Exploring function estimators as an alternative to regression in psychology

Behavior Research Methods

Volumn 37, Issue 1, 2005, Pages 23-36

  Walker, Ian ^a,b   Milne, Sarah ^a  

^a UNIVERSITY OF BATH   (United Kingdom)

^b University of Bath   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ADOLESCENT; ADULT; ARTICLE; ARTIFICIAL NEURAL NETWORK; ATTITUDE TO HEALTH; HUMAN; INFORMATION PROCESSING; MALE; PSYCHOLOGY; QUESTIONNAIRE; REGRESSION ANALYSIS; SELF EXAMINATION; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL; STATISTICS; TESTIS TUMOR; UTILIZATION REVIEW;

ADOLESCENT; ADULT; DATA COLLECTION; HEALTH KNOWLEDGE, ATTITUDES, PRACTICE; HUMANS; LINEAR MODELS; MALE; MODELS, STATISTICAL; NEURAL NETWORKS (COMPUTER); PSYCHOLOGY, EXPERIMENTAL; QUESTIONNAIRES; REGRESSION ANALYSIS; SELF-EXAMINATION; SENSITIVITY AND SPECIFICITY; TESTICULAR NEOPLASMS;

EID: 25144452510     PISSN: 1554351X     EISSN: None     Source Type: Journal    
DOI: 10.3758/BF03206395     Document Type: Article

References (31)

1. BISHOP, C. M. (1995). Neural networks for pattern recognition. Oxford: Oxford University Press.
2. CALLAN, R. (1999). The essence of neural networks. Hemel Hempstead, U.K.: Prentice-Hall.
3. CARUANA, R.. LAWRENCE, S., & GILES, C. L. (2000, November). Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping. Paper presented at Neural Information Processing Systems, Denver.
4. DASL PROJECT (2004). The data and story library. Retrieved May 4, 2004 from http://lib.stat.cmu.edu/DASL/.
5. DAWSON, C. K., O'REILLY, R. C., & MCCLELLAND. J. L. (2002). PDP++ software users' manual (Version 2.2). Retrieved December 13, 2002 from http://www.cnbc.cmu.edu/PDP++/PDP++.html.
6. ELMAN. J. L., BATES, E. A., JOHNSON, M. H., KARMILOFF-SMITH, A., PARISI. D.. & PLUNKETT, K. (1996). Rethinking innateness: A connectionist perspective on development. Cambridge, MA: MIT Press.
7. FRIEDMAN, J. H. (1991). Multivariate adaptive regression splines. Annals of Statistics, 19, 1-141.
8. GIGERENZER, G. (2000). Adaptive thinking: Rationality in the real world. Oxford: Oxford University Press.
9. HASTIE, T., TIBSHIRANI, R.. & FRIEDMAN, J. [H.] (2001). The elements of statistical learning: Data mining, inference, and prediction. New York: Springer-Verlag.
10. HORNIK, K., STINCHCOMBE, M., & WHITE, H. (1989). Multilayer feed-forward networks are universal approximators. Neural Networks, 2, 359-366.
11. LAWRENCE, S., & GILES, C. L. (2000, July). Overfitting and neural networks: Conjugate gradient and backpropagation. Paper presented at the International Joint Conference on Neural Networks, Como, Italy.
12. LAWRENCE, S.. GILES, C. L.. & TSOI, A. C. (1997). Lessons in neural network training: Overfitting may be harder than expected. In Proceedings of the Fourteenth National Conference on Artificial Intelligence (AAAI-97) (pp. 540-545). Menlo Park, CA: AAAI Press.
13. MATHWORKS (2001a). Matlab (Version 6.1) [computer software]. Natick, MA: Author.
14. MATHWORKS (2001b). Neural network toolbox user's guide (Version 4). Natick, MA: Author.
15. MCLEOD, P., PLUNKETT, K., & ROLLS, E. T. (1998). Introduction to connectionist modelling of cognitive processes. Oxford: Oxford University Press.
16. NIGRIN, A. (1993). Neural networks for pattern recognition. Cambridge, MA: MIT Press.
17. NIKZAS, D., CHAMPION. A. E., & Fox, M. (1990). Germ cell tumours of testis: Prognostic factors and results. European Urology, 18, 242-247.
18. PLUNKETT, K.. & ELMAN, J. L. (1997). Exercises in rethinking innateness: A handbook for connectionist simulations. Cambridge, MA: MIT Press.
19. ROBINSON, T. (1993). Artificial neural networks: The mole-grips of the speech scientist. In M. Cooke, S. Beet, & M. Crawford (Eds.), Visual representations of speech signals (pp. 83-94). Chichester, U.K.: Wiley.
20. ROGERS, R. W. (1975). A protection motivation theory of fear appeals and attitude change. Journal of Psychology, 91, 93-114.
21. ROGERS. R. W. (1983). Cognitive and physiological processes in fear appeals and attitude change: A revised theory of protection motivation. In J. T. Cacioppo & R. E. Petty (Eds.), Socialpsychophysiology: A sourcebook (pp. 153-176). New York: Guilford.
22. RUMELHART, D. E., & MCCLELLAND, J. L. (1986). Parallel distributed processing: Explorations in the microstructure of cognition (Vol. 1). Cambridge, MA: MIT Press.
23. SEIDENBERG, M. S., & MCCLELLAND, J. L. (1989). A distributed, developmental model of word recognition and naming. Psychological Review, 96, 523-568.
24. SETIONO, R., LEOW, W. K., & ZURADA, J. M. (2002). Extraction of rules from artificial neural networks for nonlinear regression. IEEE Transactions on Neural Networks, 13, 564-577.
25. STERNBERG, S. (1966). High-speed scanning in human memory. Science, 153, 652-654.
26. STONE, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society, B36, 111-147.
27. SUTTON, S. R. (1982). Fear-arousing communications: A critical examination of theory and research. In J. R. Eiser (Ed.), Social psychology and behavioral medicine (pp. 303-337). New York: Wiley.
28. TABACHNICK, B. G., & FIDELL, L. S. (2001). Using multivariate statistics (4th ed.). Boston, MA: Allyn & Bacon.
29. TICKLE, A. B., ANDREWS, R., GOLEA, M., & DIEDERICH, J. (1998). The truth will come to light: Directions and challenges in extracting the knowledge embedded within trained artificial neural networks. IEEE Transactions on Neural Networks, 9, 1057-1068.
30. WESTBURY, C., BUCHANAN, L., SANDERSON, M., RHEMTULLA, M., & PHILLIPS, L. (2003). Using genetic programming to discover nonlinear variable interactions. Behavior Research Methods, Instruments, & Computers, 35, 202-216.
31. YERKES, R. M., & DODSON, J. D. (1908). The relation of strength of stimulus to rapidity of habit formation. Journal of Comparative Neurology & Psychology, 18, 459-482.