirical data and the thin line the simulation. sBF ⫽ start behavior frequency; CI ⫽ commitment intensity.

some noise and, in some cases, may even appear random (e.g., Cases 28 and 31). However, in most cases, tendencies in the dynamics become clear. In the majority of cases, save Cases 1–5, setting up the reminder (or the interview performed on this day) had a strong effect on the behavior frequency. For Cases 36 – 47, the behavior frequency stayed high for the rest of the measurement after the reminder was established. For the other cases, behavior frequency rapidly decayed after a few days. For Cases 6 –15, behavior frequency decayed to zero or to its value before the campaign. For Cases 16 –23, behavior frequency seemed to stabilize at a low level, whereas for Cases 24 –35, it increased and stabilized at a high level at the end of the month under investigation.

In striking contrast to these clear dynamics, both attitude components ran on a similar level and were nearly constant over time. The instrumental component showed slight, if any, decay over time in many cases. The standard deviation of the means per case— expressing the interindividual variance—is 0.23 for the affective and 0.27 for the instrumental attitude components, both of which are considerably smaller than that for the behavior (smeans ⫽ 0.32). The mean of the standard deviations per case— expressing the variations of the values within the cases—is 0.21 for the affective and 0.17 for the instrumental attitude components, which illustrates the small variations within the cases when compared to the mean of the standard

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424 25: sBF = 0.20, CI = 0.40

26: sBF = 0.20, CI = 0.35

27: sBF = 0.20, CI = 0.35

28: sBF = 0.30, CI = 0.10

29: sBF = 0.30, CI = 0.25

30: sBF = 0.30, CI = 0.25

31: sBF = 0.30, CI = 0.30

32: sBF = 0.20, CI = 0.50

33: sBF = 0.20, CI = 0.60

34: sBF = 0.20, CI = 0.65

35: sBF = 0.20, CI = 0.70

36: sBF = 0.25, CI = 0.70

37: sBF = 0.30, CI = 0.60

38: sBF = 0.40, CI = 0.15

39: sBF = 0.25, CI = 0.80

40: sBF = 0.30, CI = 1.00

41: sBF = 0.30, CI = 1.00

42: sBF = 0.30, CI = 1.00

43: sBF = 0.50, CI = 0.90

44: sBF = 0.30, CI = 1.00

45: sBF = 0.60, CI = 0.20

46: sBF = 0.30, CI = 1.00

47: sBF = 0.65, CI = 0.20

Ctrl.: sBF = 0.20, CI = 1.00

Figure 4. Individual data of behavior frequencies for Cases 25– 47 and the control case (Ctrl.), not used in the analysis. The x-axis represents time in days from 0 to 30. The reminder was set up on Day 4. The y-axis represents the behavior frequency in percentage of separated solid waste from 0 to 1. The thick line shows the empirical data and the thin line the simulation. sBF ⫽ start behavior frequency; CI ⫽ commitment intensity.

deviations (0.28) for the behavior. The correlation of the behavior frequency with the affective attitude component is .11 and with the instrumental attitude is .22. The two attitude components are not correlated (r ⫽ .02), which supports the distinction between them. Thus, the attitude, or preference, for the behavior, as introduced in Principle 2, cannot explain the dynamics of behavior frequency. However, the attitude measures explain some variance in the behavior frequency before the campaign: The averages of both attitude components correlate with r ⫽ .39 with the start behavior frequency. The mean of both attitude components explains 25% of the variance of the start behavior frequency. This indicates that the attitude mea-

sures worked but that they cannot explain the dynamics provoked by setting up the reminder. Fit of the simulated data of the full and simplified models. Figures 3 and 4 also show a qualitatively good fit between the simulated and the empirical data. Although the model does not replicate every peak of the empirical data, no systematic deviation in the global tendencies of the dynamics could be found. This finding is in striking contrast to nearly all the simplified versions of the model investigated. Table B1 in Appendix B shows the fit statistics of the full model and all versions. The full model explains about 68% of the variance of the entire data set and, on average, about 47% of the individual dynamics. To calculate this mean of

CHANGING BEHAVIOR BY MEMORY AIDS

casewise dynamics, Cases 6 –35, without 28 and 31, were used, as these cases show neither a nearly constant behavior frequency after reminder setup nor a randomlike pattern. RMSE is about half of a uniformly distributed random time series. Compared to the constant development with the mean per case, the model has an RMSE about 20% lower over all cases and, on average, an RMSE about 25% lower per case. This indicates the considerable explicative power of the model. Only three versions have a comparable fit (less than 5% higher RMSE or lower R2). Version 3-2, which omits the effect of the memory aid independently from the commitment intensity, has about the same fit as the full model. This was expected due to the unremarkable nature of the reminder used. Furthermore, both Version 1-4, in which habits are increased independently from the frequency of behavioral performance on a day, and Version 5-3, which omits the decay of the salience of reminders, have an RMSE that is about 3% higher or an R2 that is lower over all cases, and a 3%–5% difference for the mean of casewise-calculated RMSE and R2. Besides calculating the fit indices, the plotted time series were analyzed case by case for systematic deviations of the simulation from the empirical data. As previously mentioned, no such deviations were found for the full model. In addition, Version 3-2 does not show systematic deviations. However, all other versions devi-

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ate systematically in one form or another from the empirical data. Some versions had problems with behavior frequencies close to the maximal (systematic error of Type 1) or minimal (Type 2) values of this parameter, whereas others did not replicate certain dynamic patterns (Type 3) or behavior frequencies within certain ranges (Type 4). Finally, some versions produced dynamics without any similarity to the empirical dynamics (Type 5). The most important errors, Types 2 and 3, are illustrated in Figure 5. Versions showing a Type 2 systematic error could not replicate a low behavior frequency, which, after a longer period of time, decays to zero. With parameter values resulting in the best fit to the data, behavior frequency never decayed to zero for these versions. Yet, with differently set parameter values, behavior frequency decays to zero almost immediately. This type of error has next to no effect on the values of the RMSE or R2. However, the incapacity to replicate the dynamics of low behavior frequencies is critical because it is a fundamental difference if a person performs a behavior at least daily or not at all. Versions showing this type of error overestimate the effect of a campaign by forecasting too-high values for the behavior frequency for later periods. The systematic error of Type 3 refers to the most important dynamic pattern observed in the data: the strong decay of behavior frequency in the first weeks followed by an increase in behavior

Behavior frequency

1.00

0.75

0.50

0.25

0.00 0

5

10

15

20

25

30

Days Emp.

