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The Role of Deliberative Processing in Behavioural Regulation

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It is interesting to note that most of models for behaviour have a focus on the automatic, impulsive part of behavioural regulation. However, deliberation has not disappeared from the scene and makes up a part of some models.

The Mode-Model by Fazio consists of two different classes of processes that can regulate behaviour. The spontaneous process begins with the presence of an environmental trigger. Such perceptions are affected by the knowledge structures, affect, value and expectations that are associated with the current situation. The model expects behaviour to be largely determined by this route. The deliberative processing is marked by cognitive work. It needs conscious information to be present and analyses it in order to identify costs and benefits. However, a deliberative process might still involve some components that are influenced by automatic processes, thus mixed processes are possible.

In his paper on implementation intention Gollwitzer (1993) focuses on the automatic route to behaviour. He also believes that with perceptual information certain situated knowledge is activated. In his model automatic goal pursuit arises from frequent pairing of goal and stimulus. He proposes however a deliberative way to mimic this process. If an implementation intention is consciously formed and practised then this goal information will become active, when the “if” perception occurs. The behavioural schema of the “then”-part is then automatically activated.

Pochaska’s research has led to some interesting findings concerning the change of behavioural patterns. He proposes a model of five steps that are connected in a circle. The first step is precontemplation, followed by contemplation and preparation. The behaviour change is then enacted and, crucially for the success, maintained. Although automatic processes might also be involved, the general outline is focussed on deliberative thought. Most of the suggestion for supporting people who are changing involve stimulating deliberative thought processes in the client.

Last but not least, the model of the reflective impulsive system as supposed by Strack and Deutsch: The main body of the model is made up of the associative store. Episodic and semantic links spread activation between the perception /imagination and behavioural schemata. Only the impulsive system is able to generate behaviour by an “impulsive action”. Yet the reflective system is also important, as it can influence the spreading of activation of every step of the deliberation process. Those steps involve: Propositional categorization, noetic decision and behavioural decision. This model is very comprehensive and provides a unifying framework for many different theories.

The Mode-Model, Gollwitzer’s idea on implementation intention and the reflective impulsive system share many features, yet they focus on slightly different topics with regard to behaviour control. Whereas Gollwitzer is interested in how behaviour can be changed, but on a more mechanical level than Pockaska, the other two models give an explanation of how and why behaviour arises from a more general point of view.

I believe that the strongest empirical evidence for the effect of deliberation on behaviour comes from intention implementation, because in order to change unwanted behaviour one does need to compete with the automatic behaviour for the most activation in order to activate the appropriate behavioural schema. In order to do so the automatic associations must be changed. This was confirmed by Gollwitzer (2002) and his colleagues. They showed that participants holding implementation intentions reacted to words describing the anticipated critical situation much stronger than participants who had only formed goal intentions.

I believe that all theories concerned with deliberation will need to look closely at automatic, impulsive behaviour. As with the implementation intention I believe that the influence of deliberation on behaviour is mostly indirect, as supposed by Strack and Deutsch. Furthermore I believe that research in embodied embedded cognition will contribute to the topic of behavioural regulation, because it can generate specific hypotheses on how the activation in the impulsive system spreads. For example, Wong & Yon (1991) have proposed that associations are less semantically but more perceptually oriented. For example, asking participants to describe a water melon (many report green and stripes) yields different results than asking them to describe half a water melon (participants report much more red). I believe that if it is clearer how the associations are interconnected, it will also be much easier to see how deliberate processes can have influence on behaviour and how reflective and impulsive processes interact with each other.

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Learn to like – Implementation Intention & Affect

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1. Short Summary of the research proposal

This study investigates the role of affect in consciously created automatic behaviour tendencies (implementation intentions). The general hypothesis is that the frequency of a behaviour is closely connected to the activation of a specific behaviour tendency, but this model often fails when implementation intentions are used to change or override old habits. This may be due to the affective dimension, which is contained within the cognitive structure of the habit memory, but not in the implementation intention. By adding the affective domain to implementation intentions it might be possible to mimic “natural habits” much more efficiently.

