Problem solvers often have multiple operators available to them but must select just one to apply. We present three experiments that demonstrate that solvers use at least two sources of information to make operator selections in the building sticks task (BST): information from their past history of using the operators and information from the current context of the problem. Specifically, problem solvers are more likely to use an operator the more successful it has been in the past and the closer it takes the current state to the goal state. These two effects, respectively, represent the learning and performance processes that influence solvers ’ operator selections. A computational model of BST problem solving, developed within the ACT-R theory (Anderson, 1993), provides the unifying framework in which both types of processes can be integrated to predict solvers ’ selection tendencies. � 1996 Academic Press, Inc. Most problems can be approached in multiple ways but solved by only a few. Problem solving can be viewed, then, as finding one of the few paths that leads from a problem’s initial state to its goal state through some space of possible intermediate states (Newell & Simon, 1972). In this framework,

A requirement of an information processing account of human problem solving is that it includes a mechanism by which people remember which goals and operators have been evaluated and which still need to be evaluated. One might expect that these are issues of such fundamental importance that they must have been solved or at least addressed by the two architectural accounts of cognition (Soar and ACT-R), but in fact it is an issue that is glossed in both. We identify two problems: (1) Soar and ACT-R guarantee information about goals, and (2) ACT-R combines measures of frequency and recency into a single representation of activation. In this paper we report a model of how people search simple binary trees. The model demonstrates the cognitive plausibility of a search algorithm that is supported by a memory system that delivers independent estimates of frequency and recency.

The goal of this research was to describe the ability of human operators to interact with adaptive automation (AA) applied to various stages of complex systems information processing, defined in a model of human-automation interaction. Forty participants operated a simulation of an air traffic control task. Automated assistance was adaptively applied to information acquisition, information analysis, decision making, and action implementation aspects of the task based on operator workload states, which were measured using a secondary task. The differential effects of the forms of automation were determined and compared with a manual control condition. Results of two 20-min trials of AA or manual control revealed a significant effect of the type of automation on performance, particularly during manual control periods as part of the adaptive conditions. Humans appear to better adapt to AA applied to sensory and psychomotor information-processing functions (action implementation) than to AA applied to cognitive functions (information analysis and decision making), and AA is superior to completely manual control. Potential applications of this research include the design of automation to support air traffic controller information processing.

We propose to characterize structural similarity between source and target problems by the type and size of their structural overlap. Size of structural overlap is captured by a measure of graph-distance. We investigated the influence of structural overlap on transfer success in analogical problem solving in two experiments. In both experiments, for a fixed source problem one of five target problems had to be solved. In the first experiment, target problems varied in superficial and structural similarity to the source. In the case of isomorphic source/target relations superficial similarity had no impact on transfer success while for a partial isomorphic target solution success was only high if source and target had identical surface attributes. In the second experiment, surface of source and target were kept identical and different types of structural source/target relations were investigated: For problems with a high structural overlap source inclusive and target exhaustive source/ta...

In analogical problem solving, non-isomorphic source/target relations are typically only investigated in contrast to the ideal case of isomorphism. We propose to give a closer look to different types of non-isomorphic source/target relations and varying degrees of structural overlap. We introduce a measure of graph distance which captures the "size" of partial isomorphism between two structures and we present two experiments investigating the influence of different non-isomorphic relations on analogical transfer. In the first experiment we contrast transfer performance for isomorphic vs. source inclusive problems with high vs. low superficial similarity. In the second experiment we explore different types of partial isomorphisms: source inclusiveness, target exhaustiveness, and different degrees of source/target overlap. The results indicate that (1) transfer of isomorphs is not significantly influenced by superficial similarity but transfer of partial isomorphs is, and ...

Many important computer applications require that users be able to make effective use of them with little or no formal training. Current examples include bank teller machines and airport information kiosks. Today, successful systems of this kind can only be developed by iteration using costly empirical testing. This paper aims to provide a theoretical foundation for the design of such systems, a model of learning by exploration called CE+. The theory incorporates assumptions from 1) the GOMS model and Cognitive Complexity Theory on the representation of procedural knowledge as productions, 2) the EXPL model on learning from examples, and 3) research on problem solving processes for simple puzzle-like problems. Design guidelines for systems that can be learned by exploration, Design-ForSuccessful -Guessing, are derived from the theory. These principles are compared to those developed by Norman (1988). TABLE OF CONTENTS 2. REPRESENTING AND QUANTIFYING PROCEDURAL KNOWLEDGE A CCT Example 3. LEARNING PROCEDURES FROM EXAMPLES: THE EXPL MODEL Back Chaining 8. NORMAN'S ACTION MODEL AND DESIGN GUIDELINES. This paper proposes a cognitive theory of initial learning in humancomputer interaction and an associated design framework to support development of systems that require minimal learning on the part of users. Recent advances in cognitive and machine theories of learning, combined with ideas from earlier work on problem-solving, offer the hope that principled design of such systems may be possible.