Full

1-4

5-3

Behavior frequency

1.00

0.75

0.50

0.25

0.00 0

5

10

15

20

25

30

Days Emp.

Full

3-3

4-4

Figure 5. Examples of systematic errors. Top panel: Systematic errors of Type 2 are illustrated with the dynamics of Variants 1-4 and 5-3 for Case 13. The behavior frequency should stay low for approximately 12 days and then decay to zero. Instead, for the variants, behavior frequency increased even more. For this case, no data were gathered until Day 8. Bottom panel: Systematic errors of Type 3 are illustrated with the dynamics of Variants 3-3 and 4-4 for Case 35. Behavior frequency should decay strongly in the first 3 weeks and then stay stable at a high level for the rest of the time. Instead, for the variants, behavior frequency did not decay sufficiently early on or it decayed too greatly in the final week. Emp. ⫽ empirical data; Full ⫽ simulation with full model.

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426

frequency and a late stability after about 3 weeks. Many versions either did not replicate the early deep decay of the behavior frequency or did not stabilize at a high level at the end of the month. Figure 5 explains why versions that only showed Type 2 errors (i.e., Versions 1-4 and 5-3) still showed a good overall fit. On the one hand, the difference between the empirical and simulated data is small. On the other hand, only a few cases showed a dynamic where this error was problematic. However, a model is unacceptable when it cannot replicate an entire type of dynamics and predicts a daily behavior performance where there is none. Furthermore, these versions also showed a forecast performance poorer than that of the full model. Overall, these investigations indicate the strong explicative power of the full model and that all parts of the model are necessary to replicate the empirical data. However, in the case of very unremarkable reminders as used here, the consideration of an effect of the reminder independent from the commitment is unnecessary. Still, by omitting the dependency of the habit strength increase on the behavior frequency or by omitting the decay of the reminder’s salience, the overall fit is reduced only slightly. However, these versions produce unacceptable systematic errors. Cross-validations and forecasting abilities. The results of the random-split cross-validations are presented in Table 3. The model

scored well in this analysis, indicating a good generalizability of the model’s data-fitting abilities. On average, the fit indices are only about 3% worse for the test than for the training data or the fit of the model calibrated with all data. The only exception is that the MAE of the test data is 10% higher compared to the fit of the model calibrated with all data. However, as the MAE of the test data is, on average, only 4% higher than that of the training data, this cannot be attributed to overfitting and, therefore, a toocomplex model. The quality of the forecasts as a function of the amount of data used is plotted in Figure 6, using the RMSE as an example. The other fit indices led to similar results. Figure 6 shows there is a strong dependency of the forecast quality on the number of days used to calibrate the model for the first few days. After about a week (i.e., using about 30% of the data), the forecast reaches the quality of the full model (RMSE ⫽ 0.25). The reason for the poor forecasts using data of less than a week is the slow development of habits. To calibrate the parameters that define the habit development, sufficiently strong habit effects have to emerge. Using the data from the 3rd week after memory aid setup leads to a slightly higher error than when not using this data. This indicates the particularly poor quality of the data in this period. Using the data from the beginning of the 4th week again reduces the error to the level of the model that used all data for calibration.

Table 3 Result of Cross-Validations Based on Random Splits of the Sample Set 1A MAE RMSE R2 1B MAE RMSE R2 2A MAE RMSE R2 2B MAE RMSE R2 3A MAE RMSE R2 3B MAE RMSE R2 M MAE RMSE R2

Test

Training

Percentage of training

All

Percentage of all

.158 .264 .652

.155 .243 .684

102% 109% 95%

.145 .257 .672

109% 103% 97%

.159 .251 .664

.151 .256 .668

105% 98% 99%

.145 .241 .691

109% 104% 96%

.163 .260 .641

.147 .245 .695

111% 106% 92%

.150 .253 .661

108% 103% 97%

.157 .256 .670

.156 .251 .663

101% 102% 101%

.139 .244 .701

113% 105% 96%

.152 .251 .685

.161 .254 .647

94% 99% 106%

.138 .245 .703

110% 103% 97%

.165 .260 .634

.147 .246 .697

112% 106% 91%

.153 .253 .655

108% 103% 97%

.159 .257 .658

.153 .249 .676

104% 103% 97%

.145 .249 .681

110% 103% 97%

Note. Three different random splits were investigated (number of the sets) using one part for calibration and the other for testing the result and vice versa. The table shows the mean of the fit. Indices of five optimizations performed for each set and direction. The fit of the test data is compared to the fit of the training data and to the fit of the model calibrated with all data. MAE ⫽ mean absolute error; RMSE ⫽ root-mean-square error.

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Figure 6. Root-mean-square error (RMSE) of forecast as a function of days after start of the campaign used to calibrate the model (thick line). The thin line represents the RMSE of the model calibrated with all data, calculated for the same set of days as the forecast compromises. For example, for Cutoff Day 10, the model for the forecast was calibrated with the data up to 10 days after setting up the reminder. The values represent the RMSE of the period starting 11 days after setting up the reminder until the end of the data series.

In Figure 6, the fit of the model calibrated with all data for the test period is plotted. In the first 2 weeks, the RMSE decreases, thus indicating a poorer-than-average fit for this period. A particularly strong decay of the RMSE can be observed between Cutoff Days 12 and 16. The poor data quality of this period results in the aforementioned higher RMSE of the forecast. Overall, the model shows a good forecasting ability if the data from at least 1 week is used for calibration. Identifiability and parameter values. The calibrated values of the global parameters are compiled in Table 2, together with the standard deviation of values for similarly good solutions and the correlation of the fits of the solutions, with the difference in parameter value, to that of the optimal solution. The individual parameters are included in Figures 3 and 4. Finally, the distribution of the values is, for all parameters, close to normal, with the exception of AGCRem , which has its mode at zero. Only seven parameters have a standard deviation larger than 0.1, and only three have a standard deviation larger than 0.167. The largest standard deviation is found for the slope parameter of the similarity function (Ss). However, this parameter has an endless range of values, and a variation of its value by ⫾3 leads to barely visible changes in a plot of the function. Thus, in spite of the large standard deviation, this parameter could be very precisely identified. The second largest standard deviation is found for the weight of the behavior frequency in the accessibility gain due to behavior execution (WBFAGBeh). However, this parameter also has the highest correlation (r ⫽ .51) of the fit with the differences of the parameter values to that of the optimal solution. Therefore, this parameter is important for the model, but, due to its low sensitivity, it could not be estimated well in all optimizations. It is a typical fine-tuning parameter that should be optimized separately when the other parameters are set to their optimal values. Finally, the weight of the commitment intensity in the accessibility gain for the events (WCIEvent1 and WCIEvent2) has a rather large, though still