2. Description of the proposed research

2a. Research Topic

Implementation intention refers to cognitive (if-then-)constructs that mimic the way of automatic behaviour (habits). A habit is formed by repeatedly experiencing the co-activation of a stimulus in the environment and a specific behaviour tendency. Intention implementations are formed by repeated cognitive effort instead of experience. In order to change behaviour, for example in order to override a bad habit, specific behaviour tendencies (the then-part) must be made salient at the right moment, which is achieved by associating the behaviour tendency to a relevant stimulus in the environment (the if-part of the statement). As the (if-)stimulus is perceived, the behaviour tendency is activated and eventually acted upon automatically. In fact implementation intentions are an attempt to mimic the effect of automatic behaviour, which could also be called habits. The process of habit formation is therefore of central value to this research. For goal enactment to become automated, a specific behaviour must be executed in a specific environment, leading to a specific goal. If these things occur in close temporal proximity the goal-means connection in memory grows stronger. In consequence priming the goal automatically activates the behaviour tendency. For example habitual bike riders were faster to respond to “bicycle” after being primed with the goal of travelling somewhere (Aarts & Dijksterhuis, 2000). Recently Sheeran et al. (2005) have shown that this fact is also true for health related behaviour. For example the concept of drinking is more active in heavy drinkers when primed with the goal of socialising.

Thus for automatic behaviour to occur, the spreading of the activation must have been prepared. If goals can be non-consciously activated than these goals must pre-exist in the mind of the actor. The pre-existing knowledge structure connected to these goals is thought to incorporate the goal itself, as well as the context and opportunities and actions that lead to goal fulfilment (Aarts & Dijksterhuis, 2000). But if goals can be activated automatically, how do people know which goals are desirable to attain and which states are worth working for? Ruud Custers and Henk Aarts (2005) have pointed out that mental accessibility is not enough for a goal to be acted upon. The idea of embodied embedded cognition proposes that information about a specific behaviour provided by the nervous system includes affective information. The information about a specific state not only includes information about the means necessary to attain it and important environmental cues, but also the information that this state is desired. This idea is supported by Aarts, Gollwitzer and Hassin (2004) who showed that the effect on effort were stronger if the goal state was more desired. There also needs to be a reason to come into action, which can be seen as a discrepancy between the actual state and the goal state. Furthermore the goal state must be associated with positive affect. Automatic goal-directed behaviour can arise only if all those three prerequisites are met (Custers & Aarts, 2005).

Priming has been investigated in many studies and has proven to be a very reliable effect. Much less research has been conducted into the other two factors, discrepancy with the actual state and association with positive affect. These two factors are much harder to study than priming effects, as people set their goals not based on objective value, but on their subjective interpretation of the variables involved (Kahneman & Tversky, 1979). But in order to change behaviour effectively we will have to fully understand the way automatic behaviour tendencies are naturally created.

I believe that processes that do only rely on association (as intention implementations do) are no match for the “real habits” that are enriched with affective information that tell the organism to follow through with that behaviour plan. In order to change behaviour effectively we will need to create habit-like structures that have a fair chance of competing with the automatic behaviours already in place. This might be especially important for addiction in which the situational stimulus, as well as the goal state are closely connected to physiological states that are regulated by the dopamine pathways of the mesolimbic system. Thus it is questionable if the strategy used by Holland, Aarts and Langedam (2006) to change recycling behaviour would be effective if it was used to change a behaviour that was strongly associated with positive affect. In this study I hope to show that affect is an important building material for the formation of habits and that it will interact with and enhance the effect of cognitive association training, such as implementation intentions.

