Editing ACT-R (section)

==Applications==
Over the years, ACT-R models have been used in more than 700 different scientific publications, and have been cited in many more.<ref> [http://act-r.psy.cmu.edu/publication/] ACT-R Publications and Published Models - CMU</ref>

===Memory, attention, and executive control===
The ACT-R declarative memory system has been used to model human [[memory]] since its inception.  In the course of years, it has been adopted to successfully model a large number of known effects.  They include the fan effect of interference for associated information,<ref>Anderson, J. R. & Reder, L. M. (1999). The fan effect: New results and new theories. ''Journal of Experimental Psychology: General'', ''128'', 186–197.</ref> [[Primacy effect|primacy]] and [[Recency effect|recency]] effects for list memory,<ref>Anderson, J. R., Bothell, D., Lebiere, C. & Matessa, M. (1998). An integrated theory of list memory. ''Journal of Memory and Language'', ''38'', 341–380.</ref> and serial recall.<ref>Anderson, J. R. & Matessa, M. P. (1997). A production system theory of serial memory. ''Psychological Review, 104'', 728–748.</ref>

ACT-R has been used to model attentive and control processes in a number of cognitive paradigms.  These include the [[Stroop task]],<ref>Lovett, M. C. (2005) A strategy-based interpretation of Stroop. ''Cognitive Science, 29'', 493–524.</ref><ref>Juvina, I., & Taatgen, N. A. (2009). A repetition-suppression account of between-trial effects in a modified Stroop paradigm. ''Acta Psychologica, 131(1)'', 72–84.</ref> [[task switching (psychology)|task switching]],<ref>Altmann, E. M., & Gray, W. D. (2008). An integrated model of cognitive control in task switching. ''Psychological Review, 115'', 602–639.</ref><ref>Sohn, M.-H., & Anderson, J. R. (2001). Task preparation and task repetition: Two-component model of task switching. ''Journal of Experimental Psychology: General''.</ref> the [[psychological refractory period]],<ref>Byrne, M. D., & Anderson, J. R. (2001). Serial modules in parallel: The psychological refractory period and perfect time-sharing. ''Psychological Review, 108'', 847–869.</ref> and multi-tasking.<ref>Salvucci, D. D., & Taatgen, N. A. (2008). Threaded cognition: An integrated theory of concurrent multitasking. ''Psychological Review'', 130(1), 101–130.</ref>

===Natural language===
A number of researchers have been using ACT-R to model several aspects of natural [[language]] understanding and production.  They include models of syntactic parsing,<ref>Lewis, R. L. & Vasishth, S. (2005). An activation-based model of sentence processing as skilled memory retrieval. ''Cognitive Science, 29'', 375–419</ref> language understanding,<ref>Budiu, R. & Anderson, J. R. (2004). Interpretation-Based Processing: A Unified Theory of Semantic Sentence Processing. ''Cognitive Science, 28'', 1–44.</ref> language acquisition <ref>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(2)'', 123–155.</ref> and metaphor comprehension.<ref>Budiu R., & Anderson J. R. (2002). Comprehending anaphoric metaphors. ''Memory & Cognition, 30'', 158–165.</ref>

===Complex tasks===
ACT-R has been used to capture how humans solve complex problems like the Tower of Hanoi,<ref>Altmann, E. M. & Trafton, J. G. (2002). Memory for goals: An activation-based model. ''Cognitive Science'', ''26'', 39–83.</ref> or how people solve algebraic equations.<ref>Anderson, J. R. (2005) Human symbol manipulation within an integrated cognitive architecture. ''Cognitive Science, 29(3)'', 313–341.</ref> It has also been used to model human behavior in driving and flying.<ref>Byrne, M. D., & Kirlik, A. (2005). Using computational cognitive modeling to diagnose possible sources of aviation error. ''International Journal of Aviation Psychology, 15'', 135–155. {{doi|10.1207/s15327108ijap1502_2}}</ref>

