Stochastic Independence between Recognition and Completion of Spatial Patterns as a Function of Causal Interpretation

Abstract

Stochastic Independence Between Recognition and Completion of Spatial Patterns as a Function of Causal Interpretation Wolfgang Schoppek (wolfgang.schoppek@uni-bayreuth.de) Department of Psychology, University of Bayreuth D-95440 Bayreuth, Germany 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 demon- strating 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 depen- dence between various tasks would be expected. However, in an experiment with N=40 subjects the group with causal interpretation is significantly better at com- pleting 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 recogni- tion 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 compu- tational model that is able to reproduce the set of results. Dynamic system control (DSC) is a paradigm of great interest for applied and basic research likewise. In applied contexts, researchers address questions about how human operators learn to operate new technical systems efficiently, how training should be designed, or what errors operators are likely to commit. In basic research, DSC is one of the paradigms for studying implicit learning. It has been argued that subjects control dynamic systems predominantly with exemplar knowledge about system states, part of which is considered implicit (Dienes & Fahey, 1998). This conclusion was derived from studies with systems characterized by small problem spaces, such as the “Sugar Factory” (a dynamic system with one input and one output variable, connected by a linear equation; Berry & Broadbent, 1988). However, studies with more complex systems have delivered evidence that structur- al knowledge (i.e. knowledge about the variables of a system and their causal relations) can be more effective for controlling these systems (Vollmeyer, Burns, & Holyoak, 1996; Funke, 1993), although it is not easy to apply and use this type of knowledge (Schoppek, 2002). But even for small systems, the question about what type of knowledge is learned in an implicit manner, is still open. Simulation studies that have proven the sufficiency of exemplar knowledge for controlling the Sugar Factory (Dienes & Fahey, 1995; Lebiere, Wallach, & Taatgen, 1998) have as yet not reproduced effects that point to implicit learning. An example of such effects is the stochastic independence between recognition of system states of the Sugar Factory as studied and performance in one-step control problems, found by Dienes & Fahey (1998). Since exemplar knowledge is typically construed as explicit rather than implicit, it cannot account for these dissociations. This paper addresses the question if a rudimentary form of structural knowledge is acquired in addition to exemplar knowledge, albeit implicitly or explicitly. The different use of exemplar knowledge and structural knowledge in different tasks can explain dissociations between tasks. The basic strategy for separating the two Figure 1: Problem space of the Switches & Lamps system; the states with white triangles were studied in the learning phase