@inproceedings{Taatgen_2006_Cognitive-Models,
        title = {How Cognitive Models can Inform the Design of Instructions},
        author = {Niels A. Taatgen and David Huss and John R. Anderson},
        pages = {304-309},
        series = {Proceedings of the seventh International Conference on cognitive modeling},
        year = 2006,
        abstract = {Instructions represented as lists of steps lead to inflexible and
brittle behavior in cognitive models, suggesting that list-style
instructions lead to poor learning in people as well. On the basis
of this assumption we designed an alternative operatorstyle
instruction that produces better learning in models. In an
experiment and model of interacting with a simulated Flight
Management System, a system that is notoriously hard to
learn on the basis of list-style instructions, we show that alternative
instructions produce significantly better and more robust
learning.
},
	biburl = {http://www.bibsonomy.org/bibtex/2cb8d6afb8976f785cedce6472e9db138/wnpxrz},
	keywords = {modeling cognitive model cognition}
}

@unpublished{Emond_2002_Cognitive-Modelling,
        title = {Cognitive Modeling and its Application for the Development of Socially Adept Technologies},
        author = {B. Emond and R.L. West},
        year = 2002,
        abstract = {In this paper we would like to propose that the
methodology of cognitive modeling can provide an
approach to the implementation of socially adept
technologies and the evaluation of its usability. We base
this approach on the assumption that representations play
an essential role in mediating social relations. Cognitive
representations and their causal link to the social and
physical environments have been recognized as an essential
element for understanding social relations. The challenge is
to port cognitive modeling methodology into the realm of
socially adept technologies. The first section briefly
presents the state of development of a modeling
environment aimed at supporting usability testing of sociotechnical
systems. The second section will give an
overview of an application intended to support social and
personal awareness in a web-based learning environment.
This application maps users identity, users behavior, and
shared information content into a model of human
memory.
},
	biburl = {http://www.bibsonomy.org/bibtex/2557704e0ea2c7c1ad42c1971d906da7d/wnpxrz},
	keywords = {modeling cognition model cognitive}
}

@inproceedings{nobile05cogito,
        title = {Cogito ergo Ago: foundations for a computational model of behaviour change},
        author = {Cosimo Nobile and Floriana Grasso},
        booktitle = {"Agents that Want and Like: Motivational and Emotional Roots of Cognition and Action", Symposium of the 2005 Convention of the Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB'05)},
        editor = {L. Canamero},
        month = {April},
        year = 2005,
        url = {http://www.csc.liv.ac.uk/~floriana/PIPS/papers/CogitoErgoAgo.pdf},
        id = {876299},
	priority = {4},
	description = {CiteULike: Cogito ergo Ago: foundations for a computational model of behaviour change},
	abstract = {So far AI researches in the health care promotion have considered strategies and techniques for making people aware of their health related problems and helping them to change their behaviour in order to have a better life style and be healthier. Very few researches though, to our knowledge, have focused on the deeper meanings behind a behaviour change. We argue that taking into account cognitive aspects, supported by solid psychological and philosophical theories, might help us to provide the right advice, to the right person, at the right time.},
	biburl = {http://www.bibsonomy.org/bibtex/2088d93507aa1bcc5cdb662778aa97576/wnpxrz},
	keywords = {cognition behavior modeling agent}
}

@article{chulef2001,
        title = {A Hierarchical Taxonomy of Human Goals},
        author = {Ada S. Chulef and Stephen J. Read and David A. Walsh},
        journal = {Motivation and Emotion},
        month = {#sep#},
        number = 3,
        pages = {191--232},
        volume = 25,
        year = 2001,
        url = {http://dx.doi.org/10.1023/A:1012225223418},
        description = {an attempt from a psychology perspective to organize human intentions},
	abstract = {This paper presents a hierarchical taxonomy of human goals, based on similarity judgments of 135 goals gleaned from the literature. Women and men in 3 age groups—17–30, 25–62, and 65 and older—sorted the goals into conceptually similar groups. These were cluster analyzed and a taxonomy of 30 goal clusters was developed for each age group separately and for the total sample. The clusters were conceptually meaningful and consistent across the 3 samples. The broadest distinction in each sample was between interpersonal or social goals and intrapersonal or individual goals, with interpersonal goals divided into family-related and more general social goals. Further, the 30 clusters were organized into meaningful higher order clusters. The role of such a taxonomy in promoting theory development and research is discussed, as is its relationship to other organizations of human goals and to the Big Five structure of personality.
ER  -},
	biburl = {http://www.bibsonomy.org/bibtex/2f029faed2025b2c7ad36da3f7717e4dc/wnpxrz},
	keywords = {cognition}
}

@book{pirolli2007,
        title = {Information Foraging Theory: Adaptive Interaction with Information},
        author = {P. Pirolli},
        publisher = {Oxford University Press},
        year = 2007,
        url = {http://www.amazon.de/Information-Foraging-Theory-Interaction-Human-Technology/dp/0195173325/ref=sr_1_1/028-8817308-0562117?ie=UTF8&s=books-intl-de&qid=1191250143&sr=8-1},
        typesource = {Simple CitationSource},
	source = {},
	asin = {0195173325},
	pubmed = {},
	doi = {},
	biburl = {http://www.bibsonomy.org/bibtex/22e0766c31babc118507fd4798ebe9c6b/wnpxrz},
	keywords = {cognition}
}

@article{pirolli1999,
        title = {Information foraging},
        author = {P. Pirolli and S. Card},
        journal = {Psychological Review},
        number = 4,
        pages = {643--675},
        volume = 106,
        year = 1999,
        biburl = {http://www.bibsonomy.org/bibtex/214cd4709fbf0144a5eefa5ab73f4e76b/wnpxrz},
	keywords = {cognition}
}

@article{tics2006,
        title = {Probabilistic models of cognition: Conceptual foundations},
        author = {Nick Chater and Joshua B. Tenenbaum and Alan Yuille},
        booktitle = {Special issue: Probabilistic models of cognition},
        journal = {Trends in Cognitive Sciences},
        month = {#jul#},
        number = 7,
        pages = {287--291},
        volume = 10,
        year = 2006,
        url = {http://www.sciencedirect.com/science/article/B6VH9-4K8SC6G-2/2/a664cd85339f8b0642a9c43707701c40},
        description = {ScienceDirect - Trends in Cognitive Sciences : Probabilistic models of cognition: Conceptual foundations},
	abstract = {Remarkable progress in the mathematics and computer science of probability has led to a revolution in the scope of probabilistic models. In particular, `sophisticated' probabilistic methods apply to structured relational systems such as graphs and grammars, of immediate relevance to the cognitive sciences. This Special Issue outlines progress in this rapidly developing field, which provides a potentially unifying perspective across a wide range of domains and levels of explanation. Here, we introduce the historical and conceptual foundations of the approach, explore how the approach relates to studies of explicit probabilistic reasoning, and give a brief overview of the field as it stands today.},
	biburl = {http://www.bibsonomy.org/bibtex/29a65e96bef91753b705fec462e9e60d1/wnpxrz},
	keywords = {probability probabilistic cognition}
}