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acceptable, standard deviation. It seems that the differences between individuals cannot be captured sufficiently with only two parameters or that the data have too much noise. Both problems inevitably lead to errors, and many possible compromises lead to similar fits of the model. However, as the standard deviations are still acceptable, it can be concluded that all the parameters could be indentified well and, thus, that their values can be interpreted. The accessibility decay parameter (ADP) has a value of .76. This signifies that the accessibility of the intended behavior decays at about the same speed as retrospective memories (see Figure A1 in Appendix A), a finding that supports even the stronger version of Hypothesis 3. Regarding the increase in the accessibility, the results indicate a prominent role of commitment intensity. For both types of events and the effect of the reminder, the weight of the commitment (WCI) was calibrated to higher values than the constants (AGC). This tendency is least pronounced for Event 1 (reminder setup) and most pronounced for the effect of the reminder in place. In fact, the constant of the reminder effect could be set to zero, which was expected due to the use of such an unremarkable reminder. In addition, behavior execution was found to have a medium effect on memory processes, according to the weight of the behavior frequency in the accessibility threshold (WBFAT). However, the value of the weight of the behavior frequency in the accessibility gain due to behavior execution (WBFAGBeh)—about the size of the value of the accessibility decay parameter (ADP)—indicates that the two antagonistic processes of the behavior frequency’s influence on remembering were of equal strength. In addition, the other parameters of the accessibility threshold (CAT and WHAT) are calibrated to a value of about .5, suggesting a similar importance of the two dynamic factors of the accessibility threshold. The salience of the reminder shows a slow but relevant decay (SDPRem). After 30 days, the reminder shows about half of its effect as on the first day. That this decay cannot be observed in all cases is due to the development of habits, which compensates for the reduced reminder effect. The value of the habit decay parameter (HDP) indicates that it takes about a month for the habit strength to completely develop if the behavior is performed daily and that, after about a week, the habit strength is halfway developed. This rate seems plausible and explains the somewhat poor forecast if the data used to calibrate the model are from less then a week after setting up the reminder. In such a short time, nearly no habit development took place, making it difficult to calibrate the respective parameters. The calibrated similarity function is plotted in Figure A2 in Appendix A. The dissimilarity parameter (DP) is set to a rather high value, and the turning-point parameter (TS) sets the step of the function in the area between behavior frequencies of .15 and .45. Finally, the slope (SS) is set to be rather steep. All of these values support that the step characteristic is necessary to describe similarity on an abstract level.

Discussion of the Empirically Founded System Analysis The model explains more than two thirds of the variance of the entire data set and, on average, nearly half of the individual dynamics. Furthermore, no systematic deviations from the empirical data could be found based on the plotted time series. With the exception of omitting an effect of the reminder independent of the commitment, due to the unremarkable reminder used in the cam-

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paign, no other simplifications are possible without producing systematic errors. The conclusion that the model is not overly complex is also supported by the results of the cross-validations. The calibrated model fits data not used for calibration. Additionally, if at least 1 week’s worth of data is used (due to the slow development of habits), forecasts of the quality of the model calibrated with all data are possible. Thus, the model does not overfit the data and captures the central dynamic processes behind the observed data. Moreover, the identifiability analysis supports the notion that the complexity of the model corresponds to the information of the data. However, regarding the analysis of interindividual differences, the complexity of the model, as well as the quality of the data, might be somewhat low. The parameter values all appear plausible and support the principles of the model. A particularly interesting result is that the accessibilities of intended behaviors decay at the same rate as retrospective memories. Habit strength decays and develops within about a month. Given that the salience of the reminder decays considerably more slowly, habits can compensate for the reduced effect of the reminder. Eventually, the reminder becomes unnecessary, as the behavior is performed (or at least remembered) due to habits. The parameter values and the impact of their variance indicate the following process of increasing the behavior frequency: Events strongly increase the accessibilities of intended behaviors, which then rapidly decay again. Reminders compensate for this decay by continuously increasing the accessibilities. The effect of the reminders depends largely on the person’s commitment to performing the behavior, which leads to large interindividual differences in the development of the behavior frequencies over time. Furthermore, the salience of the reminders decays over time, reducing their effectiveness. This is compensated for by slowly developing habits— but only if the behavior is performed sufficiently often. If the behavior is performed before the reminder is set up, it can build on already existing habits, which accelerates the process of increasing the behavior frequency. Regarding the results of the empirical study, each of the hypotheses is compiled: Hypothesis 1 is supported by the low correlation of the attitude components with the behavior frequency.

WBFAGBeh , to the value of the best optimization. Furthermore, omitting this parameter leads to considerable errors. Hypothesis 6 is supported by the value of WCIRem , which is considerably larger than zero. Furthermore, setting this parameter to zero leads to considerable errors. Hypothesis 7 is supported by the value of SDPRem , which is much less than the value of ADP but clearly greater than zero. Furthermore, setting the decay of salience to zero leads to systematic errors. Hypothesis 8 is supported, as the logistic function leads to a sufficient and better fit than simpler functions. Also, the parameters of the logistic function were calibrated to values that accentuate the characteristic step of this type of function. Hypothesis 9 is supported by the value of HDP, which is considerably greater than zero. Furthermore, setting this parameter to zero leads to considerable errors. Hypothesis 10 is supported, as a model with a habit increase independent of the behavior frequency produces systematic errors. Hypothesis 11 is supported by the data: Behavior frequency increases over time if the behavior is performed sufficiently often. Hypothesis 12 is supported for the same reasons as Hypothesis 8. All hypotheses derived from the model were confirmed. Thus, the data strongly support the presented model, which is now discussed on a more general level.

