Visible to the public Biblio

Filters: Author is David L. Roberts  [Clear All Filters]
2013
Titus Barik, Arpan Chakraborty, Brent Harrison, David L. Roberts, Robert St. Amant.  2013.  Modeling the Concentration Game with ACT-R. The 12th International Conference on Cognitive Modeling.

This paper describes the development of subsymbolic ACT-R models for the Concentration game. Performance data is taken from an experiment in which participants played the game un- der two conditions: minimizing the number of mismatches/ turns during a game, and minimizing the time to complete a game. Conflict resolution and parameter tuning are used to implement an accuracy model and a speed model that capture the differences for the two conditions. Visual attention drives exploration of the game board in the models. Modeling re- sults are generally consistent with human performance, though some systematic differences can be seen. Modeling decisions, model limitations, and open issues are discussed. 

2015
Ignacio X. Dominguez, Alok Goel, David L. Roberts, Robert St. Amant.  2015.  Detecting Abnormal User Behavior Through Pattern-mining Input Device Analytics. Proceedings of the 2015 Symposium and Bootcamp on the Science of Security (HotSoS-15).
Robert St. Amant, Prairie Rose Goodwin, Ignacio Dominguez, David L. Roberts.  2015.  Toward Expert Typing in ACT-R. Proceedings of the 2015 International Conference on Cognitive Modeling (ICCM 15).
2016
Ignacio X. Dominguez, Prairie Rose Goodwin, David L. Roberts, Robert St. Amant.  2016.  Human Subtlety Proofs: Using Computer Games to Model Cognitive Processes for Cybersecurity. International Journal of Human–Computer Interaction. :null.

AbstractThis article describes an emerging direction in the intersection between human-computer interaction and cognitive science: the use of cognitive models to give insight into the challenges of cybersecurity. The article gives a brief overview of work in different areas of cybersecurity where cognitive modeling research plays a role, with regard to direct interaction between end users and computer systems and with regard to the needs of security analysts working behind the scenes. The problem of distinguishing between human users and automated agents (bots) interacting with computer systems is introduced, as well as ongoing efforts toward building Human Subtlety Proofs, persistent and unobtrusive windows into human cognition with direct application to cybersecurity. Two computer games are described, proxies to illustrate different ways in which cognitive modeling can potentially contribute to the development of HSPs and similar cybersecurity applications.

Robert St. Amant, David L. Roberts.  2016.  Natural Interaction for Bot Detection. IEEE Internet Computing. July/August

Bot detection - identifying a software program that's using a computer system -- is an increasingly necessary security task. Existing solutions balance proof of human identity with unobtrusiveness in users' workflows. Cognitive modeling and natural interaction might provide stronger security and less intrusiveness.

Ignacio X. Dominguez, Jayant Dhawan, Robert St. Amant, David L. Roberts.  2016.  Exploring the effects of different text stimuli on typing behavior. Proceedings of the International Conference on Cognitive Modeling {(ICCM)}. :175–181.
Ignacio X. Dominguez, Jayant Dhawan, Robert St. Amant, David L. Roberts.  2016.  JIVUI: JavaScript Interface for Visualization of User Interaction. Proceedings of the International Conference on Cognitive Modeling (ICCM). :125–130.

In this paper we describe the JavaScript Interface for Visu- alization of User Interaction (JIVUI): a modular, Web-based, and customizable visualization tool that shows an animation of the trace of a user interaction with a graphical interface, or of predictions made by cognitive models of user interaction. Any combination of gaze, mouse, and keyboard data can be repro- duced within a user-provided interface. Although customiz- able, the tool includes a series of plug-ins to support common visualization tasks, including a timeline of input device events and perceptual and cognitive operators based on the Model Hu- man Processor and TYPIST. We talk about our use of this tool to support hypothesis generation, assumption validation, and to guide our modeling efforts. 

Ignacio X. Dominguez, Prairie Rose Goodwin, David L. Roberts, Robert St. Amant.  2016.  Human Subtlety Proofs: Using Computer Games to Model Cognitive Processes for Cybersecurity. International Journal of Human–Computer Interaction.

This article describes an emerging direction in the intersection between human-computer interaction and cognitive science: the use of cognitive models to give insight into the challenges of cybersecurity. The article gives a brief overview of work in different areas of cybersecurity where cognitive modeling research plays a role, with regard to direct interaction between end users and computer systems and with regard to the needs of security analysts working behind the scenes. The problem of distinguishing between human users and automated agents (bots) interacting with computer systems is introduced, as well as ongoing efforts toward building Human Subtlety Proofs, persistent and unobtrusive windows into human cognition with direct application to cybersecurity. Two computer games are described, proxies to illustrate different ways in which cognitive modeling can potentially contribute to the development of HSPs and similar cybersecurity applications.

Robert St. Amant, David L. Roberts.  2016.  Natural Interaction for Bot Detection. IEEE Internet Computing. 20(4):69–73.

Bot detection - identifying a software program that's using a computer system – is an increasingly necessary security task. Existing solutions balance proof of human identity with unobtrusiveness in users' workflows. Cognitive modeling and natural interaction might provide stronger security and less intrusiveness.

In Press
Ignacio X. Dominguez, Jayant Dhawan, Robert St. Amant, David L. Roberts.  In Press.  Exploring the Effects of Different Text Stimuli on Typing Behavior. International Conference on Cognitive Modeling.

In this work we explore how different cognitive processes af- fected typing patterns through a computer game we call The Typing Game. By manipulating the players’ familiarity with the words in our game through their similarity to dictionary words, and by allowing some players to replay rounds, we found that typing speed improves with familiarity with words, and also with practice, but that these are independent of the number of mistakes that are made when typing. We also found that users who had the opportunity to replay rounds exhibited different typing patterns even before replaying the rounds.