Latent Problem Solving Analysis as an explanation of expertise effects in a complex, dynamic task

Latent Problem Solving Analysis (LPSA) is a theory of knowledge representation in complex problem solving that argues that problem spaces can be represented as multidimensional spaces and expertise is the construction of those spaces from immense amounts of experience. The model was applied using a dataset from a longitudinal experiment on control of thermodynamic systems. When the system is trained with expert-level amounts of experience (3 years), it can predict the end of a trial using the first three quarters with an accuracy of .9. If the system is prepared to mimic a novice (6 months) the prediction accuracy falls to .2. If the system is trained with 3 years of practice in an environment with no constraints, performance is similar to the novice baseline.

[1]  E. Reed.  The Ecological Approach to Visual Perception , 1989 .

[2]  Kim J. Vicente,et al.  Supporting knowledge-based behavior through ecological interface design , 1991 .

[3]  Walter Kintsch,et al.  A Computational Theory of Complex Problem Solving Using Latent Semantic Analysis , 2002 .

[4]  K J Vicente,et al.  Revisiting the constraint attunement hypothesis: reply to Ericsson, Patel, and Kintsch (2000) and Simon and Gobet (2000). , 2000, Psychological review.

[5]  K. A. Ericsson,et al.  Shortcomings of generic retrieval structures with slots of the type that Gobet (1993) proposed and modelled. , 2000, British journal of psychology.

[6]  Andrian Marcus,et al.  Using latent semantic analysis to identify similarities in source code to support program understanding , 2000, Proceedings 12th IEEE Internationals Conference on Tools with Artificial Intelligence. ICTAI 2000.

[7]  Walter Kintsch,et al.  Comprehension: A Paradigm for Cognition , 1998 .

[8]  Susan T. Dumais,et al.  The latent semantic analysis theory of knowledge , 1997 .

[9]  R. Shepard,et al.  Toward a universal law of generalization for psychological science. , 1987, Science.

[10]  Cleotilde Gonzalez,et al.  Learning in a Dynamic Decision Making Task : The Recognition Process , 2002 .

[11]  Walter Kintsch,et al.  Automatic Landing Technique Assessment using Latent Problem Solving Analysis , 2003 .

[12]  Vimla L. Patel,et al.  The role of long-term working memory in text comprehension. , 1999 .

[13]  H B Richman,et al.  Simulation of expert memory using EPAM IV. , 1995, Psychological review.

[14]  Murray Glanzer,et al.  Short-term storage in the processing of text , 1981 .

[15]  K J Vicente,et al.  An ecological theory of expertise effects in memory recall. , 1998, Psychological review.

[16]  Darrell Laham,et al.  Latent Semantic Analysis Approaches to Categorization , 1997 .

[17]  Kim J. Vicente,et al.  A longitudinal study of the effects of ecological interface design on deep knowledge , 1998, Int. J. Hum. Comput. Stud..

[18]  Herbert A. Simon,et al.  THE MIND'S EYE IN CHESS , 1988 .

[19]  Robert J. Crutcher,et al.  The role of deliberate practice in the acquisition of expert performance. , 1993 .

[20]  K. A. Ericsson,et al.  Long-term working memory. , 1995, Psychological review.

[21]  W. Kintsch.  Metaphor comprehension: A computational theory , 2000, Psychonomic bulletin & review.

[22]  F. Gobet,et al.  Some shortcomings of long-term working memory. , 2000, British journal of psychology.

[23]  Walter Kintsch,et al.  Predication , 2001, Cogn. Sci..

[24]  John R. Anderson,et al.  The Adaptive Character of Thought , 1990 .

[25]  F. Gobet.  Expert memory: a comparison of four theories , 1998, Cognition.

[26]  Kim J. Vicente,et al.  A Longitudinal Study of the Effects of Ecological Interface Design on Skill Acquisition , 1996, Hum. Factors.