How to implement HyGene into ACT-RFootnote^*

* This research includes and extends work first published elsewhere (Dimov, Citation2016).

References

• Anderson, J. R. (2007). How can the human mind occur in the physical universe? New York: Oxford University Press.
   Google Scholar
• Anderson, J. R., & Douglass, S. (2001). Tower of Hanoi: Evidence for the cost of goal retrieval. Journal of Experimental Psychology: Learning, Memory, and Cognition, 27, 1331–1346.
   PubMed Web of Science ®Google Scholar
• Anderson, J. R., Fincham, J. M., & Douglass, S. (1999). Practice and retention: A unifying analysis. Journal of Experimental Psychology: Learning, Memory and Cognition, 25, 1120–1136.
   PubMed Web of Science ®Google Scholar
• Anderson, J. R., Fincham, J. M., Schneider, D. W., & Yang, J. (2012). Using brain imaging to track problem solving in a complex state space. NeuroImage, 60, 633–643. doi: 10.1016/j.neuroimage.2011.12.025
   PubMed Web of Science ®Google Scholar
• Anderson, J. R., Qin, Y., Sohn, M. H., Stenger, V. A., & Carter, C. S. (2003). An information-processing model of the BOLD response in symbol manipulation tasks. Psychonomic Bulletin & Review, 10, 241–261. doi: 10.3758/BF03196490
   PubMed Web of Science ®Google Scholar
• Anderson, J. R., & Ross, B. H. (1980). Evidence against a semantic-episodic distinction. Journal of Experimental Psychology: Human Learning and Memory, 6, 441–466.
   Google Scholar
• Borst, J. P., & Anderson, J. R. (2015a). Using the ACT-R cognitive architecture in combination with fMRI data. In An introduction to model-based cognitive neuroscience (pp. 339–352). New York: Springer.
   Google Scholar
• Borst, J. P., & Anderson, J. R. (2015b). The discovery of processing stages: Analyzing EEG data with hidden semi-Markov models. NeuroImage, 108, 60–73. doi: 10.1016/j.neuroimage.2014.12.029
   PubMed Web of Science ®Google Scholar
• Borst, J. P., Ghuman, A. S., & Anderson, J. R. (2016). Tracking cognitive processing stages with MEG: A spatio-temporal model of associative recognition in the brain. NeuroImage, 141, 416–430. doi: 10.1016/j.neuroimage.2016.08.002
   PubMed Web of Science ®Google Scholar
• Davelaar, E. J., Goshen-Gottstein, Y., Ashkenazi, A., Haarmann, H. J., & Usher, M. (2005). The demise of short-term memory revisited: Empirical and computational investigations of recency effects. Psychological Review, 112, 3–42. doi: 10.1037/0033-295X.112.1.3
   PubMed Web of Science ®Google Scholar
• Diana, R. A., Reder, L. M., Arndt, J., & Park, H. (2006). Models of recognition: A review of arguments in favor of dual-process account. Psychonomic Bulletin & Review, 13, 1–21. doi: 10.3758/BF03193807
   PubMed Web of Science ®Google Scholar
• Dimov, C. M. (2016). Connections between ACT-R’s declarative memory system and Minerva2. In A. Papafragou, D. Grodner, D. Mirman, & J. C. Trueswell (Eds.), Proceedings of the 38th annual conference of the cognitive science society (pp. 492–495). Austin, TX: Cognitive Science Society.
   Google Scholar
• Dimov, C. M., & Link, D. (2017). Do people order cues by retrieval fluency when making probabilistic inferences? Journal of Behavioral Decision Making, 30(4), 843–854. doi: 10.1002/bdm.2002
   Web of Science ®Google Scholar
• Dimov, C. M., Marewski, J. N., & Schooler, L. J. (2013). Constraining ACT-R models of decision strategies: An experimental paradigm. In M. Knauff, M. Pauen, N. Sebanz, & I. Wachsmuth (Eds.), Proceedings of the 35th annual conference of the cognitive science society (pp. 2201–2206). Austin, TX: Cognitive Science Society.
   Google Scholar
• Dougherty, M. R., Gettys, C. F., & Ogden, E. E. (1999). MINERVA-DM: A memory processes model for judgments of likelihood. Psychological Review, 106, 180–209. doi: 10.1037/0033-295X.106.1.180
   Web of Science ®Google Scholar
• Einhorn, H. J., & Hogarth, R. M. (1978). Confidence in judgment: Persistence of the illusion of validity. Psychological Review, 85, 395–416. doi: 10.1037/0033-295X.85.5.395
   Web of Science ®Google Scholar
• Fechner, H. B., Pachur, T., Schooler, L. J., Mehlhorn, K., Battal, C., Volz, K. G., & Borst, J. P. (2016). Strategies for memory-based decision making: Modeling behavioral and neural signatures within a cognitive architecture. Cognition, 157, 77–99. doi: 10.1016/j.cognition.2016.08.011
   PubMed Web of Science ®Google Scholar
• Fiedler, K. (1996). Explaining and simulating judgment biases as an aggregation phenomenon in probabilistic, multiple-cue environments. Psychological Review, 103, 193–214. doi: 10.1037/0033-295X.103.1.193
   Web of Science ®Google Scholar
