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Relevant bibliographies by topics / Adaptive Control of Thought-Rational Theory (ACT-R)

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Academic literature on the topic 'Adaptive Control of Thought-Rational Theory (ACT-R)'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Adaptive Control of Thought-Rational Theory (ACT-R)"

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Ilker, Yengin, and Furkan Ince Ibrahim. "Applying the Adaptive Control of Thought-Rational Theory into the Design of Mobile Worked Examples Applications." International Journal of Robots, Education and Art (IJREA) 4, no. 2 (2014): 21–28. https://doi.org/10.5281/zenodo.836350.

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Using a direct problem solving method creates confusion and cognitive load for the novice learners. To address the issue, we applied basics of “Adaptive Control of Thought-Rational Theory (ACT-R) and designed a mobile learning application that uses worked examples using simulation. As an alternative method, this mobile application supports teaching production rules and procedural knowledge in a step-by-step manner. This paper discusses the basics implementations of ACT-R Theory into the design of mobile application that uses worked examples as the main delivery method. The paper would be

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Ruiz Sánchez de León, José María, and Miguel Ángel Fernández Blázquez. "Cognitive architectures and brain: towards an unified theory of cognition." International Journal of Psychological Research 4, no. 2 (2011): 38–47. http://dx.doi.org/10.21500/20112084.776.

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Cognitive architectures are defined as the group of essential components belonging to a system which allows the analysis of its cognitions and behaviors. The aim of this study is to review one of the most plausible cognitive architectures from the neuroanatomic perspective: The Adaptive Control of Thought-Rational (ACT-R) is a theory about how human mind works. Following an initial approach to its basic concepts its two computational levels are described, these are: a symbolic level , which includes declarative information; and a sub-symbolic level which is represented as a parallel set of pro

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Anderson, John R., and Yulin Qin. "Using Brain Imaging to Extract the Structure of Complex Events at the Rational Time Band." Journal of Cognitive Neuroscience 20, no. 9 (2008): 1624–36. http://dx.doi.org/10.1162/jocn.2008.20108.

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A functional magnetic resonance imaging (fMRI) study was performed in which participants performed a complex series of mental calculations that spanned about 2 min. An Adaptive Control of Thought—Rational (ACT-R) model [Anderson, J. R. How can the human mind occur in the physical universe? New York: Oxford University Press, 2007] was developed that successfully fit the distribution of latencies. This model generated predictions for the fMRI signal in six brain regions that have been associated with modules in the ACT-R theory. The model's predictions were confirmed for a fusiform region that r

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Zeng, Shengkui, Qidong You, Jianbin Guo, and Haiyang Che. "Situation Awareness-Based Safety Assessment Method for Human–Autonomy Interaction Process Considering Anchoring and Omission Biases." Journal of Marine Science and Engineering 13, no. 1 (2025): 158. https://doi.org/10.3390/jmse13010158.

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Autonomy is being increasingly used in domains like maritime, aviation, medical, and civil domains. Nevertheless, at the current autonomy level, human takeover in the human–autonomy interaction process (HAIP) is still critical for safety. Whether humans take over relies on situation awareness (SA) about the correctness of autonomy decisions, which is distorted by human anchoring and omission bias. Specifically, (i) anchoring bias (tendency to confirm prior opinion) causes the imperception of key information and miscomprehending correctness of autonomy decisions; (ii) omission bias (inaction te

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Cassenti, Daniel N. "ACT-R Model of EEG Latency Data." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 51, no. 12 (2007): 812–16. http://dx.doi.org/10.1177/154193120705101208.

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Anderson and Lebiere's (1998) modeling system ACT-R (Adaptive Control of Thought – Rational) has been a leading contributor to advances in cognitive science. Despite the modeling system's success there are areas in which it may be improved. The present research advocates a suggested approach to improving ACT-R's predictive capacity by using EEG (electroencephalography) latency data to predict the time it takes to achieve certain mental steps. A model is presented which successfully represents EEG data from a simple auditory experiment. Implications of this modeling approach to ACT-R and to the

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Cassenti, Daniel N., and Andrew L. Reifers. "Counting on ACT-R to Represent Time." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 49, no. 12 (2005): 1167–71. http://dx.doi.org/10.1177/154193120504901215.

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Temporal issues consistently factor into decisions, yet surprisingly few research studies have explored how to model temporal cognition. We developed an Adaptive Control of Thought — Rational (ACT-R, e.g., Anderson & Lebiere, 1998) model to help account for how people estimate time, one of many issues in temporal cognition. According to the model, people adjust the lengths of words through abbreviation or extension and produce the words at a rate in tune with the rate of environmental events. This procedure allows an individual to synchronize with regular intervals of time in the environme

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Rezazadeh, Nader, and Touraj Banirostam. "Presentation of ACT/R-RBF Hybrid Architecture to Develop Decision Making in Continuous and Non-continuous Data." Journal of Intelligent Systems 29, no. 1 (2018): 596–611. http://dx.doi.org/10.1515/jisys-2017-0470.

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Abstract Computational models are based on symbolic architecture. For this reason, computational models function problematically in dynamic, noisy, and continuous environments. The ACT/R (Adaptive Control of Thought-Rational) model is also problematic, as it is purely based on symbolic architecture like other computational models. The ACT/R decision-making process is based on the production operator on the input subject set. This approach firstly does not make a non-linear mapping between input and the decision-making result in ACT/R. Secondly, it is not possible to decide on the input subject

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Smedley, Georgia, and Steve G. Sutton. "The Effect of Alternative Procedural Explanation Types on Procedural Knowledge Acquisition during Knowledge-Based Systems Use." Journal of Information Systems 21, no. 1 (2007): 27–51. http://dx.doi.org/10.2308/jis.2007.21.1.27.

