@article{gray08HFj,
title = {Cognitive architectures: Choreographing the dance of mental operations with the task environments},
author = { Wayne D. Gray},
year = {2008},
date = {2008-01-01},
journal = {Human Factors},
volume = {50},
number = {3},
pages = {497-505},
abstract = {Objective: In this paper, I present the ideas and trends that have given rise to the use of cognitive architectures in human factors, provide a cognitive-engineering-oriented taxonomy of these architectures, and a snapshot of their use for cognitive engineering. Background: Architectures of cognition have had a long history in human factors but a brief past. The long history entails a 50 yr preamble, whereas the explosion of work in the current decade reflects the brief past. Understanding this history is key to understanding the current and future prospects for applying cognitive science theory to human factors practice. Method: The review defines three formative eras in cognitive engineering research; the 1950s, 1980s, and now. Results: In the first era the fledging fields of Cognitive Science and Human Factors emphasized characteristics of the dancer, the limited capacity or bounded rationality view of the mind, and the ballroom, the task environment. The second era emphasized the dance; i.e., the dynamic interaction between mental operations and task environment. The third era has seen the rise of cognitive architectures as tools for choreographing the dance of mental operations within the complex environments posed by human factors practice. Conclusions: Hybrid architectures present the best vector for introducing Cognitive Science theories into a renewed engineering-based Human Factors. Application: The taxonomy provided in the paper may provide guidance on when and whether to apply a cognitive science or a hybrid architecture to a human factors issue.},
keywords = {bounded rationality, cognitive architectures, cognitive modeling, extended mind hypothesis, interactive routines, mental operations, task environment, unit task},
pubstate = {published},
tppubtype = {article}
}
Objective: In this paper, I present the ideas and trends that have given rise to the use of cognitive architectures in human factors, provide a cognitive-engineering-oriented taxonomy of these architectures, and a snapshot of their use for cognitive engineering. Background: Architectures of cognition have had a long history in human factors but a brief past. The long history entails a 50 yr preamble, whereas the explosion of work in the current decade reflects the brief past. Understanding this history is key to understanding the current and future prospects for applying cognitive science theory to human factors practice. Method: The review defines three formative eras in cognitive engineering research; the 1950s, 1980s, and now. Results: In the first era the fledging fields of Cognitive Science and Human Factors emphasized characteristics of the dancer, the limited capacity or bounded rationality view of the mind, and the ballroom, the task environment. The second era emphasized the dance; i.e., the dynamic interaction between mental operations and task environment. The third era has seen the rise of cognitive architectures as tools for choreographing the dance of mental operations within the complex environments posed by human factors practice. Conclusions: Hybrid architectures present the best vector for introducing Cognitive Science theories into a renewed engineering-based Human Factors. Application: The taxonomy provided in the paper may provide guidance on when and whether to apply a cognitive science or a hybrid architecture to a human factors issue.
@incollection{vdv07csc,
title = {Categorization and reinforcement learning: State identification in reinforcement learning and network reinforcement learning},
author = { Vladislav Daniel Veksler and Wayne D. Gray and Michael J. Schoelles},
editor = {McNamara, D. S. and Trafton, J. G.},
year = {2007},
date = {2007-01-01},
booktitle = {29th Annual Meeting of the Cognitive Science Society},
pages = {689-694},
publisher = {Cognitive Science Society},
address = {Austin, TX},
abstract = {We present Network Reinforcement Learning (NRL) as more efficient and robust than traditional reinforcement learning in complex environments. Combined with Configural Memory (Pearce, 1994), NRL can generalize from its experiences to novel stimuli, and learn how to deal with anomalies as well. We show how configural memory with NRL accounts for human and monkey data on a classic categorization paradigm. Finally, we argue for why the suggested mechanism is better than other reinforcement learning and categorization models for cognitive agents and AI.},
keywords = {artificial intelligence, categorization, category learning, cognitive architectures, cognitive modeling, configural, reinforcement learning, unsupervised learning},
pubstate = {published},
tppubtype = {incollection}
}
We present Network Reinforcement Learning (NRL) as more efficient and robust than traditional reinforcement learning in complex environments. Combined with Configural Memory (Pearce, 1994), NRL can generalize from its experiences to novel stimuli, and learn how to deal with anomalies as well. We show how configural memory with NRL accounts for human and monkey data on a classic categorization paradigm. Finally, we argue for why the suggested mechanism is better than other reinforcement learning and categorization models for cognitive agents and AI.