With the integration of perceptual-motor capabilities, ACT-R has become increasingly popular as a modeling tool in human factors and human-computer interaction.  In this domain, it has been adopted to model driving behavior under different conditions,<ref>Salvucci, D. D. (2006). Modeling driver behavior in a cognitive architecture. ''Human Factors'', ''48'', 362–380.</ref><ref>Salvucci, D. D., & Macuga, K. L. (2001). Predicting the effects of cellular-phone dialing on driver performance. In ''Proceedings of the Fourth International Conference on Cognitive Modeling'', pp. 25–32. Mahwah, NJ: Lawrence Erlbaum Associates.</ref> menu selection and visual search on computer application,<ref>Byrne, M. D., (2001). ACT-R/PM and menu selection: Applying a cognitive architecture to HCI. ''International Journal of Human-Computer Studies'', ''55'', 41–84.</ref><ref>Fleetwood, M. D. & Byrne, M. D. (2002) Modeling icon search in ACT-R/PM. ''Cognitive Systems Research'', ''3'', 25–33.</ref> and web navigation.<ref>{{Cite journal
 |last1=Fu 
 |first1=Wai-Tat 
 |last2=Pirolli 
 |first2=Peter 
 |title=SNIF-ACT: A cognitive model of user navigation on the World Wide Web 
 |journal=Human-Computer Interaction 
 |pages=355–412 
 |year=2007 
 |volume=22 
 |issue=4 
 |url=http://www.humanfactors.illinois.edu/Reports&PapersPDFs/JournalPubs/FuPirolli07.pdf 
 |url-status=dead 
 |archiveurl=https://web.archive.org/web/20100802225919/http://www.humanfactors.illinois.edu/Reports%26PapersPDFs/JournalPubs/FuPirolli07.pdf 
 |archivedate=2010-08-02 
}}</ref>

===Cognitive neuroscience===
More recently, ACT-R has been used to predict patterns of brain activation during imaging experiments.<ref>Anderson, J.R., Fincham, J. M., Qin, Y., & Stocco, A. (2008). A central circuit of the mind. ''Trends in Cognitive Sciences'', ''12(4)'', 136–143</ref>  In this field, ACT-R models have been successfully used to predict prefrontal and parietal activity in memory retrieval,<ref>Sohn, M.-H., Goode, A., Stenger, V. A, Carter, C. S., & Anderson, J. R. (2003). Competition and representation during memory retrieval: Roles of the prefrontal cortex and the posterior parietal cortex, ''Proceedings of the National Academy of Sciences, 100'', 7412–7417.</ref> anterior cingulate activity for control operations,<ref>Sohn, M.-H., Albert, M. V., Stenger, V. A, Jung, K.-J., Carter, C. S., & Anderson, J. R. (2007). Anticipation of conflict monitoring in the anterior cingulate cortex and the prefrontal cortex. ''Proceedings of National Academy of Science, 104'', 10330–10334.</ref> and practice-related changes in brain activity.<ref>Qin, Y., Sohn, M-H, Anderson, J. R., Stenger, V. A., Fissell, K., Goode, A. Carter, C. S. (2003). Predicting the practice effects on the blood oxygenation level-dependent (BOLD) function of fMRI in a symbolic manipulation task. ''Proceedings of the National Academy of Sciences of the United States of America. 100(8)'': 4951–4956.</ref>

===Education===
ACT-R has been often adopted as the foundation for [[cognitive tutors]].<ref>Lewis, M. W., Milson, R., & Anderson, J. R. (1987). The teacher's apprentice: Designing an intelligent authoring system for high school mathematics. In G. P. Kearsley (Ed.), ''Artificial Intelligence and Instruction''. Reading, MA: Addison-Wesley. {{ISBN|0-201-11654-5}}.</ref><ref>Anderson, J. R. & Gluck, K. (2001). What role do cognitive architectures play in intelligent tutoring systems? In D. Klahr & S. M. Carver (Eds.) ''Cognition & Instruction: Twenty-five years of progress'', 227–262. Lawrence Erlbaum Associates. {{ISBN|0-8058-3824-4}}.</ref> These systems use an internal ACT-R model to mimic the behavior of a student and personalize his/her instructions and curriculum, trying to "guess" the difficulties that students may have and provide focused help.

Such "Cognitive Tutors" are being used as a platform for research on learning and cognitive modeling as part of the Pittsburgh Science of Learning Center.  Some of the most successful applications, like the Cognitive Tutor for Mathematics, are used in thousands of schools across the United States.