Discussion of the Model Having shown the model’s adequacy in the first empirical study, a more general discussion is presented. The model is related to research on prospective memory and habits, and further directions are discussed for its testing, investigation, and application.

Relationship With Prospective Memory Research Hypothesis 2 is supported, as the parameters of the accessibility threshold (WBFAT and WHAT) were calibrated to values greater than zero and omitting these parameters leads to considerable errors. Hypothesis 3 is supported even in its stronger form by the value of ADP: Accessibilities for intended behaviors decay at the same speed as retrospective memories. Hypothesis 4 is supported, as the weights of the commitment influence (WCIEvent) of both events were calibrated to values larger than zero and omitting these parameters leads to considerable errors. Hypothesis 5 is supported by the high correlation of the fit of the various optimizations to the difference of values of

New findings for prospective memory research. The presented model considers many of the principal research topics found in the literature on prospective memory research. The most important are the need for cognitive resources in prospective memory tasks (Principle 3); the decay of the accessibilities of intended behaviors (Principle 4); and the accessibility increase due to events (Principle 5), behavior performance (Principle 6), and memory aids (Principle 7). The literature pertaining to these topics has been discussed above in the initial explanations of the principles. A new aspect, which has not yet been considered in prospective memory research, is the effect of habits on remembering intended behaviors. The model confirms that habits support the recall of intended behaviors, thus reducing the need for cognitive resources for a successful completion of the prospective memory task. This assumption was strongly supported by the empirical study pre-

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sented and should be considered in the investigation of repetitive prospective memory tasks. The empirical study also showed that accessibilities of intended behaviors decay at the same speed as retrospective cognitions (Principle 4). Nonetheless, prospective memory contents might be recalled more easily than retrospective memory contents, as remembering depends not only on the accessibility decay but also on the accessibility threshold (Principle 3) and particularly on factors that increase the accessibilities (Principles 4 –7). It is assumed that accessibilities of intended behaviors, which are usually of greater importance to a person than nonintended behaviors, are more frequently and strongly increased by events and situational cues than the accessibilities of retrospective memories. However, the accessibilities of important retrospective memories, for example, a credit card PIN, might be as frequently and strongly increased as those of intentions, making them just as easy to retrieve as prospective memories. Future research comparing prospective and retrospective memory should consider these factors to come to a better understanding of why prospective memories are recalled more often than retrospective memories. The increase of accessibilities is somewhat neglected in prospective memory research and is mostly treated in relation to the reduction of cognitive resources needed or in theoretical ways such as assumptions of how memory aids work. The model introduces four new aspects in Principles 5–7. They involve (a) considering the commitment to performing a behavior, also referred to as the importance of the prospective memory task to the person; (b) considering the execution of the behavior as an event that increases the accessibility of this behavior if it is performed again; (c) considering that, in real-world settings, a behavior is performed in various situations, particularly at different distances from a reminder; and (d) considering that the reminder loses effectiveness over time even though this decay is relatively slow. In the empirical study, the effect of the reminder was almost completely determined by the commitment to the behavior. In addition, for the effect of the events, commitment intensity played an important role. This not only indicates the importance of this factor for prospective memory research, but also points to options for making reminders more effective using measures such as implementation intentions or self-commitment. In this study, behavior execution as an event that increases the accessibility of this behavior happened to be important. However, further research is necessary to determine more precisely under what conditions frequent behavior performance makes remembering the behavior easier and under what other conditions remembering is more difficult. The assumption is that, if a behavior is always performed in the same situation and, in particular, within a short time period (e.g., in a laboratory setting), more-frequent executions increase memory performance. In real-world settings, a more-frequent behavior performance also means the behavior is performed in a greater number of different situations. Therefore, a higher behavior frequency indicates an increased likelihood that the behavior is performed in circumstances that make remembering more difficult (i.e., in different situations). Furthermore, if the lag between single behavior-execution events is rather large, a higher frequency of behavior performance should make the memory task more difficult. Considering the possibility that a behavior is performed in different situations might be the most important difference be-

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tween studies carried out in real-world settings and those in the laboratory. In real-world settings, it will never be possible to track every single situation in which a repeated behavior is performed. Therefore, the abstract concept of the similarity function was introduced and formalized in an extremely simplified form. Even though the entire variety of situations in which the persons performed the behavior was described with only three parameters, the function was sufficient to capture the essential characteristics of this variety. The model also indicates the necessity to conduct long-term studies. In the present study, the effect of the reminder was seen to decay over time. However, this decay is relatively slow. In particular, it is much slower than the increase in habit strength if the behavior is performed sufficiently often. Thus, habits might compensate for the decay of a reminder’s effectiveness, but, in many cases, the behavior is not performed sufficiently often. In the latter case, the behavior will not be performed after some weeks even though the reminder is in place. Finally, Principle 1 points to the fact that remembering a behavior is only one factor that determines behavior execution. Particularly in real-world settings, a behavior might be remembered but not executed because it is not feasible or another behavior option is preferred. Conversely, social psychological investigation should consider that a behavior might not be executed because it has simply been forgotten, even though the preference for it is high. In the empirical study detailed above, the behavior dynamics could not be explained by attitude. Existing research to prospective memory still to be considered in the model. The presented model considers a wide range of findings in prospective memory research. However, for the sake of simplicity, many other areas of this research field have had to be omitted and should be considered in future work. Focusing first on the primary aim of the model, that is, to forecast the effect of external memory aids, a more comprehensive overview of the literature is given. The findings on the effects of situational cues on prospective memory tasks can be summarized as the following points: 1.

Situational cues allow a situation to be recognized as the one in which a behavior should be executed (e.g., Einstein & McDaniel, 1990; Marsh et al., 2000; Marsh, Hancock, & Hicks, 2002).

2.

Moreover, situational cues remind a person which behavior should be executed. Guynn et al. (1998) pointed out that well-designed external memory aids activate associative connections between key situations and behaviors to be executed, making it easier to remember the behavior at the critical moment.

3.

External memory aids should also back up retrospective memory tasks such as remembering details of how the behavior is to be executed (Ellis & Kvavilashvili, 2000; Shapiro & Krishnan, 1999).

4.