Aarts, Custers and Holland (2007) showed that our mental apparatus is capable of ceasing goal directed activity when the goal is associated with negative affect. However this effect probably does not last for a long time and it is questionable if the priming paradigms used today have the power to compete with automatic behaviour tendencies strongly associated with positive affect, as they are found in addictions. The main question of this study is if affective information can be included into the implementation intentions. I hypothesize that affective priming just before engaging in forming intention implementations will include the affective information in the habit-like-memory of the intention implementations. When the if-stimulus of the intention implementations is perceived, the affective information will be activated together with the behaviour plan and in turn enable intention implementations to compete with the old habit.

2b. Methodological Approach (research methods, procedures, instruments, data collection)

Experiment 1. Affective priming and implementation intentions

Experiment 1 is designed to test the hypothesis concerning the influence of affective priming on implementation intentions. It is hypothesized that positive affective priming will affect implementation intentions. More specifically the affective priming is expected to enhance the effect of implementation intentions for participants with a weak prior habit, but not for participants with a strong habit already in place.

Method: Following the paradigm of Aarts en Dijksterhuis (2000), only university students who live in Nijmegen and own a bike are recruited. Each participant is randomly assigned to one of the conditions.

In a pilot study, locations for which a bike (bus, train, walking) would be a useful means of transportation are obtained. The five locations that have been indicated by a high percentage of participants to be suited for going to by bike are selected. Subsequently the most frequently mentioned reason to travel to this location is chosen. These reasons to travel constitute the description of the travel goals that are supplied to the participants during the experiment. In addition another five locations are chosen that have been judged to not be suitable for bike travel. This is repeated for all travel modes, so 40 travel mode-location pairs are created. Only the five pairs that consist of a biking-location and in which biking is the travel mode are selected as targets. The other 35 pairs are used as filler trials.

The experiment is run by a computer, which also provides all instructions. The students are told that the study consists of several experiments, designed by different experimenters and that one of the tasks will be to bike to one of the five (biking-congruent) travel goals, as part of an experiment that studies the influence of planning on behaviour. It is import to note that only the goal and the travel mode (biking) is primed, not the actual location. Participants then learn that the travel goal they will need to attain will be revealed in the last part of the experiment.

Then they are asked to complete an alleged reaction time experiment in which they have to zoom in or out pictures of transportation modes (bike, bus, train, walking), depending on the orientation of the picture, by the use of a joystick (pull or push). The approach-avoidance test (AAT) is usually used to measure implicit affective attitude, but it has been suggested, that it can also be used to change affective valence. Each participant completes two trials of 80 pictures (20 for each transportation mode). In the pull-bike condition participants have to pull 90% of the bike pictures and 50% of the images depicting other transportation modes. In the control condition images of each transportation mode must be pulled 50% of the time.

Each participant then engages in the planning task, in which they plan the five travel goals. They are asked to indicate the time of the day, the location of the district of the destination and the travel route suitable for the travel goal. Next, participants complete an association task in which they are presented with an images of 40 different locations (200 ms), that are followed by a row of asterisks (100 ms) and an image depicting a mode of transportation. Participants have to indicate as fast as possible if the depicted transportation mode is a suitable option for going to the depicted location. The dependent measurement is the mean response latency time across the five target location-bicycle pairs.

In the last task participants are presented with 10 different travel locations, which consist of the 5 target locations and 5 nearby locations. The are asked to indicate how often they used the bike to reach any of these location during the last 2 weeks. The median of the frequency is used to separate those participants that already have a habit of biking to the target locations from those that do not (or to a smaller extend). In later experiments it could be advisable to measure the habit-strength in advance and only admit those participants to the experiment that do not already have a habit formed.

It is expected that participants of the pull-bike condition react faster to the target location-bicycle pairs than participants of the control condition, but only if they did not have strong habits already.