@article{duric02ieee,
title = {Integrating perceptual and cognitive modeling for adaptive and intelligent human-computer interaction},
author = { Z. Duric and Wayne D. Gray and R. Heishman and F. Li and A. Rosenfeld and Michael J. Schoelles and Christian D. Schunn and H. Wechsler},
year = {2002},
date = {2002-01-01},
journal = {Proceedings of the IEEE},
volume = {90},
number = {7},
pages = {1272--1289},
abstract = {This paper describes technology and tools for Intelligent HCI (IHCI) where human cognitive, perceptual, motor, and affective factors are modeled and used to adapt the H - C interface. Intelligent HCI emphasizes that human behavior encompasses both apparent human behavior and the hidden mental state behind behavioral performance. IHCI expands on the interpretation of human activities, known as W4 (what, where, when, who). While W4 addresses only the apparent perceptual aspect of human behavior, the W5+ technology for IHCI described in this paper addresses also the why and how questions whose solution requires recognizing and processing around specific cognitive states. IHCI integrates parsing and interpretation of nonverbal information with a computational cognitive model of the user, which, in turn, feeds into processes that adapt the interface to enhance operator performance and provide for rational decision-making. The technology proposed is based on a general four-stage, interactive framework, which moves from parsing the raw sensory-motor input, to interpreting the user's motions and emotions, to building an understanding of the user's current cognitive state. It then diagnoses various problems in the situation and adapts the interface appropriately. The interactive component of the system improves processing at each stage. Examples of perceptual, behavioral and cognitive tools are described throughout the paper. Adaptive and Intelligent HCI are important for novel applications of computing including ubiquitous and human-centered computing.},
keywords = {Adaptation, behavioral performance, cognitive modeling, decision-making, feedback, human--computer interaction (HCI), human-centered computing, intelligent interfaces, interpretation of human behavior, nonverbal information, perceptual modeling, ubiquitous computing.},
pubstate = {published},
tppubtype = {article}
}
This paper describes technology and tools for Intelligent HCI (IHCI) where human cognitive, perceptual, motor, and affective factors are modeled and used to adapt the H - C interface. Intelligent HCI emphasizes that human behavior encompasses both apparent human behavior and the hidden mental state behind behavioral performance. IHCI expands on the interpretation of human activities, known as W4 (what, where, when, who). While W4 addresses only the apparent perceptual aspect of human behavior, the W5+ technology for IHCI described in this paper addresses also the why and how questions whose solution requires recognizing and processing around specific cognitive states. IHCI integrates parsing and interpretation of nonverbal information with a computational cognitive model of the user, which, in turn, feeds into processes that adapt the interface to enhance operator performance and provide for rational decision-making. The technology proposed is based on a general four-stage, interactive framework, which moves from parsing the raw sensory-motor input, to interpreting the user's motions and emotions, to building an understanding of the user's current cognitive state. It then diagnoses various problems in the situation and adapts the interface appropriately. The interactive component of the system improves processing at each stage. Examples of perceptual, behavioral and cognitive tools are described throughout the paper. Adaptive and Intelligent HCI are important for novel applications of computing including ubiquitous and human-centered computing.
@book{erik01iccm.book,
title = {Fourth International Conference on Cognitive Modeling},
author = { Erik M. Altmann and Axel Cleeremans and Christian D. Schunn and Wayne D. Gray},
year = {2001},
date = {2001-01-01},
publisher = {Lawrence Erlbaum Associates},
address = {Mahwah, NJ},
abstract = {(from the preface) Presents conference papers from the 2001 International Conference on Cognitive Modeling which brought together researchers from diverse backgrounds to compare cognitive models, to evaluate models using human data, and to further the development, accumulation, and integration of cognitive theory.},
keywords = {cognitive modeling, cognitive theory},
pubstate = {published},
tppubtype = {book}
}
(from the preface) Presents conference papers from the 2001 International Conference on Cognitive Modeling which brought together researchers from diverse backgrounds to compare cognitive models, to evaluate models using human data, and to further the development, accumulation, and integration of cognitive theory.
@inproceedings{gray00chi.sig,
title = {The GOMS SIG: troubleshooting, lessons learned, novel applications, teaching techniques & future research},
author = { Wayne D. Gray and Bonnie E. John and David E. Kieras and Deborah A. Boehm Davis},
url = {http://doi.acm.org/10.1145/633292.633466},
doi = {10.1145/633292.633466},
isbn = {1-58113-248-4},
year = {2000},
date = {2000-01-01},
booktitle = {CHI '00 Extended Abstracts on Human Factors in Computing Systems},
pages = {297--297},
publisher = {ACM},
address = {The Hague, The Netherlands},
series = {CHI EA '00},
keywords = {ACT-R, cognitive modeling, cognitive task analysis, CPM-GOMS, EPIC, evaluation, GOMS, interaction design, NGOMSL, prototyping, soar, usability engineering},
pubstate = {published},
tppubtype = {inproceedings}
}
