The acquisition of robust and flexible cognitive skills.

Abstract

The authors introduce a model of skill acquisition that incorporates elements of both traditional models and models based on embedded cognition by striking a balance between top-down and bottom-up control. A knowledge representation is used in which pre- and postconditions are attached to actions. This model captures improved performance due to learning not only in terms of shorter solution times and lower error rates during the task but also in an increased flexibility to solve similar problems and robustness against unexpected events. In 3 experiments using a complex aviation task, the authors contrasted instructions that explicitly stated pre- and postconditions with conventional instructions that did not. The instructions with pre- and postconditions led to better and more robust performance than other instructions, especially on problems that required transfer. The parameters of the model were estimated to obtain a quantitative fit of the results of Experiment 1, which was then successfully used to predict the results of Experiments 2 and 3.

The Effectiveness of Airline Pilot Training for Abnormal Events

“…A second hallmark of expertise is that unlike novices who are easily tripped up when subtle variations on a problem or its context are introduced, experts often seem unfazed by these manipulations (Gentner, 1988). Through their experience of encountering problems in different ways, experts appear to develop a more robust understanding that allows them to recognize and respond to variations on a problem situation (Taatgen et al, 2008). Where novices are derailed, discombobulated, or taken by surprise when problems are presented under novel circumstances, experts characteristically perform as if they have "been there and done that."…”

“…First, many studies have demonstrated that skills that are taught and practiced in a rote manner (using the same presentation each time) are less likely to generalize or transfer than those taught in a more varied or meaningful way (Healy, Schneider, & Bourne, 2012;Kieras & Bovair, 1984;Meredith, 1927;Singley & Anderson, 1989;Taatgen, Huss, Dickison, & Anderson, 2008;Woodrow, 1927). These studies suggest that such practices can leave learners with narrow, memorized understandings of problem situations that do not generalize well to situations that do not match the ones they see in training.…”

Pupil Diameter Tracks the Exploration–Exploitation Trade-off during Analogical Reasoning and Explains Individual Differences in Fluid Intelligence

“…The problem-solving process is a search of an abstract problem space and involves the formulation of hypotheses and subgoals that either succeed or fail (e.g., Taatgen, Huss, Dickison, & Anderson, 2008;Newell & Simon, 1976). There are deep structural parallels, although not strict isomorphism, with reinforcement learning concepts such as reward, punishment, exploration, and exploitation.…”

Framing the Features of Good-Quality Knowledge for Teachers and Students

“…This sense that more valued knowledge is able to generate a greater range of potential responses during problem solving has also been discussed by Perkins and Salomon (1994), Mayer and Wittrock (1996), and Bereiter (1997). Such knowledge is more robust, or in Bruner's (1966) terms more powerful, or of better quality, in the sense of being more widely applicable across a range of problem contexts and in the face of possible disturbances (Taatgen, Huss, Dickison & Anderson, 2008). When the relationship between this dimension and deep learning is considered, it might be argued that the emphasis on learning for understanding in descriptions of deep strategies implies transfer: that is, deep understanding will support transfer.…”