Finally, Einstein, McDaniel, Smith, and Shaw (1998) mentioned the feedback function of memory aids in the case of repeated behaviors. Some behaviors, such as taking medication, should not be executed more than

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once within a certain period of time. In these cases, a well-designed memory aid indicates whether the behavior has already been executed or still has to be executed. The presented model considers only the second point. To consider the first point, an additional perception module must be added, as this point does not concern memory processes. Such an augmentation of the model would make sense if investigation were to involve cases where situations can be interpreted incorrectly. If the correctness of behavior performance is a problem due to missing knowledge at the critical moment, the third point must be considered. Here, essentially the same memory model as that presented can be used, but the cognitions would be retrospective memory contents rather than intended behaviors. The accessibilities of the retrospective memories decay, but they are also increased by certain events and situational cues such as reminders. Because the behavior investigated in the empirical study had no restrictions on how often it could be performed, the last point was not considered. Such consideration might be more complicated given that the model must keep track of periods in which the behavior should be performed and in which it must not be performed. Technically, this can be easily executed, but it is first necessary to know in more detail the psychological processes of this tracking. The lack of research on problems related to overperforming a behavior leads to another somewhat neglected area not incorporated in the actual model: time-based prospective remembering. The few studies in this area (e.g., Kvavilashvili & Fisher, 2007; Maylor, 1990; Sellen et al., 1997) have focused on the question of what stimulates subjects to think about a task they have to perform at a specific time. As it happens, remembering the task is mostly automatic (i.e., it is activated by cues, or it just “pops into one’s mind”). Furthermore, the persons performing a time-based task remembered the task more often than persons performing an event-based task. However, at the critical moment, the time-based task was forgotten more often than the event-based task. Kvavilashvili and Fisher (2007) proposed a random-walk model of thoughts. The idea is that thoughts “wander around” randomly and that the closer thoughts meander to the task, the more easily the task will be remembered. This could easily be considered in the model by adding a random number to the accessibility threshold. However, in real-world conditions, pure time-based tasks might be rare since they generally become event-based tasks (e.g., by setting an alarm clock or by coupling the task with some naturally occurring event or routine behavior; see, e.g., Maylor, 1990). However, the problem of a behavior being remembered, though not at the correct moment or at the correct location, is also common for many tasks in real-world settings. In the actual model, no distinction is made between whether a behavior is completely forgotten or is remembered, just not at the right time or place. Future versions of the model might consider such problems in more detail. On the other hand, a great deal of prospective memory research covers age differences. These are not explicitly considered in the presented model. However, the literature indicates that no such differences can be found in real-world settings and that they seem to be more of an artifact of laboratory settings. Rendell and Craik (2000) performed a laboratory experiment that presumably mimicked a real-world setting, and they indeed found age differences. However, in the real world, no such differences were found. The

authors mentioned, as reasons for these striking differences between laboratory and field studies, the longer time frame, ongoing activities chosen by the persons themselves, motivational aspects, and the predictability of future events in real-world settings compared to in laboratory settings. Kvavilashvili and Fisher (2007) explained the lack of effect of age differences with the higher motivation (i.e., commitment) of the elderly persons, whereas Maylor (1990) explained the absence of age differences with the techniques used by the persons to remember the task (e.g., coupling to routine behavior, planning ahead, use of external memory aids, etc.). Henry et al. (2004) argued that, for automatic processes, no age differences can be found, but if more strategic processes are involved, then more age differences will appear. According to McDaniel and Einstein (2000), strategic processes are necessary in cases of (a) non distinctive PM cues, (b) a weak association between the cue and the intended action, (c) a highly attention-demanding or engaging ongoing task, or (d) processing of the PM cue being peripheral to the processing carried out in the ongoing task. (Henry et al., 2004, p. 28)

Thus, in the presented model, age differences could easily be considered by increasing the accessibility threshold while at the same time making the reminder more effective. Another aspect not included in the model is internal memory aids such as recollections (Ellis, 1996) and rehearsals (Kvavilashvili, 1987). These could be modeled by adding an influence similar to that of external events. Internal memory aids could also be modeled similarly to the effect of behavior execution where the accessibility would be increased not by the frequency of the behavior execution but instead by the frequency of applying the internal memory aids. The first version would model seldomoccurring intentional reminders to perform the task, whereas the second version would model a more-continuous automatic process. Future research must show which form is more adequate or in which case one form or the other is to be used. Relatedly, another topic is the need for cognitive resources in performing these recollections and rehearsals. More specifically, the need for cognitive resources in performing prospective memory tasks, as investigated in so many experiments, might have two aspects: the need for cognitive resources to (a) remember the behavior at the critical moment and (b) keep the accessibility high for a longer period. Considering internal memory aids would be especially interesting when investigating social memory aids (Schaefer & Laing, 2000) in a multiagent simulation. This would allow the modeling of the negative effects of expecting to be reminded by someone without it actually taking place.

Relation to Habit Research The model considers the main principles of associative learning to be the decay of associations (Principle 8), the increase of associations depending on the frequency of performing a behavior in a specific situation (Principle 9), and the effect of a behavior execution on similar actions performed in similar situations (Principle 10). The empirical study avoided confounding habit-related processes with other factors that can lead to repeated behavior performance (Principles 1 and 2) by focusing on the memory effects of habits and their development over time. Clear habit dynamics were found, indicating a process of increasing or de-

