Some futures for cognitive modeling and architectures: design patterns for including better interaction with the world, moderators, and improved model to data fits (and so can you)

Frank E. Ritter (), Farnaz Tehranchi (), Christopher L. Dancy () and Sue E. Kase ()
Additional contact information
Frank E. Ritter: Penn State/University Park
Farnaz Tehranchi: Penn State/University Park
Christopher L. Dancy: Bucknell University
Sue E. Kase: US Army Research Laboratory

Computational and Mathematical Organization Theory, 2020, vol. 26, issue 3, No 2, 278-306

Abstract: Abstract We note some future areas for work with cognitive models and agents that as Colbert (I am America (and so can you!), 2007) notes, “so can you”. We present three approaches as something like design patterns, so they can be applied to other architectures and tasks. These areas are: (a) Interacting directly with the screen-as-world. It is now possible for models to interact with uninstrumented interfaces both on the machine that the model is running on as well as remote machines. Improved interaction can not only support a broader range of behavior but also make the interaction more accurately model human behavior on tasks that include interaction. Just one implication is that this will force models to have more knowledge about interaction, an area that has been little modeled but essential for all tasks. (b) Providing the cognitive architecture with more representation of the body. In our example, we provide a physiological substrate to implement behavioral moderators’ effects on cognition. Cognitive architectures can now be broader in the measurements they predict and correspond to. This approach provides a more complete and theoretically appropriate way to include new aspects of behavior including stressor effects and emotions in models. And (c) using machine learning techniques, particularly genetic algorithms (GAs), to fit models to data. Because of the model complexity, this is equivalent to performing a multi-variable non-linear stochastic multiple-output regression. Doing this by hand is completely inadequate. While there is a danger of overfitting using a GA, these fits can help provide a better understanding of the model and architecture, including how the architecture changes under moderators such stress. This paper also includes some notes on model maintenance and reporting.

Keywords: Cognitive architecture; Vision models; Interaction; User models; Emotions; Moderators; Model fit; Model optimization; ACT-R; Genetic algorithm; Pattern matching (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10588-020-09308-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:comaot:v:26:y:2020:i:3:d:10.1007_s10588-020-09308-7

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10588

DOI: 10.1007/s10588-020-09308-7

Access Statistics for this article

Computational and Mathematical Organization Theory is currently edited by Terrill Frantz and Kathleen Carley

More articles in Computational and Mathematical Organization Theory from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().