An account of cognitive flexibility and inflexibility for a complex dynamic task

Problem solving involves adapting known problem solving methods and strategies to the task at hand (Schunn & Reder, 2001) and cognitive flexibility is considered to be “the human ability to adapt the cognitive processing strategies to face new and unexpected conditions of the environment” (Cañas et al., 2005, p. 95). This work presents an ACT-R 6.0 model of complex problem solving behavior for the dynamic microworld game FireChief (Omodei & Wearing, 1995) that models the performance of participants predisposed to behave either more or less flexibly based on the nature of previous training on the task (Cañas et al., 2005). The model exhibits a greater or lesser degree of cognitive inflexibility in problem solving strategy choice reflecting variations in task training. The model provides an explanation of dynamic task performance compatible with the Competing Strategies paradigm (Taatgen et al., 2006) by creating a second layer of strategy competition that renders it more flexible with respect to strategy learning, and provides an explanation of cognitive inflexibility based on reward mechanism

Cognitive modelling of complex problem solving behaviour

In the universe of problems humans face every day there is subset characterized by a salient dynamic component. The FireChief task (Omodei & Wearing 1995) is a fire-fighting computer simulation that can be characterized as the acquisition of interactive skills involving fast-paced actions cued by external information. This research describes the process followed to create a cognitive model of this complex dynamic task where full experimental control is not available. The cognitive model provides a detailed description of how cognition and perception interplay to produce the interactive skill of fighting the fire. Several artefacts were produced by this effort including a dynamic task fully compatible with ACT-R, a tool for analysing the data, and a cognitive model whose features enable the replication of several aspects of the empirical data. A key finding is that good performance is linked to an effective combination of strategic control with attention to changing task demands, reflecting time and care taken in informing and effecting action. The contributions of this work towards our understanding of complex problem solving are the methodological approach to the creation of the model, the design patterns embedded in the model (which are a reflection of the cognitive demands imposed by the nature of the task) and mainly an explanation of how skill, described in terms of strategy use, is acquired in complex scenarios. This study also provides a deeper understanding of the interactions observed in the Cañas et al. (2005) dataset, including a computational realisation of how cognitive inflexibility occurs

Cognitive Modeling of Strategies in Dynamic Tasks

Computer simulations, or microworlds, have been used for studying various topics including problem solving. This work investigates strategies for complex, dynamic problem solving in a fire-fighting microworld. Using data from a study by Canas, Antol, Fajardo & Salmern (2005), an ACT-R cognitive model is developed with the aim of providing insight into the development and selection of strategies participants use. One particular behavior observed in participants when trained repetitively on the same scenario, the creation of a fire-break barrier to prevent the fire spreading, is discussed. It was found that selection of a particular strategy depends on the fine-tuning of ACT-R production rule utilities as a consequence of environmental rewards, highlighting the role of reward size and timing. The model is able to capture various aspects of the data by promoting a free competition of small blocks of behavior based on rational analysis. A key finding is that good performance is linked to effective combination of strategic control with attention to changing task demands reflecting time and care taken in informing and effecting action