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creasing habit strength to a maximum or to zero, respectively, within approximately 1 month. This rate of habit development is somewhat slower than that found in the literature (e.g., Breckler & Wiggins, 1989; Ronis et al., 1989; Tolman & Honzik, 1930), but it still seems plausible. Differences might come from the definition of a fully developed habit (i.e., maximum habit strength) and also from the type of behavior. For example, in the study of Baldwin et al. (2006), it took months for the subjects to develop a nonsmoking habit. This might have been due to a greater difficulty in building up a habit for not doing something compared to doing something, as well as the problems of overcoming old habits and, in particular, of overcoming a physical dependency such as smoking. Furthermore, as was also pointed out by the authors, the process of smoking cessation might be more related to the behavior preference (Principle 2) than to habits, as the persons are in a conflict between desiring a cigarette (affective attitude) and assessing smoking as unwanted (instrumental attitude, i.e., convictions and norms). Perhaps the most important point of the habit concept in the model presented here is that habits are not understood as driving forces of behavior selection. Rather, habits have a central impact on memory processes by making it easier to remember repeated behaviors. The reason that habitual behavior is performed much more frequently than nonhabitual behavior is that the other behavior options are not accessible at the critical moment. If they are remembered, it might still be that the actual preference for them is lower than for the habitual behavior, especially due to affective influences. It is argued that, when performing bad habits, people are not trapped in an inevitable automaticity. Instead, they may just be absentminded or, at the actual moment, may not feel like performing the behavior they would consider preferable in other conditions. For example, people might eat an unhealthy snack because they forget they wanted to switch to eating fruit instead or because, at the moment of decision, they may prefer the unhealthy snack because of its better taste— even though, when reflecting on the decision, they would have preferred the fruit because it is healthier. Either way, it is not a reflexlike automaticity that makes people eat unhealthy food. It is an unconscious decision made under different circumstances than when reflecting on their diets with satisfied stomachs. Thus, no habits need to be broken; rather, a person has to be reminded of the target behavior, and measures must be taken to keep this behavior attractive in the critical situation when it should be performed. This is by no means an easy task, and it is not argued that changing away from a habitual to a new behavior is effortless. Instead, it is argued that, although effort is needed, as shown by many studies (e.g., Vohs et al., 2005), this effort is used not to suppress the habitual behavior but to keep the new behavior in mind. Furthermore, effort is needed to keep the new behavior attractive at the moment and situation of performance or to perform it in spite of its lower affective attractiveness. The argument should not be understood as playing down the importance of habit-based decisions in everyday life. On the contrary, for most everyday behavioral decisions, memory is the critical factor, given that the cognitive involvement in these situations is usually minimal. Furthermore, the argument does not deny that habits have many characteristics of automaticities (Bargh, 1996; Bargh & Chartrand, 1999), such as, for example, that habitual behavior can be performed without using cognitive

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resources and that these behaviors are selected unconsciously. However, habitual behaviors are not executed inevitably in the presence of an activating stimulus. The model presented here states the processes that mediate the effect of such stimuli on behavior and the conditions under which habits are or are not performed. Even though the present study supports the important role of habits in memory processes, further investigation is needed to determine to what extent habits are performed automatically. Such a study must involve behavior change because a mere comparison of habitual behavior with less habitual behavior would lead to the same result, namely, that habitual behaviors are performed more frequently than nonhabitual behaviors in conditions of low cognitive involvement. However, the argument of this article would be supported if measures that focus on keeping the nonhabitual behavior in mind and attractive have a greater impact on behavior change than measures that enforce the suppression of the habitual behavior. For example, implementation intentions that focus on performing the new behavior should be more effective than implementation intentions that focus on suppressing the habitual behavior. Related to this argument is the view that habits do not always hinder the performance of more intentional behaviors. Indeed, just as in the empirical study presented here, in most cases, habits support goal-directed behavior (Foerde, Poldrack, & Knowlton, 2007; Hay & Jacoby, 1996; Wood & Neal, 2007). This is not because habits depend on goals but because behaviors that are supportive of goals are performed more often and thus have a better chance of becoming habitual. The final principle of the model states that habits develop not only for the behavior executed in a specific situation but also for similar actions in similar situations. As has been shown when introducing Principle 10, this assumption is not new and is even implicitly considered in much of the research on habits. However, a very simple formalization was proposed and stood its test in the empirical study. The same similarity function was used as for the reminder effect discussed above, which indicates that the reminder may have been an important cue to be associated with the behavior. Regarding the habit-related part of the model, not many extensions seem possible without changing the purpose of the model and making it far more complex. In particular, the investigation of very specific phenomena, such as the causes and variations of single-action slips, should not be attempted with the presented model because such research requires a completely different class of models (e.g., Botvinick & Plaut, 2004; Cooper & Shallice, 2000). However, attention processes (e.g., Kruschke, 2001) might be considered on an abstract level in an additional perception module. In addition, more sophisticated similarity functions might be of interest despite the fact they make the model much more complicated and demand even more data. Possible concepts have been discussed in the presentation of the model (Principle 10).

Shortcomings and Further Investigation Regarding the model itself, the most important shortcoming might be its reduction to merely memory processes and omission of the other two factors that determine behavior as explained in Principle 1. Technically, adding further modules does not present a problem. However, the investigation of such a complex model becomes very difficult, and it might be preferable to first investi-

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gate module by module. For the same reason, the presented investigation was limited to separate cases that did not interact. Within the agent-based approach used, it is not difficult to investigate a population of interacting individuals. This might be one of the next steps in further exploring the model. Several shortcomings of the empirical study must be discussed. First of all, the set of variables for which time series were gathered was very limited. Thus, some of the constructs are absent in the data, especially the strength of habits and the commitment. However, both of these constructs are difficult to measure in the field. For the case of habit strength, the participants can be asked directly either whether they feel that a particular behavior is performed habitually (e.g., Orbell, Blair, Sherlock, & Conner, 2001; Verplanken & Orbell, 2003) or to what extent other habits hinder the behavior’s execution. Unfortunately, people might not be aware of their habits, and thus the answers to such questions may not be reliable (Nisbett & Wilson, 1977). However, Wood et al. (2002) found a high correlation between self-classified habits and commonly used definition criteria of habits such as the frequency of behavior performance, the contextual stability, low cognitive involvement, and low complexity of the behavior. Thus, habits might actually be adequately assessed by direct inquiry. To determine the commitment intensity, various measurements have been used. For example, Rise et al. (2003) inquired about different aspects of planning the execution of the intended behavior and set up a scale comprising several items. Other possibilities are asking about the strength of commitment participants feel toward performing a particular behavior, about the importance of performing the decision, or to what degree they try to abide by their decision. Another question refers to the quality of the data used. Three influences that might impair the data are discussed. These are systematic biases such as (a) the influence of social desirability, (b) effects of random events when using single items for measuring constructs, and (c) effects of the measurement on the data. Regarding the first influence in the presented study, the main item that asks about behavior is easy to answer, and no obvious reason exists for not giving a correct answer. Even more importantly, even if there is a systematic bias in the answers, this will influence only the absolute values, not the answers’ development. It is extremely improbable that several participants would show the same bias in changing an answer over time. There may be a social desirability bias to state higher behavior frequencies, but no norm exists as to how the behavior frequency should develop over time. Furthermore, the participants would have to plan their answers ahead of time to create a bias in the dynamics. Regarding the second influence, random events strongly affected the data, as can be seen by the peaks in the time series of the behavior frequency. However, as the same item is asked many times, the influence of random perturbations on the general tendency of the answers should be small. It can be concluded that the data should be reliable, at least in regard to the dynamics explained by the model. Third, filling out the questionnaire should not have any influence on the behavior performance as it occurs at a completely different time of day than the actual behavior. On the other hand, the interviewer visits had a rather dramatic effect on the behavior performance, but this effect was explicitly considered in the model. A final shortcoming is the empirical study’s global design. For research reasons, an experimental design using several groups with

