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

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.

Constructing Causal Knowledge Representation with the Common Model of Cognition

“…A year later, Schoppek (2002) discussed exemplar knowledge in contrast to structural knowledge, where exemplar knowledge is analogous to his previous work with I-O knowledge (Schoppek, 2001). Schoppek (2002) went into greater depth by explicating the differences between implicit and explicit knowledge types and their relation to causal interpretations.…”

“…Then, the chapter continues into how that led to the concept of mechanism and intervention, along with the importance of conditional independence. Last, this chapter discusses how associations, when integrated with a structure for causal interpretations, lead to more human-like models of causal reasoning (Schoppek, 2001;2002).…”

“…Wolfgang Schoppek (2001) discusses how causal interpretation relates to I-O (inputoutput) knowledge. He describes I-O knowledge as knowledge about instances and system states, while describing causal relations as a kind of structural knowledge.…”

“…He describes I-O knowledge as knowledge about instances and system states, while describing causal relations as a kind of structural knowledge. Schoppek (2001) separated groups between I-O knowledge and structural knowledge on a given task. He found task performance was higher with the group that had structural knowledge -knowledge of causal relations; however, their reaction time was slower than the I-O group (Schoppek, 2001).…”

“…Schoppek (2001) separated groups between I-O knowledge and structural knowledge on a given task. He found task performance was higher with the group that had structural knowledge -knowledge of causal relations; however, their reaction time was slower than the I-O group (Schoppek, 2001). Schoppek (2001) (Schoppek, 2001).…”