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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.

Embodied Embedded Cognition

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The term Embodiment refers to the idea that the internal milieu of the body (such as hormone levels or other homeostatic functions) plays an important role in the processes usually attributed to more higher cognitive processes. This influence is probably achieved through manipulation of emotional states, as suggested by Damasio (1994).

“Embedded” describes the quality of reciprocal interaction between the body and the physical world, which in turn gives rise to cognitive processes.

For a long time the human mind was thought to be totally different from those of animals. It was assumed that sensory data is elaborated by the human mind and stored in a network of abstract representations that are of a semantic structure. The currently dominant paradigm sees the human mind as essentially being a computational-representational system. Within the current paradigm the ultimate explanation for behavior lies within the virtual cognitive functions (software) that are computed by the brain (hardware). Those virtual cognitive functions handle the sensory input, compute a solution and perform output (behavior).

In opposition to this paradigm, the theory of Embodied Embedded Cognition postulates that the difference between the hardware and the software is a semantic one. The metaphor Hardware describes the materialistic, biological aspects of the brain, whereas the metaphor software focuses on the functional aspects. This does not mean that these are two different “things”. Body, brain and world form a system. The intelligent behavior arises from the interaction of the different parts. No a-modal representational system is required to connect a meaning to a symbol, but only a modal system of representations (see also Rolls (1997)).

Specific neurons are activated when they perceive a stimulus, let’s say a car. Through repetition, neuronal activity gets connected to the “real thing” (the car). When enough items of one category have been perceived, we are then able to generate a prototype (Simulators). The idea of this prototype consists of the neural activity that most of the items in one category share. You can also start with the prototype and imagine how an unknown face would look like, by slightly changing the neuronal “fingerprint” of the prototype.

Is there scientific evidence for the Embodied Embedded Cognition Theory?

First of all the a-modal representation has, per definition, all capabilities of a Turing-machine. It is therefore able to explain everything and thus nothing, so the merit as scientific theory is questionable.

Secondly, Embodied Embedded Cognition Theory has postulated some specific hypotheses that have been tested experimentally. For example, specific predictions have been made concerning the spread of related words in an a-modal, a semantic network and a modal network. Those specific predictions have been shown to be true for the human representational system (see Wong & Yon (1991). This has greatly increased the scientific weight of the theory. In comparison, the a-modal theory has never generated this kind of specific hypotheses.

Hopefully greater resolution of brain scans will help to uncover more of the many secrets the human mind still has to offer.

References

Damasio, A.R. (1994). Descartes’ Error: emotion, reason, and the human brain. New York: Grosset/Putnam.
Rolls, E.T. (1997). “Consciousness in neural networks”. Neural Networks, 10, 1227-1240.

Wong, S.K M. & Yao, Y. Y. (1991). A probabilistic inference model for information retrieval. Transactions on Information Systems 16, 301-321.

Written by Martin Glanert

October 9, 2007 at 1:16 pm