ABSTRACT
A common view in the research on dynamic system control is that human subjects use exemplar knowledge of system states – at least for controlling small systems. Dissociations between different tasks or stochastic independence between recognition and control tasks, have led to the assumption that part of the exemplar knowledge is implicit. In this paper, I propose an alternative interpretation of these results by demonstrating that subjects learn more than exemplars when they are introduced to a new system. This was achieved by presenting the same material – states of a simple system – with vs. without causal interpretation. If subjects learned exemplars only, then there should be no differences between the conditions and stochastic dependence between various tasks would be expected. However, in an experiment with N=40 subjects the group with causal interpretation is significantly better at completing fragmentary system states and in judging causal relations between switches and lamps, but not in recognizing stimuli as studied. Only in the group without causal interpretation, the contingency between recognition and completion was close to the maximum memory dependence, estimated with Ostergaard’s (1992) method. Thus, the results resemble those of other studies only in the condition with causal interpretation. The results are explained by the assumption that subjects under that condition learn and use a second type of knowledge, which is construed as a rudimentary form of structural knowledge. The interpretation is supported by a computational model that is able to reproduce the set of results.
