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The influence of causal interpretation on memory for system states

This paper reports an experiment that investigated the influence of causal interpretation on acquisition and use of two knowledge types about a static system: I-O knowledge (instances of system states) and structural knowledge (knowledge about causal relations within the system). One group of subjects saw system states without being informed about the causal nature of the material. Another group saw the same states as switches and lamps. It is assumed that the group without causal interpretation can only acquire I-O knowledge. If I-O knowledge is the predominant type when dealing with small systems, then there should be no group differences in a recognition task. Actually, the group with causal interpretation discriminates much better between targets and distractors, but with longer RTs. This is interpreted in terms of structural knowledge acquired by the group with causal interpretation, which was used to reconstruct system states in cases of doubt. Results of a task where subjects had to judge single causal relations support that interpretation, but also indicate that the knowledge about effects is probably not represented in an explicit, symbolic form. An ACT-R model that uses associations between events as a subsymbolic form of structural knowledge reproduces the data well. Thus, data and model support the significance of I-O knowledge but also shed some light on the role and the development of structural knowledge. One central question in the psychological research on complex dynamic systems refers to the knowledge that is used for controlling a system. One important aspect of that question refers to the content of the acquired knowledge. Subjects may acquire structural knowledge, defined as general knowledge about the variables of a system and their causal relations. They may as well acquire input-output knowledge (I-O knowledge), which represents instances of input values and the corresponding output values. There is evidence for the influence of both types of knowledge on performance in system control, but currently many authors emphasize the role of I-O knowledge, particularly when dealing with small systems like the "Sugar Factory" (a dynamic system with one input and one output variable, connected by a linear equation; Berry & Broadbent, 1988). Computational models developed on the basis of Logan's Instance Theory (Dienes & Fahey, 1995) or ACT-R (Lebiere, Wallach & Taatgen, 1998) demonstrate the sufficiency of I-O knowledge for the control of the "Sugar Factory". The strategy of relying on I-O knowledge seems to be preferred by most subjects, even in the control of more complex systems. However, in systems of at least six variables, high performance is usually associated with structural knowledge (Funke, 1993; Vollmeyer, Burns & Holyoak, 1995). A second aspect of the question as to what knowledge is used in system control refers to its status as explicit or implicit knowledge. In an experiment with the "Sugar Factory", Dienes and Fahey (1998) found stochastic independence between the solution of studied control problems and the recognition of the same situations as studied. The authors concluded that memory for the situations was implicit. This result extends the common finding of dissociations between recognition and completion tasks (e.g. Tulving & Hayman, 1993) to the domain of system control. In the present paper these questions were investigated by using stimuli that can be either interpreted as states of a system or simply as spatial patterns. The rationale of the experiment is that learning of instances does not depend on the causal interpretation of stimuli. Consequently, if knowledge about instances (I-O knowledge) is the main knowledge type learned, there should be no effect of causal interpretation on recognition of system states. On the other hand, if structural knowledge is learned additionally, then causal interpretation should have positive effects, particularly in a causal judgment task. The assumptions about the two knowledge types are explicated with a computational model based on the ACT-R theory (Anderson & Lebiere, 1998). The model reproduces the results of the experiment quite well, and can be considered being an explanation for the stochastic independence between completion and recognition tasks.