Cognitive Constraint Modeling: A Formal Approach to Supporting Reasoning About Behavior

Cognitive Constraint Modeling: A Formal Approach to Supporting Reasoning About Behavior Andrew Howes (HowesA@cardiff.ac.uk) School of Psychology, Cardiff University, Cardiff, Wales, UK CF10 3YG. Alonso Vera (avera@mail.arc.nasa.gov) NASA Ames Research Center, MS 262-3 Moffet Field, CA 94035. Richard L. Lewis (rickl@umich.edu) Department of Psychology, University of Michigan, Ann Arbor, MI 48109-1109. Michael McCurdy (mmccurdy@arc.nasa.gov) NASA Ames Research Center, MS 262-3 Moffet Field, CA 94035. Abstract asymptotic bounds on skilled behavior. The specific objectives of the paper are: (1) To introduce the hypothesis that skilled behavior is the optimal solution to a constraint satisfaction problem defined by architecture, task environment, and knowledge constraints. (2) To introduce a formal modeling approach, called CCM, that directly supports reasoning about the optimal bounds on skilled behavior. By using deductive inference and constraint satisfaction algorithms, CCM computes the necessary consequences of the constraints imposed by the task environment, by strategic knowledge, and by the cognitive architecture. These constraints may determine, for example, which cognitive and environmental processes can execute in parallel and which have sequential dependencies. (3) To specify two ontologies which provide alternative information processing vocabularies for the cognitive and task theory, and the resulting descriptions of behavior. The first is a straightforward formalization of temporal dependencies, implicit in existing work based on CPM- GOMS. The second is a richer ontology that permits specifying sets of communicating information processes, where both the processes, inter-process communication channels and buffers are subject to resource constraints. This framework has much in common with McClelland's cascade model (McClelland, 1979). Both ontologies are formally defined by a set of declarative axioms that are part of the model specification. The paper has the following structure. We first introduce the background to our work on CCM and then describe a reasoning tool, called CORE (Constraint-based Optimal Reasoning Engine). We describe the application of CORE, using the temporal dependency axioms, to reasoning about a dual task experiment first reported by Schumacher, Lauber, Glass, Zubriggen, Gmeindl, Kieras, Meyer (1999) and subsequently modeled by Byrne and Anderson with ACT- R/PM (Byrne and Anderson, 2001). In doing so we show that CORE is flexible enough to support inference about the implications of both central and peripheral bottleneck theories of dual task performance. CORE requires 42 simple, universally quantified, declarative statements to Cognitive Constraint Modeling (CCM) is an approach to reasoning about behavior that (1) provides a framework for investigating the hypothesis that skilled behavior is the optimal solution to a constraint satisfaction problem defined by objective, environmental, knowledge, and architectural constraints, (2) derives predictions of behavior from formal specifications of theory, (3) supports reasoning using both dependency-based and cascade-based ontologies for expressing temporal relationships between processes. A software tool that demonstrates the potential advantages of CCM is described. The tool, called CORE, can be used to partially automate the generation of behavioral predictions given a specification of the constraints. We explore the application of CORE to dual-task data previously modeled with EPIC and ACT-R. Introduction When people acquire a skill they are able to adapt their behavior so as to incrementally improve the value of some utility function. With practice, the scope for improvement attenuates and performance asymptotes. It may asymptote at a level that is consistent with constraints imposed by the environment or perhaps at a level determined by the knowledge that is brought to the task. The bounds may instead be imposed by the human cognitive architecture. More plausibly, the asymptote may be determined by a combination of constraints, including the stochastic and temporal profiles of the task environment and the human cognitive, perceptual, and motor systems. The approach to the asymptote is bounded by a multiplicity of constraints (Simon, 1992). There has of course been much work aimed at modeling skilled behavior and its acquisition (e.g. Anderson and Lebiere, 1998; Meyer and Kieras, 1997; Taatgen and Anderson, 2003). The purpose of the current paper is to provide an initial demonstration of how models of skilled behavior can be generated by the formal derivation of behavior descriptions from multiple constraints, and in particular, how this approach supports reasoning about