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This study applies Adaptive Character of Thought-Rational (ACT-R), a theory of cognitive skill acquisition, to identify two techniques theorized to provide learners with a simplified and situation-responsive set of production rules to use in a problemsolving context. The two techniques are abstraction (an optimization technique that produces a generalized rule set) and goal structuring (another optimization technique that produces a differentiated rule set). Accordingly, abstraction and goal structuring explanations were provided to users through a knowledge-based system (KBS). Due to cognitiv

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Xu, Shuo, Dawei Tu, Yongyi He, Shili Tan, and Minglun Fang. "ACT-R-typed human–robot collaboration mechanism for elderly and disabled assistance." Robotica 32, no. 5 (2013): 711–21. http://dx.doi.org/10.1017/s0263574713001094.

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SUMMARYThis work aims to propose an innovative mechanism of human–robot collaboration (HRC) for mobile service robots in the application of elderly and disabled assistance. Previous studies on HRC mechanism usually focused on integrating decision-making intelligence of human beings by qualitative judgment and reasoning intelligence of robots by quantitative calculation. Instead, novelties of the proposed methodology include (1) constructing an HRC framework by taking reference from the Adaptive Control of Thought – Rational (ACT-R) human cognitive architecture; (2) establishing semantic webs o

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Du, Na, Xingwei Wu, Teruhisa Misu, and Kumar Akash. "A Preliminary Study of Modeling Driver Situational Awareness based on SEEV and ACT-R Models." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 66, no. 1 (2022): 1602–6. http://dx.doi.org/10.1177/1071181322661246.

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In automated driving, it is important to maintain drivers’ situational awareness (SA) in order to help them avoid unnecessary interventions and negotiate challenging scenarios where human takeovers are needed. Our study developed computational models to predict a driver’s SA of a target object. Using the SEEV (Salience, Effort, Expectancy, and Value) and ACT-R (Adaptive Control of Thought-Rational) framework, the model achieved an accuracy of 78.3%, an F1-score of 0.66, and the area under the receiver operating characteristic (AUROC) value of 0.773 with object features as inputs. On average, t

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Book chapters on the topic "Adaptive Control of Thought-Rational Theory (ACT-R)"

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Kim, Nayoung, and Chang S. Nam. "Adaptive Control of Thought-Rational (ACT-R): Applying a Cognitive Architecture to Neuroergonomics." In Neuroergonomics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-34784-0_6.

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Peebles, David, and Anna L. Cox. "Modelling Interactive Behaviour with a Rational Cognitive Architecture." In Selected Readings on the Human Side of Information Technology. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-088-2.ch013.

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In this chapter we discuss a number of recent studies that demonstrate the use of rational analysis (Anderson, 1990) and cognitive modelling methods to understand complex interactive behaviour involved in three tasks: (1) icon search, (2) graph reading, and (3) information retrieval on the World Wide Web (WWW). We describe the underlying theoretical assumptions of rational analysis and the adaptive control of thought-rational (ACT-R) cognitive architecture (Anderson & Lebiere, 1998), a theory of cognition that incorporates rational analysis in its mechanisms for learning and decision makin

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Peebles, David, and Anna L. Cox. "Modelling Interactive Behaviour with a Rational Cognitive Architecture." In Human Computer Interaction. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-87828-991-9.ch073.

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In this chapter we discuss a number of recent studies that demonstrate the use of rational analysis (Anderson, 1990) and cognitive modelling methods to understand complex interactive behaviour involved in three tasks: (1) icon search, (2) graph reading, and (3) information retrieval on the World Wide Web (WWW). We describe the underlying theoretical assumptions of rational analysis and the adaptive control of thought-rational (ACT-R) cognitive architecture (Anderson & Lebiere, 1998), a theory of cognition that incorporates rational analysis in its mechanisms for learning and decision makin

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Conference papers on the topic "Adaptive Control of Thought-Rational Theory (ACT-R)"

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Dianita, Orchida, Keisuke Takeuchi, Kimi Ueda, Hirotake Ishii, Hiroshi Shimoda, and Fumiaki Obayashi. "The Influence of Worker’s Motivation on Intellectual Concentration by ACT-R Cognitive Models." In Intelligent Human Systems Integration (IHSI 2023) Integrating People and Intelligent Systems. AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1002834.

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IntroductionThe purpose of this study is to examine the worker’s motivation effect on intellectual concentration by an experiment and a cognitive process simulation. By utilizing the Adaptive Control of Thought-Rational (ACT-R) concept, it helps to understand the cognitive models of the different motivational condition. ACT-R is one of the prevalent cognitive architectures that mainly differ the cognitive process into production modules and declarative modules.Several studies have been investigating the human cognitive performance in respect of others factors such as working environments. Insp

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Stevens, Christopher, Christopher Fisher, and Mary Frame. "A Cognitive Model for Guiding Automation." In 14th International Conference on Applied Human Factors and Ergonomics (AHFE 2023). AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1003575.

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A variety of systems and exist for managing human-machine team throughput and effectiveness. One example is autonomous managers (AMs), software that dynamically reallocates tasks to individual members of a team based on their workload and performance. Cognitive models can inform these technologies by projecting performance into the future and enabling “what-if” analyses. For example, would removing a task from an individual whose current performance is low cause them to improve? Conversely, can a team member who is currently performing well handle even more work without dropping performance? I

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Reports on the topic "Adaptive Control of Thought-Rational Theory (ACT-R)"

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Lennon, Craig T. Comparing Adaptive Control of Thought-Rational (ACT-R) Baseline Activation Terms for Implementation in the Symbolic and Subsymbolic Robotic Intelligence Control System (SS-RICS). Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada600879.

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