| Reference: | Andreassen, P. B., & Kraus, S. J. (1990). Judgmental extrapolation and the salience of change. Journal of forecasting, 9(4), 347-372. doi:10.1002/for.3980090405
Brehmer, B. (1974). Hypotheses about relations between scaled variables in the learning of probabilistic inference tasks. Organizational Behavior and Human Performance, 11(1), 1-27.
Busemeyer, J. R., Byun, E., Delosh, E. L., & McDaniel, M. A. (1997). Learning functional relations based on experience with input-output pairs by humans and artificial neural networks. In K. Lamberts & D. Shanks (Eds.), Knowledge concepts and categories (pp. 405-437). Cambridge, MA: MIT Press.
Busemeyer, J. R., Dewey, G. I., & Medin, D. L. (1984). Evaluation of exemplar-based generalization and the abstraction of categorical information. Journal of Experimental Psychology: Learning, Memory, and Cognition, 10(4), 638-648. doi:10.1037/0278-7393.10.4.638
Carroll, J. D. (1963). Functional learning: The learning of continuous functional mappings relating stimulus and response continua. Princeton, NJ.
DeLosh, E. L., Busemeyer, J. R., & McDaniel, M. A. (1997). Extrapolation: the sine qua non for abstraction in function learning. J Exp Psychol Learn Mem Cogn, 23(4), 968-986.
Goodwin, P., & Wright, G. (1993). Improving judgmental time series forecasting: A review of the guidance provided by research. International Journal of Forecasting, 9(2), 147-161.
Harvey, N. (1988). Judgmental forecasting of univariate time series. Journal of Behavioral Decision Making, 1(2), 95-110. doi:10.1002/bdm.3960010204
Hogarth, R. M., & Makridakis, S. (1981). Forecasting and planning: An evaluation. Management Science, 27(2), 115-138.
Kalish, M. L., Lewandowsky, S., & Kruschke, J. K. (2004). Population of linear experts: knowledge partitioning and function learning. Psychological review, 111(4), 1072-1099. doi:10.1037/0033-295X.111.4.1072
Koh, K., & Meyer, D. E. (1991). Function learning: Induction of continuous stimulus-response relations. Journal of Experimental Psychology: Learning, Memory, and Cognition, 17(5), 811-836. doi:10.1037/0278-7393.17.5.811
Kusev, P., van Schaik, P., Tsaneva‐Atanasova, K., Juliusson, A., & Chater, N. (2018). Adaptive Anchoring Model: How Static and Dynamic Presentations of Time Series Influence Judgments and Predictions. Cognitive science, 42(1), 77-102.
Lawrence, M., Goodwin, P., O`Connor, M., & Önkal, D. (2006). Judgmental forecasting: A review of progress over the last 25 years. International Journal of Forecasting, 22(3), 493-518. doi:10.1016/j.ijforecast.2006.03.007
Lawrence, M., & Makridakis, S. (1989). Factors affecting judgmental forecasts and confidence intervals. Organizational Behavior and Human Decision Processes, 43(2), 172-187.
Peirce, J. W. (2007). PsychoPy—psychophysics software in Python. Journal of neuroscience methods, 162(1), 8-13.
Roll, R. (1984). Orange juice and weather. The American Economic Review, 74(5), 861-880.
Sniezek, J. A. (1986). The role of variable labels in cue probability learning tasks. Organizational Behavior and Human Decision Processes, 38(2), 141-161.
Stewart, T. R., & Lusk, C. M. (1994). Seven components of judgmental forecasting skill: Implications for research and the improvement of forecasts. Journal of forecasting, 13(7), 579-599. |