different treatments would have been preferable. The perfect design for assessing the habit development would be to initiate a new behavior and then—for every experimental group at a different point in time— change the behavior back again or change it to another behavior. However, such a design would be extremely difficult to implement and would raise ethical questions because the subjects in the campaign would be completely confused about procedure. Therefore, when gathering data from actual behaviorchange campaigns, certain limitations must be accepted, and it cannot be expected that data like those in laboratory experiments will be obtained. However, the model surely must be tested with more data gathered during such campaigns to ascertain the generalizability of the results. Data on two campaigns promoting water disinfection in Bolivia have already been analyzed and were supportive of the model. These investigations will be published elsewhere.

Conclusions It will always be challenging to investigate the method in which behavior-change measures function in a real-world setting. Many interacting effects and processes must be considered simultaneously. However, at the same time, only very limited data are available, and even a simple reminder affects behavior in a complicated way. The proposed solution is the development of computer models that include all relevant processes in a highly abstract form. On the empirical side, the suggestion is to gather data on only a few variables but to do so repeatedly to capture the dynamics in time series. By combining computer simulation with timeseries data, it is possible to investigate hidden processes and related variables, which are not measurable in the field. Thus, the presented study led to many surprising findings that may lay the foundation for future research. Most importantly, the role of habits in the remembering of intended behaviors should be considered both in habit research and in prospective memory research. Furthermore, in the area of behavior change, habits should be seen more as abettors of goal-directed behavior rather than as obstacles to be overcome: The problem of changing everyday behavior does not involve the breaking of old habits but rather the lack of habits for the new behavior. Until these new habits are developed, measures such as reminders and implementation intentions are necessary for the behavior to be performed frequently. It has also been shown that, to fully understand the effects of memory aids, it is important to perform field studies over longer periods to investigate how effects vary due to situational circumstances and changes over time. This article might also trigger future laboratory research, as this model introduces many differentiations that have been largely ignored in prospective memory research. For example, the fact that a behavior is remembered more often could be due to either a slower decay of accessibilities, a stronger increase of accessibilities due to different events and processes, or a reduced accessibility threshold due to situational circumstances. The research presented in this article shows that intended behaviors are forgotten at the same speed as retrospective memory contents. However, due to their higher importance, accessibilities of intentions are more often and more strongly increased by events, situational cues, and external and internal memory aids. Similarly, the need for cognitive resources might be

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caused by efforts to remember a behavior at a critical moment or by efforts to keep accessibility high for a longer period. Finally, the study draws connections to research on self-commitment and implementation intentions that still have to be explored more deeply. This study suggests that the commitment to performing a behavior increases the reminding effect of events and cues. From a pragmatic point of view, the study attained its goal of developing a simple model that can predict and explain the dynamics of behavior performance constrained by forgetting and that can allow the planning of behavior-change campaigns using reminders. The model has thus passed its first test with empirical data from a real behavior-change campaign and is now ready for further investigation. If the model scores well, it might be applied to planning future campaigns. Nevertheless, it should be seen not as a perfect solution but simply as one tool among others that can assist individuals or teams to quickly arrive at acceptable decisions.

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Appendix A Graphical Representations of Some Equations

Accessibility (reduction of learning time)

1.0 0.8 0.6 0.4 0.2 0.0 0

1

2

3

Days

4

5

6

7

Ebbinghaus (1885)

Finkenbinder (1913)

Krueger (1929)

ADP = 0.84; AGCBeh = 0.21

ADP = 0.84; AGCBeh = 0.42

ADP = 0.42; AGCBeh = 0.105

ADP = 0.76; AGCBeh = 0.25

ADP = 0.76; AGCBeh = 0.5

Figure A1. Proportional decay of accessibility after an accessibility-increasing event at Day 0, according to Equation 2. The model was calibrated to the value ADP ⫽ .76, approaching the value of .84, which replicates the empirical data of Ebbinghaus (1885), Finkenbinder (1913), and Krueger (1929). These studies used the saving method, which expresses the accessibility of memories in the percentage of time saved in relearning the material. A smaller ADP of .40 would lead to a slower decay. The accessibility is constantly increased by the accessibility gain constant for executing the behavior (AGCBeh), according to Equation 4. ADP ⫽ accessibility decay parameter.

Proportional Decay of Accessibilities (Equation 2 of Principle 4) Figure A1 visualizes the proportional decay of the accessibility decay parameter, ADP. On Day 0, the accessibility is increased by an event to its maximum and then decays. The decay is fast at first and then slows down. In Figure A1, a constant influence of behavior execution (accessibility gain parameter for behavior execution, AGPBeh; see Principle 6 in the main text) is considered and varied. Note that ADP depends on the length of a simulated time step; here, a time step is a day. If the simulation were run in steps of minutes instead, the value

of ADP would be much smaller. The same holds for the other decay parameters (i.e., SDPRem and HDP). To compare this study’s results with research on retrospective memory dynamics, ADP was calibrated to some existing data that describe forgetting curves. Given that forgetting depends on various factors (see Principle 3 in the main text), studies that consider only the amount of material remembered were not suitable here. Instead, only studies that employ the saving method for measuring retention were used. The measure is the percentage of learning time saved in comparison to the first time learning. Three data sets are included in Figure A1: Ebbinghaus (1885), Finkenbinder (1913, mean of all conditions), and Krueger (1929, 150% condi-

1.0

Similarity

0.8 0.6 0.4 0.2 0.0 0.0

0.1

0.2

Calibrated

0.3

0.4 0.5 0.6 Behavior frequency T = 1.0

S = 10

0.7

0.8

0.9

1.0

DP = 0.4

Figure A2. Similarity function (Equation 5) for different values of the turning-point parameter, TS; slope parameter, SS; and dissimilarity parameter, DP. The model was calibrated to the following values: TS ⫽ .52, SS ⫽ 19.52, and DP ⫽ .79. The function is also plotted for TS ⫽ 1.0, SS ⫽ 10, and DP ⫽ .4, for which the values of the other respective parameters were kept at the calibrated value.