Experiment 2. The duration of affective enhanced implementation intentions

Due to the close temporal proximity of the bike-AAT and the association task, the effect of the affective prime might be quite small. It is usually presumed that information that contains affective information is better retained by the memory. This makes sense because emotion was (and probably still is) the number one orientation for behaviour in humans. I would expect that the habit-like memory structure of the implementation intention is subject to the same effect. Therefore it would be interesting to have a repeated measure of the dependent variable.

Method: Experiment 2 uses the same procedures and materials as experiment 1 with the exception that the association task is not followed by the habit-strength measurement, but a filler task and a second association task are added into the experimental design. There could also be multiple association tasks. This could easily be realised if the filler tasks would be unrelated different “real” experiments.

I hypothesize that the difference in reaction time between the “normal” implementation intentions and the “affective enhanced” implementation intentions (for low-habit participants) will grow over time. The working mechanism of affect in habit might (simply) be a memory effect.

Experiment 3. Breaking the habit: Negative affect and implementation intentions

Förster, Liberman and Friedman (2007) proposed that a goal pursuit system is capable of structuring and restructuring the associative network in line with the actor’s goals and the situational affordances. By introducing a third condition (bike-push-condition) during the AAT, the effect of negative affect on implementation intentions can be studied.

Method: Experiment 3 uses the same paradigm as experiment 1 (experiment 2 can also be incorporated without problems), with the exception of the extra condition. Participants in the push-bike condition have to push 90% pf the bike pictures away from themselves. The images of the other modes of transportation receive an equal 50% push/pulling rate. I hypothesize that participants in the push-bike condition will show slower reaction times on the association task if they did not have the habit in advance. It would be remarkable if negative affect would also have an effect on reaction times on participants who already have a strong habit link. Yet the effect might be enhanced by multiple training session with the AAT.

2c. Societal Relevance

The relevance of this study can be explained best by the words of the famous William James:

“Ninety-nine hundredths or, possibly, nine hundred and ninety-nine thousandths of our activity is purely automatic and habitual, from our rising in the morning to our lying down each night.”.

Only in the last few years has behavioural sciences acknowledged that human behaviour is shaped by automatic tendencies in many and complex ways. However these automatic behaviour tendencies do not always elicit the behaviour we (consciously) want, but can lead to many problems and illnesses. Most problems western society faces today, such as drug abuse or eating disorders, can be explained in terms of addiction, which is just a name for a dysfunctional habit that displays certain properties. Being able to change these dysfunctional habits would benefit not only severe cases, but virtually everyone who wants to get rid of an annoying habit.

2d. Literature references

Aarts, H., Custers, R., Holland, R.W. (2007). The nonconscious cessation of goal pursuit: when goals and negative affect are coactivated. Journal of Personality and Social Psychology, 92, 165-178.

Aarts, H., Dijksterhuis, A., (2000). Habit as Knowledge Structures: Automaticity in Goal-Directed Behaviour. Journal of Personality and Social Psychology, 78, 53-63.

Aarts, H., Gollwitzer, P. M., & Hassin, R. R. (2004). Goal contagion: Percieving is for pursuing. Journal of Personality and Social Psychology, 92, 631-642.

Cuusters, R., Aarts, H., (2005). Positive Affect as Implicit Motivator: On the Nonconscious Operation of Behavioral Goals. Journal of Personality and Social Psychology, 89, 129-142.

Förster, J., Liberman, N., Friedman, R. S. (2007). Seven Principles of Goal Activation: A Systematic Approach to Distinguishing Goal Priming From Priming of Non-Goal Constructs. Personality and Social Psychology Review, 11, 211-233.

Holland, R. W., Aarts, H., & Langendam, D. (2006). On the power of implementation intentions: breaking and creating habits on the working floor. Journal of Experimental Social Psychology, 42, 776-783.

Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision und risk. Econometrica, 47, 263-291.

Sheeran, P., Aarts, H., Custers, R., Rivis, A., Webb, T., Cooke, R. (2005). The goal-dependent automaticity of drinking habits. British Journal of Social Psychology, 44, 47-63.