• Fiedler, K. (2000). Beware of samples! A cognitive-ecological sampling approach to judgment biases. Psychological Review, 107, 659–676. doi: 10.1037/0033-295X.107.4.659
   PubMed Web of Science ®Google Scholar
• Gigerenzer, G., Hoffrage, U., & Kleinbölting, H. (1991). Probabilistic mental models: A Brunswikian theory of confidence. Psychological Review, 98, 506–528. doi: 10.1037/0033-295X.98.4.506
   PubMed Web of Science ®Google Scholar
• Gonzalez, C., Lerch, J. F., & Lebiere, C. (2003). Instance-based learning in dynamic decision making. Cognitive Science, 27, 591–635. doi: 10.1207/s15516709cog2704_2
   Web of Science ®Google Scholar
• Hintzman, D. L. (1984). Minerva 2: A simulation model of human memory. Behavior Research Methods, Instruments, & Computers, 16, 96–101. doi: 10.3758/BF03202365
   Google Scholar
• Hintzman, D. L. (1988). Judgments of frequency and recognition memory in a multiple-trace memory model. Psychological Review, 95, 528–551. doi: 10.1037/0033-295X.95.4.528
   Web of Science ®Google Scholar
• Lange, N. D., Davelaar, E. J., & Thomas, R. P. (2013). Data acquisition dynamics and hypothesis generation. Cognitive Systems Research, 24, 9–17. doi: 10.1016/j.cogsys.2012.12.006
   Web of Science ®Google Scholar
• Lange, N. D., Thomas, R. P., & Davelaar, E. J. (2012). Temporal dynamics of hypothesis generation: The influences of data serial order, data consistency, and eliciation timing. Frontiers in Psychology, 3, 1–16. doi: 10.3389/fpsyg.2012.00215
   PubMed Web of Science ®Google Scholar
• Marewski, J. N., & Mehlhorn, K. (2011). Using the ACT-R architecture to specify 39 quantitative process models of decision making. Judgment and Decision Making, 6, 439–519.
   Web of Science ®Google Scholar
• Marewski, J. N., & Schooler, L. J. (2011). Cognitive niches: An ecological model of strategy selection. Psychological Review, 118, 393–437. doi: 10.1037/a0024143
   PubMed Web of Science ®Google Scholar
• McKoon, G., Ratcliff, R., & Dell, G. S. (1986). A critical evaluation of the semantic-episodic distinction. Journal of Experimental Psychology: Learning, Memory, and Cognition, 12, 295–306.
   PubMed Web of Science ®Google Scholar
• Mehlhorn, K., Taatgen, N. A., Lebiere, C., & Krems, J. F. (2011). Memory activation and the availability of explanations in sequential diagnostic reasoning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 37, 1391–1411.
   PubMed Web of Science ®Google Scholar
• Mischel, W. (2008). The Toothbrush Problem. Observer. Retrieved from http://www.psychologicalscience.org/observer/the-toothbrush-problem
   Google Scholar
• Nellen, S. (2003). The use of the “take-the-best” heuristic under different conditions, modeled with ACT-R. In F. Detje, D. Dörner, & H. Schaub (Eds.), Proceedings of the fifth international conference on cognitive modeling (pp. 171–176). Germany: Universitätsverlag Bamberg.
   Google Scholar
• Paik, J., Kim, J. W., Ritter, F. E., & Reitter, D. (2015). Predicting user performance and learning in human-computer interaction with the herbal compiler. ACM Transactions on Computer-Human Interaction (TOCHI), 22(5), 1–26. doi: 10.1145/2776891
   Web of Science ®Google Scholar
• Rubin, D. C., & Umanath, S. (2015). Event memory: A theory of memory for laboratory, autobiographical, and fictional events. Psychological Review, 122, 1–23. doi: 10.1037/a0037907
   PubMed Web of Science ®Google Scholar
• Salvucci, D. D., & Anderson, J. R. (2001). Integrating analogical mapping and general problem solving: The path-mapping theory. Cognitive Science, 25, 67–110. doi: 10.1207/s15516709cog2501_4
   Web of Science ®Google Scholar
• Schneider, D. W., & Anderson, J. R. (2012). Modeling fan effects on the time course of associative recognition. Cognitive Psychology, 64, 127–160. doi: 10.1016/j.cogpsych.2011.11.001
   PubMed Web of Science ®Google Scholar
• Schooler, L. J., & Hertwig, R. (2005). How forgetting aids heuristic inference. Psychological Review, 112, 610–628. doi: 10.1037/0033-295X.112.3.610
   PubMed Web of Science ®Google Scholar
• Taatgen, N. A., & Anderson, J. R. (2002). Why do children learn to say broke? A model of learning the past tense without feedback. Cognition, 86, 123–155. doi: 10.1016/S0010-0277(02)00176-2
   PubMed Web of Science ®Google Scholar
• Thomas, R. P., Dougherty, M. R., Sprenger, A. M., & Harbison, J. (2008). Diagnostic hypothesis generation and human judgment. Psychological Review, 115, 155–185. doi: 10.1037/0033-295X.115.1.155
   PubMed Web of Science ®Google Scholar
• Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability. Cognitive Psychology, 5, 207–232. doi: 10.1016/0010-0285(73)90033-9
   Web of Science ®Google Scholar