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0.07

Habit gain

0.06 0.05 0.04 0.03 0.02 0.01 0.00 0.00

0.10

BF = 0.2 (all)

0.20

0.30

0.40 0.50 0.60 Behavior frequency

BF = 0.2 (valid)

0.70

BF = 0.3 (all)

0.80

0.90

1.00

BF = 0.3 (valid)

Figure A3. The habit gain function (Equation 10) is defined as a logistic function where the values are limited to a maximum that equals the value of the function for the behavior frequency (BF) performed. In the diagram, the complete logistic functions are shown by dotted lines and the permitted values (i.e., values less than or equal to the value for BFExe) by solid lines. The function is plotted for executed behavior frequencies of .2 and .3. The parameters were set to the calibrated values (i.e., DP ⫽ .79, HDP ⫽ .08, SS ⫽ 19.5, TS ⫽ .52). HabitGainExe ⫽ .5.

tion). In all cases, an acceptable replication of the data followed an ADP of approximately .84.

Function for Habit Strength Increase of Other Than Executed Actions (Equation 10 of Principle 10)

Similarity Function (Equation 5 of Principle 7)

Equation 10 in the main text describes how habits for actions not executed increase in their dependence on their similarity to actions actually performed. This somewhat complicated function is graphically represented in Figure A3. For behavior frequencies lower than the executed behavior frequency, the habit strength increases to the same extent as for the executed frequency, as defined by Equation 9 in the main text (HabitGainExe). For higher behavior frequencies, the habit strength increases in direct proportion to the similarities between the situations in which the actions of these behavior frequencies are performed and those in which the actions represented by the executed behavior frequency are performed. For the present case, the similarity of situations is modeled with the similarity function introduced in Principle 7 in the main text. Of course, a different similarity function could be used here if the situational differences are distinct from those of the reminder effect or if differences in the motor characteristics of the actions have to be considered.

Both the effect of the distance from a reminder on its reminding effect (see Principle 7 in the main text) and the effect of the similarity of situations on habit development (see Principle 10 in the main text) were modeled with the similarity function Sf(BF) as defined by Equation 5 in Principle 7. In spite of the equation’s complexity, the function has a rather simple shape, which can be seen in Figure A2. The similarity function has the shape of a step: Up to a certain behavior frequency, its value shows very little change. Then, the values decrease rapidly and, finally, again, change very little. The turning-point parameter, TS, determines at which frequencies the step of the similarity function is located; the slope parameter, SS , sets the width of the area of change, and the dissimilarity parameter, DP, defines the height of the step. The similarity between the situations of the two frequencies is inversely proportional to the difference in the similarity functions for the two behavior frequencies.

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Appendix B Investigated Model Versions Table B1 compiles the investigated versions of the model and the goodness-of-fit indicators used to evaluate the quality of these versions. The results are discussed in the main text.

Table B1 Overview of the Investigated Model Versions, Fit Indices Calculated Over All Cases, Mean of Fit Indices Calculated Per Case for Cases With Actual Dynamics (Cases 6 –35, Without 28 and 31), and Type of Systematic Errors Occurring With That Version Fit over all cases Model version Full 0-1: 0-2: 1-1: 1-2: 1-3: 1-4: 2-1: 2-2: 2-3: 3-1: 3-2: 3-3: 4-1: 4-2: 4-3: 4-4: 4-5: 5-1: 5-2: 5-3:

model Random Mean No forgetting No habit influence at all No habit dynamics Constant habit increase Without events Without event constant Without event commitment influence Without memory aid effect Without constant of memory aid Without memory aid commitment influence No similarity function at all Effect of memory aid without similarity function Habit influence without similarity function Linear similarity function Power similarity function No influence of BF on accessibility threshold No influence of BF on accessibility gain No decay of salience of reminder

MAE RMSE 0.146 0.434 0.229 0.251 0.183 0.165 0.154 0.219 0.184 0.160 0.188 0.148 0.190 0.216 0.170 0.173 0.184 0.187 0.156 0.165 0.160

0.249 0.518 0.305 0.327 0.266 0.263 0.255 0.334 0.275 0.263 0.287 0.252 0.285 0.301 0.263 0.265 0.268 0.270 0.272 0.263 0.257

R2 67.9% 0.1% 51.0% 44.0% 62.8% 63.9% 66.1% 45.5% 60.6% 64.2% 57.9% 67.3% 58.1% 53.4% 63.8% 63.2% 62.7% 62.2% 63.1% 64.0% 65.6%

Mean of per case fits MAE RMSE 0.182 0.400 0.290 0.302 0.222 0.206 0.193 0.283 0.231 0.206 0.212 0.182 0.214 0.248 0.216 0.211 0.208 0.214 0.193 0.198 0.199

0.260 0.482 0.340 0.369 0.281 0.276 0.269 0.376 0.300 0.281 0.297 0.260 0.293 0.326 0.284 0.280 0.276 0.285 0.285 0.272 0.268

R2 46.8% 7.4% 5.3% 39.5% 40.2% 44.9% 26.8% 42.6% 37.9% 47.0% 37.6% 26.8% 38.5% 39.7% 42.2% 39.2% 39.2% 42.9% 44.5%

Systematic error 5 5 5 3 3 2 5 2, 3 2, 3 2, 3 2, 3 5 3 3 3 3 1, 4 3 2

Note. Italicized fit indices indicate a difference of 5% or less from the full model. In Systematic errors column, 1 ⫽ cannot replicate variations of BF when close to 1; 2 ⫽ cannot replicate low BF ⬎ 0 over some period and then decay to 0; 3 ⫽ cannot replicate early deep decays with late high stability; 4 ⫽ cannot replicate certain values of BF, not even at start; 5 ⫽ dynamics of model have no similarity to empirical dynamics. BF ⫽ behavior frequency; MAE ⫽ mean absolute error; RMSE ⫽ root-mean-square error.

Received July 13, 2007 Revision received February 6, 2009 Accepted February 7, 2009 䡲