Spanning seven orders of magnitude: a challenge for

This paper presents a learning theory pertinent to dynamic decision making (DDM) called instancebased learning theory (IBLT). IBLT proposes five learning mechanisms in the context of a decision-making process: instance-based knowledge, recognition-based retrieval, adaptive strategies, necessity-based choice, and feedback updates. IBLT suggests in DDM people learn with the accumulation and refinement of instances, containing the decision-making situation, action, and utility of decisions. As decision makers interact with a dynamic task, they recognize a situation according to its similarity to past instances, adapt their judgment strategies from heuristic-based to instance-based, and refine the accumulated knowledge according to feedback on the result of their actions. The IBLT’s learning mechanisms have been implemented in an ACT-R cognitive model. Through a series of experiments, this paper shows how the IBLT’s learning mechanisms closely approximate the relative trend magnitude and performance of human data. Although the cognitive model is bounded within the context of a dynamic task, the IBLT is a general theory of decision making applicable to other dynamic environments.

For 25 years, we have been working to build cognitive models of mathematics, which have become a basis for middle- and high-school curricula. We discuss the theoretical background of this approach and evidence that the resulting curricula are more effective than other approaches to instruction. We also discuss how embedding a well specified theory in our instructional software allows us to dynamically evaluate the effectiveness of our instruction at a more detailed level than was previously possible. The current widespread use of the software is allowing us to test hypotheses across large numbers of students. We believe that this will lead to new approaches both to understanding mathematical cognition and to improving instruction. For 25 years, we have been working to understand mathematical cognition through the use of cognitive modeling and applying that knowledge to constructing curricula (both text and software) that are more educationally effective than preexisting approaches. This work has been successful on many levels. It has advanced knowledge of cognition in general and of mathematical cognition in particular; the resulting curricula have proven to be educationally effective in school settings; and the curricula, as commercial products, have found a strong following in the school marketplace. We believe that our development model, which involves a close and continuing relationship among basic research, applied research, and field testing, can serve as a model for other efforts to apply cognitive psychology to education. In this article, we describe some of the history of our efforts, our view of the relationship between basic research and development, and some directions for further research.

Understanding the impact of fatigue on human cognition represents an important challenge in applying research in cognitive science to real-world situations. In this study, we explored the cognitive mechanisms responsible for performance decrements in people doing the Walter Reed Serial Addition/Subtraction Task (SAST) periodically during 88 hrs of total sleep deprivation. In our model, performance on the SAST relies heavily on declarative knowledge of mathematical facts, allowing us to extend fatigue mechanisms associated with procedural knowledge from previous research to include analogous parameters and mechanisms in declarative knowledge in the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture. This research contributes to a comprehensive theory of how the human arousal system impacts cognition and performance.

This paper describes the modeling of human performance in a real-world, embodied, stochastic task: baseball batting. Experimental results were gathered in a virtual reality setup and a Markov model of performance, especially errors, was developed. The focus of this paper is on a model of the task developed in the ACT-R cognitive architecture, most specifically of the critical subtask of generating an expectation for the next pitch. The model required no parameter tuning and provides an a priori account of the results based on the architectural constraints of declarative memory. The Markov and ACT-R models are briefly compared. The broader relevance of the task is discussed and possible applications are suggested.

The computation-as-cognition metaphor requires that all cognitive objects are constructed from a fixed set of basic primitives; prominent models of cognition and perception try to provide that fixed set. Despite this effort, however, there are no extant computational models that can actually generate complex concepts and processes from simple and generic basic sets, and there are good reasons to wonder whether such models may be forthcoming. We suggest that one can have the benefits of computationalism without a commitment to fixed feature sets, by postulating processes that slowly develop special-purpose feature languages, from which knowledge is constructed. This provides an alternative to the fixed-model conception without radical anti-representationlism. Substantial evidence suggests that such feature development adaptation actually occurs in the perceptual learning that accompanies category learning. Given the existence of robust methods for novel feature creation, the assumption of a fixed basis set of primitives as psychologically necessary is at best

The question of whether human behavior could be better explained by assuming that people are capable of extracting from their experience some general principles (rules) or by supposing that they store in memory concrete, individual exemplars (instances) of the situations they deal with was examined in 2 experiments, adopting the Sugar Factory dynamic system control task, that contrasted the predictions of the computational model by Dienes and Fahey (1995) with those deriving from the ACT-R based model developed by Dieter Wallach and coworkers (Lebiere, Wallach, & Taatgen, 1998; Taatgen & Wallach, 2002). The first experiment produced findings that could not be explained by the Dienes & Fahey’s model while being consistent with the model of Wallach. The second experiment, however, obtained results that were at odds with the predictions of the latter. A new model is presented that is able to account for the results of both experiments by assuming that participants improve their performance in the Sugar Factory task by choosing, among a pool of very simple solution strategies, those that are judged increasingly more promising by the ACT-R procedural learning mechanism.

The characteristics of long-term, symbolic learning were investigated using Soar and ACT-R models of a task to rearrange blocks into specific configurations. Long sequences of problems were run collecting data to answer fundamental questions about long-term, symbolic learning. The questions were whether symbolic learning continues indefinitely, how learned knowledge is used, and whether performance degrades over the long term. It was found that in both systems symbolic learning eventually stopped, ACT-R produced three observable phases of learning, and both Soar and ACT-R suffer from the utility problem of degraded performance with continuous on-line learning.

What are the characteristics of long-term learning? We investigated the characteristics of long-term, symbolic learning using the Soar and ACT-R cognitive architectures running cognitive models of two simple tasks. Long sequences of problems were run collecting data to answer fundamental questions about long-term, symbolic learning. We examined whether symbolic learning continues indefinitely, how the learned knowledge is used, and whether computational performance degrades over the long term. We report three findings. First, in both systems, symbolic learning eventually stopped. Second, learned knowledge was used differently in different stages but the resulting production knowledge was used uniformly. Finally, both Soar and ACT-R do eventually suffer from degraded computational performance with long-term continuous learning. We also discuss ACT-R implementation and theoretic causes of ACT-R’s computational performance problems and settings that appear to avoid the performance problems in ACT-R.

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